Communications of the ACM
sing Neural Networks to Classify Music
New work from students at the University of Hong Kong describes a novel use of neural networks, collections of artificial neurons or nodes that can be trained to accomplish a wide variety of tasks, previously used only in image recognition.
The students used a convolutional network to "learn" features, such as tempo and harmony, from a database of songs that spread across 10 genres. The result was a set of trained neural networks that could correctly identify the genre of a song, which in computer science is considered a very hard problem, with greater than 87 percent accuracy. In March the group won an award for best paper at the International Multiconference of Engineers and Computer Scientists.
 Convolutional neural network to extract musical patterns in MFCC Credit: Technology Review |
What made this feat possible was the depth of the student's convolutional neural network. Conventional "kernel machine" neural networks are, as Yoshua Bengio of the University of Montreal has put it, shallow. These networks have too few layers of nodes—analogous to the layers of neurons in your cerebral cortex—to extract useful amounts of information from complex natural patterns.
From Technology Review
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