Tag: API

See yourself in Code

“Commissioned for the BBC’s make it digital event, the brief was ‘to get children into code’. My installation downloaded the event’s twitter feed in real time and displayed the page’s body text inside the bodies of passing people. Moving their hands around allowed people to scroll through the html/js/CSS.”

By Robin Price

http://robinprice.net/2015/03/do-you-see-yourself-in-code/

20150227-Robin-10-1024x683

 

This project uses:

  • maxurl
  • jit.gl.text2d
  • jit.openni
  • kinect

https://cycling74.com/project/do-you-see-yourself-in-code/

Uploading tracks for Echonest analysis

Get track analysis data for your music using the Echonest API.

The track analysis includes summary information about a track including tempo, key signature, time signature mode, danceability, loudness, liveness, speechinesss, acousticness and energy along with detailed information about the song structure (sections) beat structure (bars, beats tatums) and detailed info about timbre, pitch and loudness envelope (segment).

track API documentation: http://developer.echonest.com/docs/v4/track.html

Its a two (or three) step process. Here’s an example of how to upload your track and get an audio summary, using curl from the command line in Mac OS. Note, you will need to register with Echonest to get a developer API key here: http://developer.echonest.com/raw_tutorials/register.html

upload

Note that the path to the filename needs to be complete or relative to the working directory. Also, in this example there was no metadata identifying the title of the song. You may want to change this before uploading. Replace the API key with your key.

curl -F “api_key=TV2C30KWEJDKVIT9P” -F “filetype=mp3” -F “track=@/Users/tkzic/internetsensors/echo-nest/bowlingnight.mp3” “http://developer.echonest.com/api/v4/track/upload”

Here is the response returned:

{“response”: {“status”: {“version”: “4.2”, “code”: 0, “message”: “Success”}, “track”: {“status”: “pending”, “artist”: “Tom Zicarelli”, “title”: “”, “release”: “”, “audio_md5”: “7edc05a505c4aa4b8ff87ba40b8d7624”, “bitrate”: 128, “id”: “TRLFXWY14ACC02F24C”, “samplerate”: 44100, “md5”: “78ccac72a2b6c1aed1c8e059983ce7c7”}}}

track profile

Here’s the query to get the analysis – using the ID returned by the previous call.  Replace the API key with your key.

curl “http://developer.echonest.com/api/v4/track/profile?api_key=TV2C30KYGHTUVIT9P&format=json&id=TRLFXWY14ACC02F24C&bucket=audio_summary”

Here is the response – which also contains a URL that you can use to get more detailed segment based acoustic analysis of the track.

{

“response”: { “status”: { “code”: 0, “message”: “Success”, “version”: “4.2” }, “track”: { “analyzer_version”: “3.2.2”, “artist”: “Tom Zicarelli”, “audio_md5”: “7edc05a505c4aa4b8ff87ba40b8d7624”, “audio_summary”: { “acousticness”: 0.64550727753299, “analysis_url”: “http://echonest-analysis.s3.amazonaws.com/TR/TRLFXWY14ACC02F24C/3/full.json?AWSAccessKeyId=AKIAJRDFEY23UEVW42BQ&Expires=1420763215&Signature=OLqYwvuzVmAqp1xLTi5x4CsYJuE%3D”, “danceability”: 0.5680872294350238, “duration”: 245.91673, “energy”: 0.19974462311717034, “instrumentalness”: 0.8089125726216321, “key”: 11, “liveness”: 0.10906007889455183, “loudness”: -25.331, “mode”: 1, “speechiness”: 0.03294587631927559, “tempo”: 93.689, “time_signature”: 4, “valence”: 0.43565861274829504 }, “bitrate”: 128, “id”: “TRLFXWY14ACC02F24C”, “md5”: “78ccac72a2b6c1aed1c8e059983ce7c7”, “samplerate”: 44100, “status”: “complete” } } }

analysis

Use the analysis_url returned by the previous request. Note that it expires a few minutes after the request. But you can always re-run the audio_profile request to get a new analysis_url

curl “http://echonest-analysis.s3.amazonaws.com/TR/TRLFXWY14ACC02F24C/3/full.json?AWSAccessKeyId=AKIASVIFEY23UEGE42BQ&Expires=1420763215&Signature=OLqYwvuzVmAqp1xLTi5x4CsYJuE%3D”

The analysis result is too large to display here. For more information, get the Echonest Analyze Documentation: http://developer.echonest.com/docs/v4/_static/AnalyzeDocumentation.pdf

 

New musical instruments

A presentation for Berklee BTOT 2015 http://www.berklee.edu/faculty 

monk-thelonious-4fc61815c29ec

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?

  • new composition tools,
  • reactive music,
  • connecting things,
  • sensors,
  • voices, 
  • brains

Notes:

predictions?

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

Patterns

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.

New pianos

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):

https://www.youtube.com/watch?v=bry_62fVB1E

Experiments

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.

Analysis, re-synthesis, transformation

Computers can analyze the audio spectrum in real time. Sounds can be transformed and re-synthesized with near zero latency.

Infinite Jukebox

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.

http://labs.echonest.com/Uploader/index.html

Try examples: “Karma Police”, Or search for: “Albert Ayler”)

Remixing a remix

“Mindblowing Six Song Country Mashup”: https://www.youtube.com/watch?v=FY8SwIvxj8o (start at 0:40)

Screen Shot 2015-01-09 at 11.25.13 PM

Local file: Max teaching examples/new-country-mashup.mp3

More about Echonest

Feature detection

Looking at music under a microscope.

removing music from speech

First you have to separate them.

SMS-tools

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).

Screen Shot 2015-01-06 at 1.40.37 AM

Screen Shot 2015-01-06 at 1.40.12 AM

Settings for above example:

  • Window size: 1800 (SR / f0 * lobeWidth) 44100 / 200 * 8 = 1764
  • FFT size: 2048
  • Mag threshold: -90
  • Max harmonics: 30
  • f0 min: 150
  • f0 max: 200
Many kinds of features
  • Low level features: harmonicity, amplitude, fundamental frequency
  • high level features: mood, genre, danceability
Examples of feature detection
Music information retrieval

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

Polyphonic audio editing

Blurring the distinction between recorded and written music.

Melodyne

by Celemony

http://www.celemony.com/en/start

A minor version of “Bohemian Rhapsody”: http://www.youtube.com/watch?v=voca1OyQdKk

Music recognition

“How Shazam Works” by Farhoud Manjoo at Slate: http://reactivemusic.net/?p=12712, “About 3 datapoints per second, per song.”

  • Music fingerprinting: https://musicbrainz.org/doc/Fingerprinting
  • Humans being computers. Mystery sounds. (Local file: Desktop/mystery sounds)
  • Is it more difficult to build a robot that plays or one that listens?

Sonographic sound processing

Transforming music through pictures.

by Tadej Droljc

 http://reactivemusic.net/?p=16887

(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 Audio

Web browser is the new black

Noteflight

by Joe Berkowitz 

http://www.noteflight.com/login

Plink

by Dinahmoe

http://labs.dinahmoe.com/plink/

Can you jam over the internet?

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”

More about Web Audio

Conversation with robots

Computers finding meaning

The Google speech API

http://reactivemusic.net/?p=9834

The Google speech API uses neural networks, statistics, and large quantities of data.

Microsoft: real-time translation

Reverse entropy

InstantDecomposer

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

  • local: InstantDecomposer version: tkzic/pdweekend2014/IDecTouch/IDecTouch.pd
  • local: slicejockey2test2/slicejockey2test2.pd
More about reactive music

Sensors and sonification

Transforming motion into music

Three approaches
  • earcons (email notification sound)
  • models (video game sounds)
  • parameter mapping (Geiger counter)
Leap Motion

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

Internet sensors project

Detecting motion from the Internet

http://reactivemusic.net/?p=5859

Twitter streaming example

http://reactivemusic.net/?p=5786

MBTA bus data

 Sonification of Mass Ave buses, from Harvard to Dudley

http://reactivemusic.net/?p=17524

Screen Shot 2014-11-11 at 3.26.16 PM

Stock market music

http://reactivemusic.net/?p=12029

More sonification projects
Vine API mashup

By Steve Hensley

Using Max/MSP/jitter

local file: tkzic/stevehensely/shensley_maxvine.maxpat

Audio sensing gloves for spacesuits

By Christopher Konopka at future, music, technology

http://futuremusictechnology.com

Computer Vision

Sensing motion with video using frame subtraction

by Adam Rokhsar

http://reactivemusic.net/?p=7005

local file: max-projects/frame-subtraction

The brain

Music is stored all across the brain.

Mouse brain wiring diagram

The Allen institute

http://reactivemusic.net/?p=17758 

“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/

OpenWorm project

A complete simulation of the nematode worm, in software, with a Lego body (320 neurons)

http://reactivemusic.net/?p=17744

AARON

Harold Cohen’s algorithmic painting machine

http://reactivemusic.net/?p=17778

Brain plasticity

A perfect pitch pill? http://www.theverge.com/2014/1/6/5279182/valproate-may-give-humans-perfect-pitch-by-resetting-critical-periods-in-brain

DNA

Could we grow music producing organisms? http://reactivemusic.net/?p=18018

 

Two possibilities

Rejecting technology?
bob-dylan-5WFW_o_tn
An optimistic future?

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:

https://www.youtube.com/watch?v=OWmMVmiGJD0

More areas to explore