After two days in the studio I worked through so many of the conceptual questions that have been bugging me for months. And opened up a stack of new ones.
Basically, I managed to hack my way around the twotone file structure and get my bridge samples into their system, playing as instruments in the data sonification tool.
Trumpets now play the Rama VIII Bridge in Bangkok, and the glockenspiel plays the Golden Gate. Problem is, all of these bridge sounds are already so complex, once you start mapping them to different notes in response to the shifts in data, it’s pure sonic chaos! If I had a system that played a sample and shifted the pitch as the data changes, that would be way more seamless. I am enjoying the ad hoc nature of this process though and the way it is forcing me to consider at a much deeper level, the relationship between the data and the sounds.
As imagined, the one to one parameter mapping of sound sample to dataset is not actually that interesting. In terms of compositional complexity – it gets repetitive very quickly. And, extremely dense sonically if I haven’t chosen the initial samples well.
Something one note, simple, not too much going on, without multiple beats or tones.
Eventually I will upload some of these composition samples, but for now am still navigating how much of this process to share and what to keep private for the eventual ‘outcome’. Although as we discussed in the Publishing as Practice workshop today, having ways to show your artistic process can be both liberating and engaging.
Liberating, because it frees you from the grip of perfectionism + as my dear friend Ernest always says: finished is better than perfect! Engaging because while it may pierce the bubble of mystery around your work, it can also make you more approachable. Since this is a project that relies heavily on collaboration, for me it makes sense to make the process as transparent as possible. This allows potential creative partners to dive into the various threads of creative process, and gives a quick overview for anyone interested in working together. It’s also a little alarming, as nothing is ‘finished’ and I don’t feel nearly ready to make it public. Yet here I am, writing for you – whoever you are, dear reader – to lay my artistic soul bare.
There was something else. Ah yes, the constraints of the twotone platform mean that I have to take a very ‘zen’ approach to the work. Like the Tibetan Monks I sawy in New York City back in 1989, drawing sand mandalas. So intricate and beautiful, painstaking work that they released into the river once it was finished. You can’t stay attached to the outcome if you keep working through the process, over and over again.
Also that there is no ONE definitive work that will come from this. Many variations will emerge. And I am starting to make peace with that as part of the creative process.
I think perhaps I had envisaged – or ensounded? – a massive, global, all the bridges playing together event. But honestly, that is only possible as a conceptual frame. If you take even the 29 sensors on the ONE bridge and try to make a piece out of them, the sonic chaos resulting is going to be almost unbearable to listen to. So I need to find ways to pin it back into a context or reason for listening, and connecting. That is, the bridges have to relate to each other in some way, and to my own practice and experience. Otherwise it becomes totally random. I am starting to find more interesting questions through this process. And dealing with technical issues that I hadn’t even considered – like the sheer volume of data generated by a bridge sensor. And the compatability or otherwise of the various types of data with each other and the systems I need to use for creating sound compositions.
As an example, I have figured out that the selected storm data from the Hardanger Bridge structural monitoring sensors is only available in mat format but the csv files I need are massive and broken down by hour, throughout the day. So I needed to find out exactly what time did this storm hit? Hurricane Nina seems like a good place to start. Around 2-8pm on a Saturday, 10th January 2015 – now I have attempted to open those csv files but their compression is not playing nice with my computer. It takes another level of engagement now to connect with the engineers and find out if they are interested in the sonification process, and how possible it is to switch formats.
I am charmed to discover that the accelerometers used are made by Canterbury Seismic Instruments, in Christchurch New Zealand, where my grandmother was born. Which makes complete sense, given the magnitute and frequency of earthquakes NZ needs to monitor. Cusp-3 Series Strong Motion Accelerographs.
That brings us up to date, and my decision now to try selecting more subtle bridge samples as a starting point, and find out how they sound using the two datasets I am already working with. Then I need to get my head around the generative composition tools and work on mapping out the structure of the piece for the Church of Our Lady.
Thanks to the generous structural monitoring engineers at NTNU, I have access to an incredible range of accelerometer data from the Hardanger Bridge. It only took one more specific search term, and is published under a creative commons (cc-by) license.
Now the fun really starts – downloading the csv files: LowWind, HighFreq; MaxLift, LowFreq, MaxPitch, HighFreq (which I misread as MaxPatch and thought OMG they have sonified it already! Although perhaps they have. I still need to write and make contact) MaxDrag, LowFreq… The monitoring sensors are in place since 2013, there is seven years of data. And the storms – Storm Nina, Storm Ole, Storm Roar, Storm Thor!
Wind and Acceleration Data from the Hardanger Bridge
The dataset consists of long-term wind and acceleration data collected from the Hardanger Bridge monitoring system. The data are collected through wind sensors (anemometers) and accelerometers that are installed on the bridge. The dataset includes both the raw data (in “.csv” format) and the organized data (with “.mat” extension based on hdf5 format). Downloadable zipped folders contain monthly data with different frequency resolutions, special events (storms, etc.) and the raw data. Details on the organization of the data can be found in the readme file and the data paper, both of which can be found in the dataset.
Fenerci, A., Kvåle, K. A., Petersen, Ø. W., Rönnquist, A., & Øiseth, O. A. (2020). Wind and Acceleration Data from the Hardanger Bridge. https://doi.org/10.21400/5NG8980S
Datasets and Weather
Ok I’m breaking this down now – the CSV files are by year and month eg. Raw 2015 1.
Storms happen in January, Storm Nina: 10th Jan 2015, Storm Thor: 29th Jan 2016.
So to focus on the storms, go for the first month. I can’t use their smaller already selected and edited mat files in the data sonification tool. Maybe it’s possible to conver mat to csv? (oh that question that opens up a whole new can of worms!)
And have just discovered that my idea works to replace the audio files in the twotone sampler with my own bridge sounds… except that I have to go through the meticulously and make each NOTE as a bridge sound, as they move up and down on the scale while playing the data. I think that’s enough for today. Back to the sensors.
For now I’m taking the full month raw csv files and parsing them by date. You gotta start somewhere – Storm Nina go!
Poetic Storm Nina Video Homage
MELLOM BAKKAR OG BERG Ivar Aasen
Mellom bakkar og berg ut med havet heve nordmannen fenge sin heim, der han sjølv heve tuftene grave og sett sjølv sine hus oppå dei.
Han såg ut på dei steinute strender; der var ingen som der hadde bygd. «Lat oss rydja og byggja oss grender, og så eiga me rudningen trygt»
Han såg ut på det bårute havet, der var ruskut å leggja utpå, men der leikade fisk nedi kavet, og den leiken, den ville han sjå.
Fram på vinteren stundom han tenkte: «Gjev eg var i eit varmare land!» Men når vårsol i bakkane blenkte, fekk han hug til si heimlege strand.
Og når liane grønkar som hagar, når det lavar av blomar på strå, og når netter er ljose som dagar, kan han ingen stad venare sjå.
Sud om havet han stundom laut skrida: Der var rikdom på benkjer og bord, men ikring såg han trelldomen kvida og så vende han atter mot nord.
Lat no andre om storleiken kivast, lat deim bragla med rikdom og høgd, mellom kaksar eg inkje kan trivast, mellom jamningar helst er eg nøgd.
Lyd Mellom bakkar og berg ut med havet
BETWEEN HILLS AND MOUNTAINS Ivar Aasen
Between hills and mountains out to sea raise the Norwegian get his home, where he himself raise the tufts dig and put their houses on top of them. He looked out on the rocky beaches; there was no one who had built there. “Let us clear and build our villages, and then own the rudder safely » He looked out on the stretcher sea, there was debris to lay on, but there were fish playing down the cave, and that toy, he wanted to see. Until the winter he sometimes thought: “I wish I were in a warmer country!” But when the spring sun on the slopes shone, he got the urge to say home-grown beach. And when liane greens like pastures, when it blooms of flowers on straw, and when nights are as bright as days, he can see no city venare. South of the sea he sometimes loudly slid: There was wealth on benches and tables, but around he saw the bondage quiver and then he turned north again. Let no others about the size kivast, let them brag with wealth and heights, between cookies I can not thrive, between jams preferably I am satisfied.
Sound: Between hills and mountains out to sea
Wind-induced response of long-span suspension bridges subjected to span-wise non-uniform winds: a case study
Master thesis – NTNU Norwegian University of Science and Technology. Department of Structural Engineering. [E.M. Forbord & H. Hjellvik]
The response has also been predicted using wind data from the Hardanger Bridge, and the predictions have been compared to the measured response. Uniform profiles of wind speed and turbulence have been given different values based on the measured data, more specifically the mean value of all sensors and the value from the midmost wind sensor. It is seen that the choice of value does not affect the accuracy of response predictions. No matter what values are chosen, the predictions are quite inaccurate in general. lntroducing a non-uniform profile of mean wind speed makes the predictions slightly better in some cases, but not noteworthy, and the accuracy is still relatively low. When also including the non-uniformity of turbulence in the response calculations, the predicted response is reduced and the accuracy worsened with respect to the measured response. Accounting for the non-uniformity of self-excited forces shows almost no effect on the predictions. It is concluded that non-uniform wind profiles do not improve the accuracy of predicted bridge response, and that other uncertainties in the calculation methods have larger impact on the predictions than whether the non-uniform profiles are included or not.
2.1 Random Vibration Theory
6.2 Influence of Non-Uniform Turbulence Standard Deviations
In this section, the influence of span-wise non-uniform turbulence standard deviations on the dynamic response will be presented. Three wind speed profiles have been analysed with different turbulence std profiles. The wind speed profiles used are the linear profile and the sinus profile shown in Figure 5.5a and 5.5d, in addition to a uniform wind speed profile. The three different turbulence std profiles shown in Figure 6.15 are studied. They all have the same integrated sum along the span to make them comparable. The two non-uniform turbulence std profiles chosen have the opposite shapes of the wind speed profiles used in this section, because this is often seen in the measurement data from the Hardanger Bridge. Both of these turbulence std profiles will be compared to uniform turbulence standard deviations, for all the three wind speed profiles. The horizontal turbulence std has a span wise mean value of 20% of the wind profile’s mean wind speed, and for the vertical component the corresponding value is 10%. The effect of turbulence std on the response is included in the calculations through the wind spectra, which have a quadratic dependancy on the turbulence std, as shown in Eg. (2.40). The span-wise variation of wind speed is also included in the formula. Therefore, to study the effect of the turbulence std profiles isolated, the response using a uniform wind speed profile and different turbulence std profiles has been calculated. In addition comes the linear and sinus wind profiles, to study if the same turbulence std profiles have different effect on these than on the uniform wind speed profile. The calculated response will only be presented for wind profiles with the mean wind speed of 10 mis, because the trends, the shape and differences of the response along the span are nearly the same for all mean wind speeds for the different wind speed profiles.
6.3 Influence of Non-Uniform Self Excited Forces
To study the influence of span-wise non-uniform self-excited forces on the dynamic response, several wind speed profiles have been numerically tested with both uniform and non-uniform self-excited forces. The non-uniform self-excited forces are caused by the non-uniform wind profile. The re sponse is predicted with uniform self-excited forces where the aerodynamic properties are dependent on the mean wind speed of the wind profile, and with non-uniform self-excited forces where the aero dynamic properties vary along the span with the wind speed. Toen the bridge response in both cases are compared. The wind profiles tested are presented in Figure 5.5. As in section 6.1, the standard de viations of turbulence components are span-wise uniform, such that the influence of the non-uniform self-excited forces are investigated separately. The horizontal and vertical turbulence standard devi ations have been set to 20 and 10%, respectively, of the horizontal mean wind speed.
The influence of the non-uniform turbulence standard deviation has connection to the shape of the wind speeds along the span. As discussed previously, the response shifts to where the wind speeds is largest. The same can be said about the turbulence std. It was seen that the wind is dominating and shifts the response more than the turbulence std, for this particular shapes and ratio between the mean wind speed and standard deviation of the turbulence components. The horizontal shift in the response due to the non-uniform turbulence std comes from the cross-spectral densities of the turbulence components which is high when two points with large turbulence std are considered.
The effect of including the non-uniform self-excited forces on the response increase with the mean wind speed of the wind profile. The difference between the response using non-uniform and uniform self-excited forces are largest for the highest mean wind speeds studied. The lateral response using non-uniform self-excited forces deviates less from the response using uniform self-excited forces compared to the vertical and torsional response. This is due to the aerodynamic derivatives which has been taken as zero. The reason for the large ratios in the vertical and torsional direction is the aerodynamic derivatives that reduce the total damping and stiffness of the structure as mentioned. For lower mean wind speeds, 10-20m/s, the difference is below 10% for all response components.
Data Sonification toolkit coming together! Today I’m learning about twotone and how to resucitate a dead web audio interface. The wonderful Øystein Fjeldbo comes by to help me navigate this brave data world, and talks me through some of the options I’m exploring to make a proof-of-concept. First up, based on a tutorial in the Brexification post from MCT (Music Commmunicationst Technology) Master’s student blog at NTNU turns out to be not that adapatble. It’s very handy that it comes embedded in Max for Live (Connection Kit) and I get the sense of how easy it could be to use.
I can change the API to another live data stream, but there is no simple way to swap their synthesised sounds for a sample player. It turns out to be a completely different process, applying paramater changes to a tone generated by the patch, rather than making changes to an audio sample. So, we take a look at the patch made by the students, who have adapted it to their own needs – but again, this is too specific and not quite what I want to do. Now I’m a little concerned that I will have to give the process over to a custom build, but when we look into the code for the mysteriously vanishing twotone app, it turns out this is something Øystein can help me rebuild.
Or so the twotone web audio app promises – sadly their domain is no longer active. Even though it was launched with great fanfare on the 5th March in 2019. Only two years on, and it’s already obsolete. “TwoTone is imagined and made by Datavized Technologies with support from Google News Initiative. We hope you like it.” Did Data Sonification fall out of fashion so fast? Luckily it’s open source, and thanks to GitHub, the code still exists. But I have no idea what to do with it, or where to start.
It’s such a beautiful and simple concept, a data sonification web-based audio tool. You simply upload your dataset and then can choose instrumentation, key, duration, etc. Once we have it running on my browser, I start working my way through the tutorials.
I really have no idea what any of this means, but I trust that Øystein knows!
Despite not being able to swap out my own sounds with the pre-made samples, it’s satisfying working through the options to make interesting sonification compositions.
I’m pretty happy to be able to add my own csv data sets – a couple of examples from my research. Golden Gate Bridge accelerometers recorded on mobile phone by Stephen Wylie, via kaggle https://www.kaggle.com/mrcity/golden-gate-accel-20180512This is one minute of data from the “Linear Accelerometer” of the Physics Toolbox Suite v1.8.6 for Android. The data was collected from a Pixel 2 phone on the east side of the Golden Gate Bridge at the midpoint between the two towers of the bridge at approximately 3:20 PM local time on May 12, 2018. CCO Public Domain.
and the composition looks like this…
adding sound later – the export is doing a glitchy thing where it only gives a minutes of the sound, not all 30 minutes… But it’s only a problem with this one, not my next attempt using pedestrian data from the Brooklyn Bridge.
Towards Manhattan / Towards Brooklyn. Weather summary, precipitation:
Now it gets fun to start playing with filters: defining the key, speed and instruments<
I’ve figured out a hack to get my own samples in there – just have to name them exactly as the existing sets are named. Going to work on that tomorrow.
There’s a range of durations, from 02 seconds to 14 seconds, and I think the actual notes can be sacrificed for a variety of sounds. This is where it starts to get really creative! And sounds like some wild free jazz. Now I need to do some more study in order to really get a sense of the possibilities of sonification. Here’s the lecture series by Thomas Hermann – one of the people who literally wrote the book on sonification.
SONIFICATION AND SOUND DESIGN, MCT 2019
MCT Data Sonification course taught by Thomas Hermann: techniques beyond parameter mapping for applications in data mining and bio feedback
MCT4046 Sonification and Sound Design Seminar Series – Spring 2019 Speaker: Thomas Hermann
Facilitator: Shreejay Shrestha Video production: MCT students 2018-2020 Video recording: Shreejay Shrestha, Guy Sion Audio recording: Jørgen Nygård Varpe A/V editing & processing: Sepehr Haghighi Design: Karolina Jawad Music: Sepehr Haghighi Technical support: Robin Støckert, Anders Tveit, Alexander Refsum Jensenius Administration: Maj Vester Larsen Consultant: Sigurd Saue Seminar Series Curation: Anna Xambó Recorded in NTNU Trondheim 2019
Norwegian composer Arne Nordheim, recommended by Øystein to listen for generative compositions and experimental music techniques.
Hardanger Bridge monitoring
Next step: contact NTNU engineers and ask for access to their data, pretty please?!
NTNU Case study: The Hardanger Bridge
The Hardanger Bridge is a 1380 meters long suspension bridge, located on the western coast of Norway. It is the longest suspension bridge in Norway, and the 10th longest in the world, which makes it an interesting case study. Our research in the field of suspension bridges requires knowledge from several engineering fields; such as aerodynamics, signal processing, finite element analysis and control theory.
A comprehensive measurement system is operating on the Hardanger Bridge to improve the current understanding of the dynamic behaviour of long-span suspension bridges and their interaction with wind. This includes sensors for measurement of both response and environmental excitation. The system is described in detail under Structural monitoring (Structural monitoring – The Hardanger Bridge).
The Hardanger Bridge, opened in 2013, is a 1380 m long suspension bridge crossing the Hardanger fjord in western Norway. The main span is 1310 m long, which makes it the longest suspension bridge in Norway and the 10th longest in the world. The two concrete bridge towers are 200 m high, made using the slip forming technique, and supports the steel bridge casing and the cables that are anchored in the mountain side.
The main objective with the monitoring project is to study the dynamic behaviour of the suspension bridge; especially the wind-induced vibrations. All data generated from the extensive measuring system is directly used in research related to the ferry-free costal highway E39 project; both PhD and master theses.
The monitoring system consists of the following sensors (illustrated in Figure 2):
20 triaxial accelerometers
System identification and modal analysis
Based on recordings established by the monitoring system, parameters characterizing the system behaviour; typically represented by natural frequencies, damping ratios, and mode shapes; are estimated (system identification). The results are a highly valuable asset for these applications:
Studying the dynamic behaviour of the bridge
Updating the numerical model, such that it better describes the real behaviour of the bridge
Verification and possible improvement of the current state-of-the-art methods used for numerical modelling
Load modelling and identification
Modelling of the wind-induced forces on suspension bridges is crucially important for accurate prediction of the dynamic response.
The modelling of the environmental wind loads hinges on the description of the spatial and temporal characteristics of the wind field. The wind data from the field measurements will be used to characterize the wind field. It is aimed to test the performance of load models and reveal the uncertainty involved in response prediction.
The models for motion-induced loads most commonly used in bridge aerodynamics are linear engineering approximations. It has been shown in several case studies that the models are working well when the response of the bridge is dominated by one vibration mode in each direction. Taking into account that the principle of superposition does not hold in fluid dynamics, it is unknown if the models will be able to predict reliable results for a more complex motion. Thus, there is a need to test the accuracy of the linear assumption introduced in the modelling of the self-excited forces. This challenging task does not only require development of new experimental setup but also identification techniques able to work with an arbitrary motion.
Postdocs and PhD candidates working with suspension bridges:
MuseNet – OpenAI Try MuseNet. We’re excited to see how musicians and non-musicians alike will use MuseNet to create new compositions! In simple mode (shown by default), you’ll hear random uncurated samples that we’ve pre-generated. Choose a composer or style, an optional start of a famous piece, and start generating.openai.com
Magenta Studio – Standalone Continue. Continue uses the predictive power of recurrent neural networks (RNN) to generate notes that are likely to follow your drum beat or melody. Give it an input file and it can extend it by up to 32 measures. This can be helpful for adding variation to a drum beat or creating new material for a melodic track.magenta.tensorflow.org
Making music with magenta.js Step 1: Making sounds with your browser. Everything in @magenta/music is centered around NoteSequences. This is an abstract representation of a series of notes, each with different pitches, instruments and strike velocities, much like MIDI. For example, this is a NoteSequence that represents “Twinkle Twinkle Little Star”. Try changing the pitches to see how the sound changes!hello-magenta.glitch.me
The Musician in the Machine In this article, we’ll look at how we did it. Along the way we’ll listen to some more samples we really loved. Of course, some samples came out great, while some didn’t work as well as we hoped, but overall the project worked beautifully.magenta.tensorflow.org
Getting Started – Magenta Getting started. Ready to play with Magenta? This page will help you get started making music and art with machine learning, and give you some resources if you want to explore on your own!magenta.tensorflow.org
Music Transformer: Generating Music with Long-Term Structure Update (9/16/19): Play with Music Transformer in an interactive colab! Generating long pieces of music is a challenging problem, as music contains structure at multiple timescales, from milisecond timings to motifs to phrases to repetition of entire sections.magenta.tensorflow.org
A simple OpenAI Jukebox tutorial for non-engineers II. Overview and limitations . OpenAI uses a supercomputer to train their models and maybe to generate the songs too, and well, unless you also have a supercomputer or at least a very sweet GPU setup, your creativity will be a bit limited.. When I started playing with Jukebox, I wanted to created 3-minute songs from scratch, which turned out to be more than Google Colab (even with the pro …robertbrucecarter.com
Show notebooks in Drive – Google ColaboratoryColab notebooks allow you to combine executable code and rich text in a single document, along with images, HTML, LaTeX and more. When you create your own Colab notebooks, they are stored in your Google Drive account. You can easily share your Colab notebooks with co-workers or friends, allowing them to comment on your notebooks or even edit them.colab.research.google.com
Show notebooks in Drive – Google Colaboratory Please note: this next upsampling step will take several hours. At the free tier, Google CoLab lets you run for 12 hours. As the upsampling is completed, samples will appear in the Files tab (you can access this at the left of the CoLab), under “samples” (or whatever hps.name is currently).colab.research.google.com
MIMIC CREATIVE AI
MIMIC is a web platform for the artistic exploration of musical machine learning and machine listening. We have designed this collaborative platform as an interactive online coding environment, engineered to bring new technologies in AI and signal processing to artists, composers, musicians and performers all over the world.
The MIMIC platform has a built-in audio engine, machine learning and machine listening tools that makes it easy for creative coders to get started using these techniques in their own artistic projects. The platform also includes various examples of how to integrate external machine learning systems for sound, music and art making. These examples can be forked and further developed by the users of the platform.
Over the next three years, we aim to integrate brand new and developing creative systems into this platform so that they can be more easily used by musicians and artists in the creation of entirely new music, sound, and media, enabling people to understand and apply new computational techniques such as Machine Learning in their own creative work.
MIMIC or “Musically Intelligent Machines Interacting Creatively” is a three year AHRC-funded project, run by teams at Goldsmiths College, Durham University and the University of Sussex.
Intelligent Instruments: a funded ERC project – Sonic Writing The European Research Council has awarded me an ERC Consolidator grant for the project Intelligent Instruments: Understanding 21st-Century AI Through Creative Music Technologies.The five-year, 2 million Euro research project will consist of a team of postdocs, doctoral researchers and an instrument designer from the fields of music, computer science and philosophy. http://www.sonicwriting.org
Mubert’s unique algorithm creates and streams electronic generative music in real time, based on the samples from our extensive database. Every day new samples are added to the stream to support endless and seamless flow of one-of-a-kind work music.
Mubert is an AI music solution for any business, platform & use case. Mubert delivers worldwide copyright-protected AI-generated music via API. Infinite customization, cost-efficiency & legal compliance can help businesses fix key music industry pain points. All music is royalty free & cleared for any cases both for personal & commercial usage.Pricing .01c per minute or $299 per month startups / $1,000 per month large business https://mubert.com/blog/ https://mubert.com/products/streaming/
To facilitate their ability to connect with audiences and make a positive global impact, Mubert is launching a new extension that allows users to play unlimited streams of AI-powered music in their shows without any risks of DMCA takedowns and other copyright issues.
Subscribe with Music for Live Streams to feel your background on YouTube, Twitch, Facebook & 30 other popular services with Chill, Ambient, Trance, and other high-quality music curated by Mubert. https://streamers.mubert.com/DMCA-safe music stations for live streams. Compatible with Youtube, Facebook, Twitch & other streaming services. Premium $4.99 month
How AI-generated music is changing the way hits are made Music-making AI software has advanced so far in the past few years that it’s no longer a frightening novelty; it’s a viable tool. For the second episode of The Future of Music, I went to LA to visit the offices of AI platform Amper Music and the home of Taryn Southern, a pop artist who is working with Amper and other AI platforms to co …www.theverge.com
How technology can democratize music From the printing press to the digital camera, innovation has often democratized the creative arts. In this forward-looking talk, music producer Drew Silverstein demos a new software that allows anyone to create professional-grade music without putting human musicians out of work.www.ted.com
Track licenses are available for purchase at a few different tiers based on the intended usage. All licenses (regardless of tier) are royalty-free, permit global distribution of content, and are valid in perpetuity.
Personal License — $29 (This tier is meant for your personal or educational project needs. The licensing does not cover ad spend or promotions. For example, a video made as a hobby.)
Enterprise Basic License — $74 (This tier is meant for internal or external professional projects and cannot be supported with an ad spend. For example, an internal training video that will be shared within your company only or public tutorial content for latest feature release.)
Branded Content License — $399 (This tier is meant for professional projects that will be posted on your own social channel or website and can be supported with an ad spend. For example, a YouTube video on your channel.)
Online Ad License — $1,199 (This tier is meant for professional projects that can be both used in ads and supported with an ad spend. For example, a video that will run as a YouTube pre-roll or Instagram ad.)
All Media/Multimedia — Request a quote (This tier includes a combination of the above plus additional licensing needs. Please contact us so that we can evaluate your use-case and provide a quote.)
What does Amper do?
Amper is an AI music company. We develop enterprise products to help people make music using our AI Composer technology. Today we offer two products—our music creation platform Score, and an API that allows companies to integrate our music composition capabilities into their own tools. What is Score?
Score is a tool for content creators to quickly make music to accompany videos, podcasts, games, and other types of content using our AI Composer. Score is designed to significantly reduce the time it takes to source music and adapt it to fit a particular project. Who is Score intended for?
Score was built for businesses who create a lot of content and are looking for ways to source high quality music more efficiently. Video editors, podcast producers, and video game designers can all benefit from Score’s capabilities. How is Score different from stock music sites?
Each track Score outputs is composed by our AI in real-time and is always custom to your project. Collaborating with Score allows you to tailor a broad variety of your track’s musical attributes, including length, structure, genre, mood, instrumentation, and tempo.
Additionally, all the sounds you hear in Score are samples of real instruments recorded at Amper’s Los Angeles studio. Unlike stock music, which is often made using widely available sample “packs”, Score’s sounds are proprietary. This makes Amper’s music truly unique.
Music Ally Is A Knowledge Company NEW! Learn. Music Ally has launched a brand new Learning Hub for the music industry, with more than 30 modules of certified video content at launch, combined with relevant supporting materials from the rest of Music Ally’s information and insight.musically.com
Structural Health Monitoring | HBMModular Solution for Efficient Structural Health Monitoring. All structures, whether bridges, wind energy plants, water, gas and oil pipelines, tunnels, oil rigs, pavements, rails, but also ships, planes, trains or others are subject to various internal and external factors which may cause wear or malfunction.This can happen, for example due to deterioration, an incorrect construction process …www.hbm.com
Modular Solution for Efficient Structural Health Monitoring
All structures, whether bridges, wind energy plants, water, gas and oil pipelines, tunnels, oil rigs, pavements, rails, but also ships, planes, trains or others are subject to various internal and external factors which may cause wear or malfunction. This can happen, for example due to deterioration, an incorrect construction process, lack of quality control or an extreme situation resulting from an accident or environmental load. To be able to observe these changes in the material and to react in a proper way before serious damage is caused, the implementation of a damage identification system is crucial. The monitoring of structural behavior can detect anomalies in time, thus enabling maintenance and repair actions to be implemented more efficiently, with a direct impact on the reduction of operating costs. Replacing schedule-driven maintenance with condition-based maintenance is the main goal of infrastructure monitoring providing the following benefits.
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HBK Norway Great Belt Suspension Bridge, Denmark
Supply of FBG accelerometers for prediction of bridge cable vibrations
Gaining Real Insight into a Structure’s Health
Civil engineering structures are withstanding an exponential increase of applied loads, impacts and environmental burdens. The assessment of the resulting structural behavior is becoming mandatory so that faults can be detected in the early stages and safety is guaranteed.
Visual inspections do not give enough information to extend the structure’s lifetime, but by monitoring the structural health, any anomalies can be detected in time. This will optimize maintenance and reduce operating costs.
Monitor your entire structure’s life-cycle – from its design, construction, and operation to its rehabilitation or end-of-service life using HBM turnkey solutions for:
Material testing and load assessment
Strain and temperature distribution
Convergences and vibration estimation
Displacements, deflections, and rotation measurements
Bridge design validation
Bridge load assessment
Bridge short-term monitoring during construction
Bridge long-term structural health monitoring
Mezcala Cable-Stayed Bridge, MexicoSupply of FBG accelerometers for structural monitoring systemTrans-Rhumel Cable-Stayed Bridge, Algeria
Supply of complete optical measurement system for SHM
The new continuing education course at , “Structural Health Monitoring (SHM)”, is very relevant for professionals who design large and complex bridges and buildings, or work with the detection of damage to various building structures. For example, bridges should be monitored at regular intervals to ensure the safety of the users and the environment. The bridges age and are often exposed to higher traffic, train and freight loads than they were originally designed for.
STRUCTURAL HEALTH MONITORING OF BRIDGES IN SWEDEN – RoctestFUNCTION AND RESULTS Using these new techniques in the field created a lot of problems, especially during the construction period. Serious malfunctions could jeopardise the function and quality of the system and were keenly reported in order to examineroctest.com
The collapse of the Polcevera bridge in Italy represents a serious event which seems to be a direct result of cumulated local damages due to the aggressive environment of the construction site. Recently, evidence of corrosion of both ordinary and post-tension steel reinforcements were detected, in addition to concrete carbonation. Such phenomena generally lead to an increase in the deformation of all the elements of the bridge structure, which start to increase in time, leading to a progressive deterioration of the overall system. As a consequence, a proper structural monitoring layout would provide an extremely useful tool, for a correct plan of maintenance for all the elements of the considered infrastructure. In this work, strategies for the definition of structural health monitoring systems for bridges are discussed, from both software and hardware points of view. More specifically, a Cloud computing interface is considered, to make recorded data available for further analyses and post-processing procedures. The presented definition of the monitoring architecture could lead to the proper maintenance of all the structural elements, preventing the unexpected collapse of the structure.
Structural Health Monitoring – Do you need to test the integrity of a structure over time?
The company was founded March 1, 1999 as a spin-off from Aalborg University in Denmark. Our patented software is today used e.g. by mechanical engineers for modal analysis of operating machinery and components, and by civil engineers for ambient vibration analysis of large structures like bridges and buildings.
Structural Vibration Solutions A/S is located at NOVI Science Park, which is one of northern Europe’s most respected science parks. The location plays an important role as NOVI Science Park secures the close relations between the company and the research from Aalborg University.
About – Structural Vibration Solutions The company was founded March 1, 1999 as a spin-off from Aalborg University in Denmark. Our patented software is today used e.g. by mechanical engineers for modal analysis of operating machinery and components, and by civil engineers for ambient vibration analysis of large structures like bridges and buildings.svibs.com
Structural Monitoring Solutions
Structural Health Monitoring Systems (SHM)
Most companies rest on their technology laurels. Not SMS, as we partner with the best universities, engineering firms, the most progressive DOTs and other proven manufacturing leaders in the field of asset management. As a bridge owner, you get answers to your problems, not data. By using fiber optics, you need fewer power drops, less installation labor, and maintenance. This technology is ruggedized so you’re not procuring a replacement system in several years. You can trust us as we have 30 years of equipment manufacturing, SHM project management, data analysis, and expertise allow owners access to all the luxuries of SHM, at a cost-effective price.
Cable Stays | Structural Health MonitoringLaser Focused Cable Stays are difficult to inspect in critical areas. Acoustic monitoring provides a 100% volumetric 24/7 inspection. Many DOTs have adopted monitoring and others are actively planning an installation.www.smsshm.com
HOME | SMS SHMStructural Health Monitoring (SHM) Most companies rest on their technology laurels. Not SMS, as we partner with the best universities, engineering firms, the most progressive DOTs and other proven manufacturing leaders in the field of asset management.www.smsshm.com
Genoa Bridge in Italy
For the Ponte Morandi bridge, Acoustic Monitoring would have given warning of the bridge collapse as the Associated Press stated that Italian engineers knew of problems with the Genoa Bridge since 1979.
Sensors for Structural Health Monitoring | FPrimeC Solutions Inc.With recent advancements in Sensor technology, Structural Health Monitoring (SHM) systems have been developed and implemented in various civil structures such as bridges, buildings, tunnels, power plants, and dams. Many advanced types of sensors, from wired to wireless sensors, have been developed to continuously monitor structural condition through real-time data collection.www.fprimec.com
MISTRAS Structural Monitoring
MISTRAS, STARTED OUT IN 1978 MANUFACTURING MONITORING SYSTEMS AND SENSORS…
MISTRAS offers a complete and fully integrated structural monitoring service from design to data analysis. Our structural and process engineers are able to assess customers technical needs and propose a range of monitoring options from a wide range of systems and sensors. Using the latest open source software integrated into MISTRAS systems we are able to collect, analyse, manage and present findings of monitoring accurately and concisely. This provides our customers valuable information to allow effective asset management. We work opening and honestly to provide reliable, accurate and best value. https://mistrasgroup.co.uk/bridges-structures-structural-monitoring/
Structural Monitoring of Bridges and Structures- Mistras Group MISTRAS offers a comprehensive range of monitoring, inspection and site services for bridges and structures in a wide variety of industries. We work with customers providing one-source solutions, for structures from their initial construction to management towards the latter part of service life.mistrasgroup.co.uk
MISTRAS offers a comprehensive range of monitoring, inspection and site services for bridges and structures in a wide variety of industries. We work with customers providing one-source solutions, for structures from their initial construction to management towards the latter part of service life. From basic inspection and traditional NDT, to cutting-edge advanced NDT and long-term structural health monitoring, we have a wide range of tools to utilise. Our unique mix of degree-educated and chartered civil & structural engineers, experienced bridge inspectors, NDT experts (PCN, ASNT Level 2 and 3) and specialists monitoring division combine into comprehensive team that provide the highest standard inspection solution that you need.
We provide accurate comprehensive information and knowledge about structural condition, material properties, defects, and integrity that assists effective asset management, whole life costing and safety. MISTRAS has extensive expertise in the assessment of materials including steel, concrete, cables, composites and damage such as corrosion, cracking, scour and wire break. In addition our pool of site operatives can offer skills and capability to you, such as rope access services, slinger/banksman, confined space workers, confined space rescue teams and managers, first aiders/medics, concrete repair, small scale civils, installation of site telecoms and site network
MISTRAS has extensive experience in the application of wire break monitoring to:
Cable stay bridges
Post tension beams, slabs and box girders
We provide a full design service, experienced installation teams, including IRATA, confined space, offshore certified. Once installed, we provide full remote system management, reporting, long-term support and maintenance. MISTRAS offer a completely open service and can demystify the technology and process. We can show clients our full design process, example wire breaks and our analysis procedures to provide full reassurance in the technology and service we provide.
I got the chance to walk across the Golden Gate Bridge in San Francisco, CA for the first time in 22 years on May 12, 2018. There have been a great many technological advancements since then, as now we are all walking around with powerful computers and sensors in our pockets. I decided it would be fun to measure the bridge and provide others the opportunity to analyze data as to its motion for a brief snippet of time.
This is one minute of data from the “Linear Accelerometer” of the Physics Toolbox Suite v1.8.6 for Android. The data was collected from a Pixel 2 phone on the east side of the Golden Gate Bridge at the midpoint between the two towers of the bridge at approximately 3:20 PM local time on May 12, 2018.
This dataset is hereby owned by the community under the terms of a very lenient license in the condition that I have published it shortly after recording it.
Golden Gate Bridge G-Force Data
G-Force Data collected by Android’s Physics Toolbox Suite on 12 May 2018
I got the chance to walk across the Golden Gate Bridge in San Francisco, CA for the first time in 22 years on May 12, 2018. There have been a great many technological advancements since then, as now we are all walking around with powerful computers and sensors in our pockets. I decided it would be fun to measure the bridge and provide others the opportunity to analyze data as to its motion for a brief snippet of time.
This is one minute of data from the “g-force Meter” of the Physics Toolbox Suite v1.8.6 for Android. The data was collected from a Pixel 2 phone on the east side of the Golden Gate Bridge at the midpoint between the two towers of the bridge at approximately 3:15 PM local time on May 12, 2018.
This dataset is hereby owned by the community under the terms of a very lenient license in the condition that I have published it shortly after recording it.
Maybe this will inspire people to install sensors on the bridge, and other bridges, to monitor for things such as traffic (such as to find an optimum speed limit), dangerous fatigue, or dangerous wind conditions. At the very least, one could use this in comparison with a baseline stable motion to see how the bridge shakes. One could study effects of vehicles traversing the bridge (not that there’s any visual data for when that happened relative to this dataset, but I do believe at least one big bus drove by my device during this recording). One could study if there are periodic vibrations, and if so, at what frequencies. This would be even more interesting if correlated with wind data and run compared to several different wind speeds.
New York City – East River Bicycle Crossings | Kaggle Context. The New York City Department of Transportation collects daily data about the number of bicycles going over bridges in New York City. This data is used to measure bike utilization as a part of transportation planning.www.kaggle.com
The New York City Department of Transportation collects daily data about the number of bicycles going over bridges in New York City. This data is used to measure bike utilization as a part of transportation planning. This dataset is a daily record of the number of bicycles crossing into or out of Manhattan via one of the East River bridges (that is, excluding Bronx thruways and the non-bikeable Hudson River tunnels) for a stretch of 9 months.
A count of the number of bicycles on each of the bridges in question is provided on a day-by-day basis, along with information on maximum and minimum temperature and precipitation.
This data is published in an Excel format by the City of New York (here). It has been processed into a CSV file for use on Kaggle.
In this dataset, how many bicycles cross into and out of Manhattan per day?
How strongly do weather conditions affect bike volumes?
Hourly Traffic on Metropolitan Transportation Authority (MTA) Bridges and Tunnels: Beginning 2010
Transportation View Data This dataset provides data showing the number of vehicles (including cars, buses, trucks and motorcycles) that pass through each of the bridges and tunnels operated by the MTA each hour of the day. The data is updated weekly.API https://data.ny.gov/resource/qzve-kjga.json
Pittsburgh’s Commitment to Transparency – tylertech As with most cities, Pittsburgh is swimming in data. And while having this large quantity of details, databases, and stats can be advantageous, it can also overwhelm, especially if the data isn’t readily accessible. Data might be stashed in binders, files, or siloed on an employee’s desktop. However, by implementing a modern approach to its data management and analysis systems …www.tylertech.com
New York State inspectors assess all of the bridges every two years including a bridge’s individual parts. Bridges are analyzed for their capacity to carry vehicular loads. Inspectors are required to evaluate, assign a condition score, and document the condition of up to 47 structural elements, including rating 25 components of each span of a bridge, in addition to general components common to all bridges. The NYSDOT condition rating scale ranges from 1 to 7, with 7 being in new condition and a rating of 5 or greater considered as good condition. Bridges that cannot safely carry heavy vehicles, such as some tractor trailers, are posted with weight limits. Based upon inspection and load capacity analysis, any bridge deemed unsafe gets closed.
This is a dataset hosted by the State of New York. The state has an open data platform found here and they update their information according the amount of data that is brought in. Explore New York State using Kaggle and all of the data sources available through the State of New York organization page!
Update Frequency: This dataset is updated annually.
This dataset is maintained using Socrata’s API and Kaggle’s API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.
ANZAC Bridge (Pyrmont/Rozelle, 1995) | StructuraeANZAC Bridge is a motorway bridge / freeway bridge, cable-stayed bridge with semi-fan system and prestressed concrete bridge that was built from 1992 until 1995. The project is located in Pyrmont and Rozelle, Municipality of Leichhardt, Sydney, New South Wales, Australia.structurae.net
ANZAC Bridge | Australia | Sixense Context. The ANZAC Bridge is an eight-lane cable-stayed bridge in the west of Sydney. The bridge is 32.2m wide and the main span is 345m long. The reinforced concrete pylons are 69m high and support the deck by two planes of stay cables.The bridge can carry a maximum of 180,000 cars per day and is a critical infrastructure of the global Sydney road network.www.sixense-group.com
Service provided: ANZAC bridge monitoring
Location: Sydney, Australia
Client: Transport for NSW, Freyssinet Australia Period of the service: 2011Sixense solutions used:
The Rion-Antirion bridge (Harilaos Trikoupis Bridge) links mainland Greece to Peloponnese at the west side of the Corinthe Gulf near Partas. It is 2.2 km long, supported by 4 diamond shaped pylons.
The environment in which the bridge was constructed combines a number of physical challenges and thus makes this project particularly complex: a strait of about 2,500m width, deep water (up to 65m) combined with deep soil strata of weak alluviums, possibility of strong seismic activity, tectonic movements and adverse high wind actions.
A Structural Health Monitoing (SHM) system was specially designed to survey the behaviour of the bridge subjected to this difficult environment.
Real-time and history
Installed in 2004, the monitoring sytem provides highly reliable and useful information about events occurring on the bridge and the response of the structure.
Real time alerts as well as a database of historical information allows the concessionnaire, Gefyra, to secure the daily service, optimise the periodic maintenance and ultimately asess and extend the design life of the structure.
In particular the SHM system provided:
alerts concerning exesssive stay cable vibration in 2006, resulting in an upgrade of the structure with cable dampers which reduced the amplitude of vibration by four
verification of the remaining fatigue design life of cable gussets using the historical database of cable loading
optimisation of data by using a smart algorithm which record high sampling data file during specific seismic events.
Structural Health Monitoring (or SHM) uses permanently installed sensors to generate continuous data. A specific software enables to display the data in smart graphics and generates alerts. It is used in conjunction with spot check inspections to enable relevant structural data analysis. Its aim is to maintain infrastructures, extend their working lives, and detect and forecast their faults.
We monitor the structure and its environment (usage, weather, etc.) simultaneously by integrating a varied range of measurements, with the majority of data being gathered automatically, including satellite measurements. The data is processed to provide relevant indicators that operators can reliably use to optimise the operation and maintenance of their structures.
Our experience allows us to conduct SHM under challenging conditions (in confined spaces, working at height, etc.) by offering durable instrumentation for a broad range of applications, including extreme environments, offshore structures and potentially explosive ATEX zones.
Structural Health Monitoring (SHM) | Sixense Structural Health Monitoring (or SHM) uses permanently installed sensors to generate continuous data. A specific software enables to display the data in smart graphics and generates alerts.www.sixense-group.com
Sixense Monitoring (Head office) Parc de l’Ile – 21 rue du Port 92022 Nanterre CEDEX Tel: +33 (0)1 41 44 85 00
Sixense Digital 280 av. Napoléon Bonaparte 92500 Rueil Malmaison Tel: +33 (0)1 47 76 42 62
Millennium Bridge | United Kingdom | Sixense 330m above the Thames. The London Millennium Bridge is a 330 m pedestrian bridge, spanning the River Thames between St. Paul’s Cathedral and the new Tate Gallery.www.sixense-group.com
Monitoring of cable-stayed and prestressed structures
Sixense has specialist expertise, tools and methods that have been specifically developed for cable-stayed and prestressed concrete structures:
Substantial knowledge of the issues around structural ageing
Robust and proven measurement solutions
Acquisition and visualisation software incorporating alerts, indicators and analyses
Acoustic monitoring for real-time detection of cable strand failures as a result of corrosion or fatigue
These solutions provide key management information for operators of simple or complex structures.
Why you should use our services:
A team of civil engineering and monitoring experts available throughout the full project life cycle The members of our team are specialists in Civil Engineering, Metrology, Electronics and Computer Science. This broad skill set gives us a clear understanding of client needs on which to develop and recommend appropriate solutions
25 years of experienceWe have a 25-year track record of assisting and supporting public- and private-sector contracting authorities from national governments and local authorities to industrial companies and concession holders in the management of their built heritage. We have more than 20,000 connected sensors worldwide installed on modest and major structures, including the Ile de Ré bridge in France, the Rion-Antirion bridge in Greece, the Russki bridge in Russia and the Bosphorus bridges in Turkey.
Our resources We can provide a complete turnkey project, from design right through to operation, including manufacturing, installation, software configuration and commissioning.
EverSense®: monitoring and analysis
A unique expertise in the instrumentation of stay-cabled and prestressed concrete structures
The power of the EverSense® solution lies in its ability to integrate and process any type of automated measurement to provide relevant indicators and dashboards to a range of different stakeholders.
Our systems are deployed worldwide to inspect and monitor structures such as bridges, offshore platforms, wind turbines and nuclear facilities.
EverSense® | SixenseThe power of the EverSense® solution lies in its ability to integrate and process any type of automated measurement to provide relevant indicators and dashboards to a range of different stakeholders. Our systems are deployed worldwide to inspect and monitor structures such as bridges, offshore platforms, wind turbines and nuclear facilities.www.sixense-group.com
Une expertise unique sur l’instrumentation des structures câblées et en béton précontraint
The competence of our teams gives us the ability to recommend solutions tailored to solving specific problems:
Corrosion and detection monitoring of reinforced and prestressed concrete structures
Fatigue risk monitoring
Early-stage detection of scouring
Risk of gantry structure collapse
The members of our team are specialists in Civil Engineering, Metrology, Electronics and Computer Science.
This broad skill set gives us a clear understanding of client needs on which to develop and recommend appropriate software solutions.
EverSense® comprises a series of modules, ranging from real-time data acquisition and alert management to data exploitation via a web server.
Sensors designed to meet your needs
Sixense offers a comprehensive range of structural, hydraulic, environmental and other measurement sensors to monitor:
cable-stayed and prestressed structures
structures at risk of corrosion
structures at risk of scouring
structures at risk of ageing by fatigue
As part of providing a comprehensive, efficient and effective monitoring service, we combine many different technologies, including: electrical, electromagnetic, fibre optic, vibrating string strain gauge, acoustic and ultrasonic sensors, radar and laser measurement solutions, fully equipped surveying solutions and InSAR satellite measurement.
Deployment: hardware and software supply and installation, with skills transfer-based technical support
Support: multi-year maintenance and measurement interpretation
Training provided in the use, maintenance and processing of data, as well as ongoing structure management
Acoustic monitoring is the only technology available with the ability to detect cable strand failures as a result of corrosion or fatigue.
Any loss of section as a result of a failed strand can be detected and located.
Why choose our SHM solutions?
Our solutions facilitate proactive decision-making so that you can:
View the status of your structures in real time
Predict and optimise maintenance needs
Ensure user safety
Extend the operating service life of your structures
You want to know more about our expertise? We provide you customized solutions.
Our experts in Engineering, Monitoring, Mapping and Platform solutions help you manage your infrastructures throughout their entire life cycle.Conformément au règlement nᵒ 2016/679, dit règlement général sur la protection des données personnelles, le candidat dispose d’un droit d’interrogation et d’accès aux données à caractère personnel le concernant, ainsi que d’un droit de rectification de ces données. Le candidat dispose également d’un droit d’opposition. Ces droits peuvent être exercés par courrier électronique ou postal, accompagné de la copie d’un titre d’identité signé, adressé à DPO Soletanche Freyssinet – 280 avenue Napoléon Bonaparte – 92500 Rueil Malmaison.
Anzac Bridge | NSW Environment, Energy and Science SHR Criteria a) [Historical significance] Anzac Bridge has historical significance as it is a contemporary solution to the problem of conveying road traffic over Johnstons Bay, which was part of an important transport route from Sydney to the north shore and Parramatta since the mid nineteenth century, known as the five bridges route.www.environment.nsw.gov.au
Lat: -33.86888888888889 Long: 151.18555555555557
The Anzac Bridge is a world standard bridge in scale, aesthetics and design features. The experience of crossing the bridge is cathedral-like, with its vaulted canopy of stay cables. The subtle sweep of the bridge’s cantilevered deck, which links into the arterial road network and is supported at either end by monumental reinforced concrete towers, forms a striking and integral part of the Sydney skyline. It has quickly become one of the iconic images of Sydney, particularly for those who have views of it, cross it to work by road or bike, or use its highly visible towers as an aid to urban navigation.
SHR Criteria f) [Rarity]
The Anzac Bridge is the largest cable stayed bridge in NSW, and indeed Australia (other examples of cable stayed bridges in NSW are mainly footbridges).
SHR Criteria g) [Representativeness]
The Anzac Bridge is a representative example of a reinforced concrete cable stayed bridge in the state. It is currently the longest such bridge in Australia. Other, earlier examples of cable-stayed bridges are the Westgate Bridge in Victoria, and the Batman Bridge in Tasmania.
Instrumentation plan for 56 nodes on main span of the Golden Gate Bridge
Fig. 6. Instrumentation plan for 56 nodes on main span of the Golden…Download scientific diagram | Instrumentation plan for 56 nodes on main span of the Golden Gate Bridge from publication: Design and Implementation of Scalable Wireless Sensor Network for Structural Monitoring | An integrated hardware and software system for a scalable wireless sensor network WSN is designed and developed for structural health monitoring. An accelerometer sensor node is designed, developed, and calibrated to meet the requirements for structural vibration monitoring… | Wireless Sensor Network, Monitoring and CE | ResearchGate, the professional network for scientists.www.researchgate.net
Structural Health Monitoring of the Golden Gate BridgeSukun Kim, Shamim Pakzad, David Culler, James Demmel, Gregory Fenves, Steven Glaser, and Martin Turon http://sukunkim.com/research/ggb/ A Wireless Sensor Network (WSN) for Structural Health Monitoring (SHM) is designed, implemented, deployed and tested on the 4200ft long main span and the south tower of the Golden Gate Bridge (GGB). Ambient structural vibrations are reliably measured at a low cost and without interfering with the operation of the bridge. Requirements that SHM imposes on WSN are identified and new solutions to meet these requirements are proposed and implemented. In the GGB deployment, 64 nodes are distributed over the main span and the tower, collecting ambient vibrations synchronously at 1kHz rate, with less than 10us jitter, and with an accuracy of 30uG. The sampled data is collected reliably over a 46-hop network, with a bandwidth of 441B/s at the 46th hop. The collected data agrees with theoretical models and previous studies of the bridge. The deployment is the largest WSN for SHM. * This work is supported by the National Science Foundation under Grant No. EIA-0122599 and by the Center for Information Technology Research in the Interest of Society (CITRIS).
14. (1 Main span, 2 side spans, 6 spans at the North Viaduct, 5 spans at the South Viaduct).
Mostly straight with a curve at the South Viaduct.
9151′ (2789.2m). Main bridge spans are 1125′ (342.9m), 4200′ (1280.2m), and 1125′ (342.9m). North viaduct spans are approx. 200′ (61.0m) with a 347′ (105.8m) anchorage housing. South viaduct spans range from 71′ (21.6m) to 320′ (97.5m).
Width of Deck
87.1′ (26.5m) to 90′ (27.4m).
1937 (several upgrades since 1937).
1995. 69 accelerometers and 4 relative displacement sensors on the bridge, and a free-field station on the south side of the bridge.
Main span and side spans: suspended steel truss spans supported by braced steel towers. North and South Viaducts are mainly steel truss spans, with a steel arch span at the South Viaduct, supported by steel towers.
Suspension spans: braced steel cellular shaft towers. 2 columns per tower. North and South Viaducts: braced steel towers and concrete pylons.
Reinforced concrete piers support the main span towers.
The bridge was instrumented under the agreement between the Golden Gate Bridge, Highway and Transportation District and DOC.
Crowdsensing Framework for Monitoring Bridge Vibrations Using Moving SmartphonesThis paper discusses new services that can be delivered to urban environments through big data generated by the public’s smartphones, enhancing the relationship between a city and its infrastructure.By Thomas J. maTarazzo, PaolosanTi, shamimn. Pakzad, krisToPher CarTer, CarloraTTi, BaBakmoaveni, Chrisosgood, andnigel JaCoB
This webpage contains detailed information regarding full-scale applications of wireless sensors which can serve as a resource to the research and practitioner community. Reference papers/reports detailing the various deployments, URL for the deployments, pictures, and other information can be found here.
Testing and validation processes are critical tasks in developing a new hardware platform based on a new technology. This paper describes a series of experiments to evaluate the performance of a newly developed MEMS-based wireless sensor node as part of a wireless sensor network (WSN). The sensor node consists of a sensor board with four accelerometers, a thermometer and filtering and digitization units, and a MICAz mote for control, local computation and communication. The experiments include calibration and linearity tests for all sensor channels on the sensor boards, dynamic range tests to evaluate their performance when subjected to varying excitation, noise characteristic tests to quantify the noise floor of the sensor board, and temperature tests to study the behavior of the sensors under changing temperature profiles. The paper also describes a large-scale deployment of the WSN on a long-span suspension bridge, which lasted over three months and continuously collected ambient vibration and temperature data on the bridge. Statistical modal properties of a bridge tower are presented and compared with similar estimates from a previous deployment of sensors on the bridge and finite element models.
Some very useful tutorials (including an ableton live project with all the settings – just need to upload bridge samples) and one slightly concerning automatic music generator which seems to be based on avoiding paying copyright to artists. So not down with that, but curious about what it does. Also the generative.fm station of endless compositions, quite soothing. Actually, we’re going to start with some random plugins and synths.
Nodal is generative software for composing music, interactive real-time improvisation, and a musical tool for experimentation and play. Nodal uses a new method for creating and exploring musical patterns, probably unlike anything you’ve used before. You can play sounds using Nodal’s built-in synthesiser or any MIDI compatible hardware or software instrument.
Nodal is based around the concept of a user-defined network. The network consists of nodes (musical events) and edges (connections between events). You interactively define the network, which is then automatically traversed by any number of virtual players. Players play their instruments according to the notes specified in each node. The time taken to travel from one node to another is based on the length of the edges that connect the nodes. Nodal allows you to create complex, changing sequences using just a few simple elements. Its unique visual representation allows you to edit and interact with the music generating system as the composition plays.
Nodal is compatible with DAW software such as Ableton Live, Logic Studio, Digital Performer and Garage Band. It can transmit and receive MIDI sync. You can edit a network using the mouse and keyboard, and optionally a MIDI keyboard. Nodal recognises and sends notes, sync, continuous controller and pitch bend information.
Nodal is developed at SensiLab, Monash University.
How to Create Amazing Generative Music using Ableton Live and Max Devices – Tutorial
Fantastic Devices and how to use them.. to create surprising generative jams with hardware or soft synths, inside Ableton Live. I can just listen to this for hours! And it can be your best solution if you need to create a lot of musical content in a short span of time. The inspiration for this Jam/Tutorial came when i started using the Modular Sequencer, by Sound Manufacture. I find it a smart and useful device, as it easily allows to do things that would require tons of other devices, and a lot of time to make them work in Ableton Live. Plus it has its own unique features, a lot of them! And it can also communicate with other Sound Manufacture devices, to considerably expand the range of options. Here you can find the devices shown in this video. Modular Sequencer: https://www.soundmanufacture.net/modu… Chord-o-mat: https://www.soundmanufacture.net/chor…
Here is the Template Project download link, you’ll find the project in the zip file contained in the Album download. Just set price as ZERO and download for free:
Okay, let’s get this out of the way – no one should describe this as an “AI DJ.” There is no autonomous machine intelligence acting as a DJ. On the contrary, the mushy digital mash-up textures on offer here are unique, fresh, and distinctively sound like something that came from Moisés. Part analysis, part generative artwork, part creative remix, OÍR is simultaneously the intentional work of an artist and a machine reflection of a wide variety of streamed DJ sets.
Technically speaking, says Moisés, “the system is a compendium of OpenAI’s Jukebox, trained from scratch, StyleGAN2 for visuals.” “The mixing and DJ ‘transitions’ are done with a MIR [Music InformatioN Retrieval] ‘automatic mixing’ Python script,” he says.
But it’s worthwhile also understanding his artistic intention:
OÍR stems from my ongoing research on AI, sound synthesis, and electronic music.
Since starting my adventure into Deep Learning systems for music a couple of years ago, I’ve asked myself if Deep Learning (AI) is a tool or a medium? Right now I’ve come to the conclusion that it can be both, and this is what exactly I’m trying to explore with this project.
When we talk about Deep Learning as medium, there are three particular processes engaged when working with generative systems: curation of the data, training and monitoring of the algorithm as it ‘learns,’ and generating new synthetic media. Rinse and repeat.
There are a couple of aspects that interest me from this process. Each time you train the AI algorithm, its weights and biases or what it has ‘learned’ change over time — depending on the data you are having it learn from. The algorithm generates patterns present in these vast amounts of images and music, as is the case of OÍR, and these can be changing as the ‘learning’ process continues.
So this quality of a constantly changing and morphing generative algorithm is exactly what I want to explore with OÍR, and what better way to do it than though electronic dance music and techno culture.
I chose a channel as the canvas for the first episode, or EPOCH, of OÍR with a selection from the archive from HÖR Berlin, because I feel this channel has done the amazing job of generating a collective culture, specifically within techno and electronic music. I wanted to explore which patterns are emerging from this culture – which patterns can be synthesized, both visual and sonic, from all these sets and different approximations of techno, over 1,400+ hours and counting.
My desire with this art project is not to automatize or replace DJ’s or electronic musicians in any way, but rather have OÍR be a sort of ‘live generative archive’, as I did before with my album 𝕺𝖐𝖆𝖈𝖍𝖎𝖍𝖚𝖆𝖑𝖎 in relation to the Mexican 3ball electronic music genre, of certain cultural moments in electronic music which are increasingly existing on big tech platforms and the internet. By the way, OÍR means “to listen” in Spanish.
Now You Can Generate Music From Scratch With OpenAI’s NN Model – Analytics India Magazine One of the popular AI research labs, OpenAI has been working tremendously in the domain of artificial intelligence, particularly on the grounds of neural networks, reinforcement learning, among others.Just a few days back, the AI lab introduced Microscope for AI enthusiasts who are interested in exploring how neural network work.. And now the audio team of OpenAI has introduced a new machine …analyticsindiamag.com
[2008.01370] Timbre latent space: exploration and creative aspects Recent studies show the ability of unsupervised models to learn invertible audio representations using Auto-Encoders. They enable high-quality sound synthesis but a limited control since the latent spaces do not disentangle timbre properties. The emergence of disentangled representations was studied in Variational Auto-Encoders (VAEs), and has been applied to audio. arxiv.org
GANSynth: Making music with GANs How does it work? GANSynth uses a Progressive GAN architecture to incrementally upsample with convolution from a single vector to the full sound. Similar to previous work we found it difficult to directly generate coherent waveforms because upsampling convolution struggles with phase alignment for highly periodic signals. Consider the figure below: The red-yellow curve is a periodic signal …magenta.tensorflow.org
The MAESTRO Dataset and Wave2Midi2Wave MAESTRO (MIDI and Audio Edited for Synchronous TRacks and Organization) is a dataset composed of over 172 hours of virtuosic piano performances captured with fine alignment (~3 ms) between note labels and audio waveforms. This new dataset enables us to train a suite of models capable of transcribing, composing, and synthesizing audio waveforms with coherent musical structure on timescales … magenta.tensorflow.org