Music Genre Classification Using Convolutional Neural Network2014.10.21.docx
نویسندگان
چکیده
Feature extraction is a crucial part of many MIR tasks. Many manual-selected features such as MFCC have been applied to music processing but they are not effective for music genre classification. In this work, we present an algorithm based on spectrogram and convolutional neural network (CNN). Compared with MFCC, the spectrogram contains more details of music components such as pitch, flux, etc. We use feature detector as filter to convolve spectrogram to get four feature maps, which can catch trends of spectrogram in both time and frequency scale. Then sub-sample layer is applied to reduce dimension and enhance resistance to translation in pitch and tempo. Finally the extracted high-level features are connected to a multi-layer perceptron (MLP) classifier. A classification accuracy of 72.4% is obtained on Tzanetakis dataset by uing the proposed features, which performs better than MFCC.
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