Web Audio step sequencer.
Web Audio step sequencer.
By Chris Woodford at Explain That Stuff, 2014
By Sami Lemmetty at University of Helsinki, 1999
Includes links to Speech synthesis demonstration CD http://research.spa.aalto.fi/publications/theses/lemmetty_mst/appa.html
For the Echonest API track profile response.
By Jason Sundram at Running With Data
Spectral slider plugin for Ableton Live
By Adam Rokhsar at Utami
The Sound Of Tubes, Tape & Transformers.
By Hugh Robjohns at Sound On Sound
A presentation for Berklee BTOT 2015 http://www.berklee.edu/faculty
Around the year 1700, several startup ventures developed prototypes of machines with thousands of moving parts. After 30 years of engineering, competition, and refinement, the result was a device remarkably similar to the modern piano.
What are the musical instruments of the future being designed right now?
Ray Kurzweil’s future predictions on a timeline: http://imgur.com/quKXllo (The Singularity will happen in 2045)
In 1965 researcher Herbert Simon said: “Machines will be capable, within twenty years, of doing any work a man can do”. Marvin Minsky added his own prediction: “Within a generation … the problem of creating ‘artificial intelligence’ will substantially be solved.” https://forums.opensuse.org/showthread.php/390217-Will-computers-or-machines-ever-become-self-aware-or-evolve/page2
Are there patterns in the ways that artists adapt technology?
For example, the Hammond organ borrowed ideas developed for radios. Recorded music is produced with computers that were originally as business machines.
Instead of looking forward to predict future music, lets look backwards to ask,”What technology needs to happen to make musical instruments possible?” The piano relies upon a single-escapement (1710) and later a double-escapement (1821). Real time pitch shifting depends on Fourier transforms (1822) and fast computers (~1980).
Artists often find new (unintended) uses for tools. Like the printing press.
The piano is still in development. In December 2014, Eren Başbuğ composed and performed music on the Roli Seaboard – a piano keyboard made of 3 dimensional sensing foam:
Here is Keith McMillen’s QuNexus keyboard (with Polyphonic aftertouch):
Here are tools that might lead to new ways of making music. They won’t replace old ways. Singing has outlasted every other kind of music.
These ideas represent a combination of engineering and art. Engineers need artists. Artists need engineers. Interesting things happen at the confluence of streams.
Computers can analyze the audio spectrum in real time. Sounds can be transformed and re-synthesized with near zero latency.
Finding alternate routes through a song.
by Paul Lamere at the Echonest
Echonest has compiled data on over 14 million songs. This is an example of machine learning and pattern matching applied to music.
Try examples: “Karma Police”, Or search for: “Albert Ayler”)
“Mindblowing Six Song Country Mashup”: https://www.youtube.com/watch?v=FY8SwIvxj8o (start at 0:40)
Local file: Max teaching examples/new-country-mashup.mp3
Looking at music under a microscope.
First you have to separate them.
by Xavier Serra and UPF
Harmonic Model Plus Residual (HPR) – Build a spectrogram using STFT, then identify where there is strong correlation to a tonal harmonic structure (music). This is the harmonic model of the sound. Subtract it from the original spectrogram to get the residual (noise).
Settings for above example:
Finding the drop
“Detetcting Drops in EDM” – by Karthik Yadati, Martha Larson, Cynthia C. S. Liem, Alan Hanjalic at Delft University of Technology (2014) http://reactivemusic.net/?p=17711
Blurring the distinction between recorded and written music.
A minor version of “Bohemian Rhapsody”: http://www.youtube.com/watch?v=voca1OyQdKk
“How Shazam Works” by Farhoud Manjoo at Slate: http://reactivemusic.net/?p=12712, “About 3 datapoints per second, per song.”
Transforming music through pictures.
by Tadej Droljc
(Example of 3d speech processing at 4:12)
local file: SSP-dissertation/4 – Max/MSP/Jitter Patch of PV With Spectrogram as a Spectral Data Storage and User Interface/basic_patch.maxpat
Try recording a short passage, then set bound mode to 4, and click autorotate
Spectral scanning in Ableton Live:
Web browser is the new black
by Joe Berkowitz
What is the speed of electricity? 70-80 ms is the best round trip latency (via fiber) from the U.S. east to west coast. If you were jamming over the internet with someone on the opposite coast it might be like being 100 ft away from them in a field. (sound travels 1100 feet/second in air).
Global communal experiences – Bill McKibben – 1990 “The Age of Missing Information”
Computers finding meaning
The Google speech API uses neural networks, statistics, and large quantities of data.
Making music from from sounds that are not music.
by Katja Vetter
. (InstantDecomposer is an update of SliceJockey2): http://www.katjaas.nl/slicejockey/slicejockey.html
Transforming motion into music
camera based hand sensor
“Muse” (Boulanger Labs) with Paul Bachelor, Christopher Konopka, Tom Shani, and Chelsea Southard: http://reactivemusic.net/?p=16187
Max/MSP piano example: Leapfinger: http://reactivemusic.net/?p=11727
local file: max-projects/leap-motion/leapfinger2.maxpat
Detecting motion from the Internet
MBTA bus data
Sonification of Mass Ave buses, from Harvard to Dudley
By Steve Hensley
local file: tkzic/stevehensely/shensley_maxvine.maxpat
By Christopher Konopka at future, music, technology
Sensing motion with video using frame subtraction
by Adam Rokhsar
local file: max-projects/frame-subtraction
Music is stored all across the brain.
The Allen institute
“Hacking the soul” by Christof Koch at the Allen institute
(An Explanation of the wiring diagram of the mouse brain – at 13:33) http://www.technologyreview.com/emtech/14/video/watch/christof-koch-hacking-the-soul/
A complete simulation of the nematode worm, in software, with a Lego body (320 neurons)
Harold Cohen’s algorithmic painting machine
Could we grow music producing organisms? http://reactivemusic.net/?p=18018
There is a quickening of discovery: internet collaboration, open source, linux, github, r-pi, Pd, SDR.
“Robots and AI will help us create more jobs for humans — if we want them. And one of those jobs for us will be to keep inventing new jobs for the AIs and robots to take from us. We think of a new job we want, we do it for a while, then we teach robots how to do it. Then we make up something else.”
“…We invented machines to take x-rays, then we invented x-ray diagnostic technicians which farmers 200 years ago would have not believed could be a job, and now we are giving those jobs to robot AIs.”
Kevin Kelly – January 7, 2015, reddit AMA http://www.reddit.com/r/Futurology/comments/2rohmk/i_am_kevin_kelly_radical_technooptimist_digital/
Will people be marrying robots in 2050? http://www.livescience.com/1951-forecast-sex-marriage-robots-2050.html
“What can you predict about the future of music” by Michael Gonchar at The New York Times http://reactivemusic.net/?p=17023
Jim Morrison predicts the future of music:
Using the python sms-tools library.
Here is a song made from the processed sounds:
This project was an assignment for the Coursera “Audio Signal Processing for Music Applications” course. https://www.coursera.org/course/audio
Sounds were recorded from a shortwave radio between 5-10MHz.
freesound.org links to the sounds:
The sound is an AM shortwave broadcast station from between 7-8 MHz. It is speech with atmospheric noise and a digitally modulated carrier at 440Hz in the background.
I tried various approaches to removing the speech and isolating the carrier. But ended up using the following parameters to remove noise and speech, but for most part leaving a 440hz digital mode signal with large gaps in it.
After more experimentation, the following changes resulted in a cool continuous tone with speechlike quality (but not intelligible) and the background noise is gone.
Here is the full list of parameters:
Here is a plot:
Here is the resulting sound of the sinusoidal part of the harmonic model:
The sound is continuous digital modulation (buzzing) from a shortwave radio between 7-8 MHz. The buzz is around 100Hz with atmospheric background noise.
Transformation using HPS (harmonic plus stochastic) model.
Not very impressive analysis, but the resynthesis had a very cool looking spectrogram due to some frequency shifting.
I realized that I had set f0min too high. Went back to using the HPR model without transformation to see if I could separate the tone. Here is the plot:
Here are the resulting sounds transformation (unused) and the sinusoidal/residual results that were used in the track.
A repeating pulse around from a shortwave radio between 7-8 MHz. The frequency of the pulse is around 1000Hz with a noise component.
Another noise filter – this was way more difficult due to high freq material.
Instead, I went with a downward pitch transform, using the HPS model transform. Here are the resulting sounds from the HPR filter (unused) and the HPS transform.
The sound contains typical amateur radio CW signals from the 40 Meter band, with several interfering signals (QRM) and atmospheric noise (QRN). Using the HPR model, I was able to completely isolate and re-synthesize the CW signal, removing all the noise and interfering signals.
Note that you can actually see the morse code letters “T, U, and W” on the spectrogram of model!
Here is the re-synthesized CW sound:
The WWV National Bureau of Standards “clock” station at 5MHz. A combination of pulses, tones, speech, and background noise.
I was trying to separate the voice from the rest of the tones and noise. After several hours and various approaches, I gave up. The signal may be too complex to separate using these models. There were some interesting plots with the HPR model
Finally decided to just isolate the 440 Hz. clock pulse from the rest of the signal:
Here is the resulting sound (note that the tone starts several seconds into the sample)
John Coltrane: You can learn something from everybody, no matter how good or bad they play, everybody has something to say.
Sal Khan: In the future people will take agency for their own education.
For artists, everything is a tool.