Music Genre Classification: Training an AI model
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929357307510784 |
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| author | Mogonediwa, Keoikantse |
| author_facet | Mogonediwa, Keoikantse |
| contents | Music genre classification is an area that utilizes machine learning models and techniques for the processing of audio signals, in which applications range from content recommendation systems to music recommendation systems. In this research I explore various machine learning algorithms for the purpose of music genre classification, using features extracted from audio signals.The systems are namely, a Multilayer Perceptron (built from scratch), a k-Nearest Neighbours (also built from scratch), a Convolutional Neural Network and lastly a Random Forest wide model. In order to process the audio signals, feature extraction methods such as Short-Time Fourier Transform, and the extraction of Mel Cepstral Coefficients (MFCCs), is performed. Through this extensive research, I aim to asses the robustness of machine learning models for genre classification, and to compare their results. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15096 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Music Genre Classification: Training an AI model Mogonediwa, Keoikantse Sound Machine Learning Audio and Speech Processing Music genre classification is an area that utilizes machine learning models and techniques for the processing of audio signals, in which applications range from content recommendation systems to music recommendation systems. In this research I explore various machine learning algorithms for the purpose of music genre classification, using features extracted from audio signals.The systems are namely, a Multilayer Perceptron (built from scratch), a k-Nearest Neighbours (also built from scratch), a Convolutional Neural Network and lastly a Random Forest wide model. In order to process the audio signals, feature extraction methods such as Short-Time Fourier Transform, and the extraction of Mel Cepstral Coefficients (MFCCs), is performed. Through this extensive research, I aim to asses the robustness of machine learning models for genre classification, and to compare their results. |
| title | Music Genre Classification: Training an AI model |
| topic | Sound Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2405.15096 |