Identifying Cover Songs Using Deep Neural Networks
نویسنده
چکیده
Feature Extraction Features are extracted using a two-layer stacked auto-encoder (SAE) with 1440 input neurons, 500 first hidden layer neurons and 100 2nd layer hidden neurons. Each CQT input patch consists 8 frames with 180 frequency bins or one measure of the song and each chroma feature input consists of 144 frequency bins and 10 frames with 50% overlap. The SAE is then trained on a set of concatenated spectral inputs from 80 original songs.
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