Music Artist Classification with WaveNet Classifier for Raw Waveform Audio Data
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Models for music artist classification usually were operated in the frequency domain, in which the input audio samples are processed by the spectral transformation. The WaveNet architecture, originally designed for speech and music generation. In this paper, we propose an end-to-end architecture in the time domain for this task. A WaveNet classifier was introduced which directly models the features from a raw audio waveform. The WaveNet takes the waveform as the input and several downsampling layers are subsequent to discriminate which artist the input belongs to. In addition, the proposed method is applied to singer identification. The model achieving the best performance obtains an average F1 score of 0.854 on benchmark dataset of Artist20, which is a significant improvement over the related works. In order to show the effectiveness of feature learning of the proposed method, the bottleneck layer of the model is visualized.
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