Neural networks built for image recognition are well-suited for "seeing" sound.
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.
