Ms-sincresnet: Joint Learning Of 1D And 2D Kernels Using Multi-scale Sincnet And Resnet For Music Genre Classification
2021 · Pei-Chun Chang, Yong-Sheng Chen, Chang-Hsing Lee
Abstract
In this study, we proposed a new end-to-end convolutional neural network, called MS-SincResNet, for music genre classification. MS-SincResNet appends 1D multi-scale SincNet (MS-SincNet) to 2D ResNet as the first convolutional layer in an attempt to jointly learn 1D kernels and 2D kernels during the training stage. 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 clip is fed into 1D MS-SincNet filter learning module to obtain three-channel 2D representations. The learned representations carry rich timbral, harmonic, and percussive characteristics comparing with spectrograms, harmonic spectrograms, percussive spectrograms and Mel-spectrograms. ResNet is then used to extract discriminative embeddings from these 2D representations. The spatial pyramid pooling (SPP) module is further used to enhance the feature discriminability, in terms of both time and frequency aspects, to obtain the classifi
Authors
(none)
Tags
Stats
Related papers
- A Novel Multimodal Music Genre Classifier Using Hierarchical Attention And Convolutional Neural Network (2020)0.00
- Convolution Channel Separation And Frequency Sub-bands Aggregation For Music Genre Classification (2022)0.00
- Genre-agnostic Key Classification With Convolutional Neural Networks (2018)0.00
- Multi-level And Multi-scale Feature Aggregation Using Pre-trained Convolutional Neural Networks For Music Auto-tagging (2017)15.43
- Music Genre Classification Using Spectral Analysis And Sparse Representation Of The Signals (2018)8.09
- D3net: Densely Connected Multidilated Densenet For Music Source Separation (2020)0.00
- Convolutional Recurrent Neural Networks For Music Classification (2016)18.98
- Explaining Deep Convolutional Neural Networks On Music Classification (2016)0.00