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Effective Music Genre Classification using Late Fusion Convolutional Neural Network with Multiple Spectral Features
- Authors
- Cho, Sung-Hyun; Park, Yechan; Lee, Jaesung
- Issue Date
- Oct-2022
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Convolutional Neural Network; Mel-Frequency Cepstral Coefficient; Mel-Spectrogram; Music Genre Classification; Music Information Retrieval; Short-Time Fourier Transform
- Citation
- 2022 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2022
- Journal Title
- 2022 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2022
- ISSN
- 0000-0000
- Abstract
- Music genre classification is getting more and more attention amid the growing content consumption for music. Music Information Retrieval researchers have proposed various structures based on Convolutional Neural Networks that mainly achieve state-of-the-art results in the music genre classification tasks. Using multiple musical features as model inputs can improve classification accuracy. Therefore, this study proposes a new Convolutional Neural Network model using three musical features for music genre classification: Short-Time Fourier Transform, Mel-Spectrogram, and Mel-Frequency Cepstral Coefficient. © 2022 IEEE.
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