For the Echonest API track profile response.
By Jason Sundram at Running With Data
http://runningwithdata.com/post/1321504427/danceability-and-energy
For the Echonest API track profile response.
By Jason Sundram at Running With Data
http://runningwithdata.com/post/1321504427/danceability-and-energy
Facial recognition software for detecting emotions
Emotient API: http://www.emotient.com/products#FACETSDK
A Soundcloud April fools prank becomes reality.
A research paper by Karthik Yadati, Martha Larson, Cynthia C. S. Liem, Alan Hanjalic at Delft University of Technology (2014)
http://www.terasoft.com.tw/conf/ismir2014/proceedings/T026_297_Paper.pdf
The Soundcloud prank (2013)
http://www.attackmagazine.com/news/soundcloud-when-april-fools-day-pranks-go-wrong/
Open source audio feature detection library in c++ with Python wrapper.
By Music Technology Group at Universitat Pompeu Fabra
a system to automatically detect laughter from acoustic features of audio using neural networks.
By Mary Knox at Berkeley
https://www.icsi.berkeley.edu/pubs/speech/laughter_v10.pdf
A system to automatically detect laughter events.
By Lyndon S. Kennedy and Daniel P.W. Ellis at Columbia University and ICSI
http://www.ee.columbia.edu/~lyndon/pubs/nistrt2004-laughter.pdf
Wifi analysis tools for 2.4 and 5 GHz networks.
by Passmark.
http://www.passmark.com/products/metageek-wi-spy-dbx.htm
By Farhad Manjoo at Slate
http://www.slate.com/articles/technology/technology/2009/10/that_tune_named.html
“An Industrial-Strength Audio Search Algorithm”
By Avery Wang, Shazam co-founder
http://www.ee.columbia.edu/~dpwe/papers/Wang03-shazam.pdf
“How does Shazam work to recognize a song?”
By Nicolae Surdu
http://www.soyoucode.com/2011/how-does-shazam-recognize-song
This project uses the Max fzero~ object to detect which key of the piano gets pressed and send a pre-written Tweet like “Signs point to yellow” based on the color of the key.
It works with the Internet sensors project that sends Tweets from Max using Ruby. https://reactivemusic.net/?p=7013
https://github.com/tkzic/internet-sensors
folder: twitter-ruby
fzero~ is probably not the best choice for this. It doesn’t work above 2500hz which means it won’t probably distinguish between the lowest and highest key which are an octave apart. In fact the Little-Tikes piano, for a pitched instrument, is difficult to analyze. Due to relatively equal weight of partials to fundamental, and the quick decay. Other choices, would be pitch~ (Jehan) fiddle~ (Pucket…)
I remember seeing an Arduino project where somebody did this in reverse – actually built a motorized striker to play the piano)
… insert link to video here…