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In this study, we proposed a new end-to-end convolutional neural network,called MS-SincResNet, for music genre classification. MS-SincResNet appends 1Dmulti-scale SincNet (MS-SincNet) to 2D ResNet as the first convolutional layerin an attempt to jointly learn 1D kernels and 2D kernels during the trainingstage. First, an input music signal is divided into a number of fixed-duration(3 seconds in this study) music clips, and the raw waveform of each music clipis fed into 1D MS-SincNet filter learning module to obtain three-channel 2Drepresentations. The learned representations carry rich timbral, harmonic, andpercussive characteristics comparing with spectrograms, harmonic spectrograms,percussive spectrograms and Mel-spectrograms. ResNet is then used to extractdiscriminative embeddings from these 2D representations. The spatial pyramidpooling (SPP) module is further used to enhance the feature discriminability,in terms of both time and frequency aspects, to obtain the classification labelof each music clip. Finally, the voting strategy is applied to summarize theclassification results from all 3-second music clips. In our experimentalresults, we demonstrate that the proposed MS-SincResNet outperforms thebaseline SincNet and many well-known hand-crafted features. Consideringindividual 2D representation, MS-SincResNet also yields competitive resultswith the state-of-the-art methods on the GTZAN dataset and the ISMIR2004dataset. The code is available at https://github.com/PeiChunChang/MS-SincResNet

MS-SincResNet: Joint learning of 1D and 2D kernels using multi-scale SincNet and ResNet for music genre classification