Using Neural Networks to Classify Music
Technology Review (06/03/10) Mims, Christopher A neural network built for image recognition is now able to classify music. University of Hong Kong students trained a conventional "kernel machine" neural network to recognize characteristics such as tempo and harmony from a database of songs from 10 genres, but discovered that the optimal number of layers of nodes needed to identify the musical genre was three. The adapted convolutional network was able to correctly and quickly identify a song with greater than 87 percent accuracy. Although the convoluted neural network was not able to identify songs outside of its training library, the team believes its ability to recognize 240 songs within two hours suggests that it is scalable. Cats, which have unique visual cortexes, served as the inspiration for the project. The Hong Kong project is the latest convoluted neural network based on a mammal to show a high level of flexibility. The results raise the question as to why such neural networks have not been used to address other problems involving perception in artificial intelligence.
Technology Review (06/03/10) Mims, Christopher A neural network built for image recognition is now able to classify music. University of Hong Kong students trained a conventional "kernel machine" neural network to recognize characteristics such as tempo and harmony from a database of songs from 10 genres, but discovered that the optimal number of layers of nodes needed to identify the musical genre was three. The adapted convolutional network was able to correctly and quickly identify a song with greater than 87 percent accuracy. Although the convoluted neural network was not able to identify songs outside of its training library, the team believes its ability to recognize 240 songs within two hours suggests that it is scalable. Cats, which have unique visual cortexes, served as the inspiration for the project. The Hong Kong project is the latest convoluted neural network based on a mammal to show a high level of flexibility. The results raise the question as to why such neural networks have not been used to address other problems involving perception in artificial intelligence.
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