Music Genre Classification: Ensemble Learning with Subcomponents-level Attention
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866916535529897984 |
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| author | Liu, Yichen Dasgupta, Abhijit He, Qiwei |
| author_facet | Liu, Yichen Dasgupta, Abhijit He, Qiwei |
| contents | Music Genre Classification is one of the most popular topics in the fields of Music Information Retrieval (MIR) and digital signal processing. Deep Learning has emerged as the top performer for classifying music genres among various methods. The letter introduces a novel approach by combining ensemble learning with attention to sub-components, aiming to enhance the accuracy of identifying music genres. The core innovation of our work is the proposal to classify the subcomponents of the music pieces separately, allowing our model to capture distinct characteristics from those sub components. By applying ensemble learning techniques to these individual classifications, we make the final classification decision on the genre of the music. The proposed method has superior advantages in terms of accuracy compared to the other state-of-the-art techniques trained and tested on the GTZAN dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_15602 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Music Genre Classification: Ensemble Learning with Subcomponents-level Attention Liu, Yichen Dasgupta, Abhijit He, Qiwei Sound Information Retrieval Machine Learning Multimedia Audio and Speech Processing Music Genre Classification is one of the most popular topics in the fields of Music Information Retrieval (MIR) and digital signal processing. Deep Learning has emerged as the top performer for classifying music genres among various methods. The letter introduces a novel approach by combining ensemble learning with attention to sub-components, aiming to enhance the accuracy of identifying music genres. The core innovation of our work is the proposal to classify the subcomponents of the music pieces separately, allowing our model to capture distinct characteristics from those sub components. By applying ensemble learning techniques to these individual classifications, we make the final classification decision on the genre of the music. The proposed method has superior advantages in terms of accuracy compared to the other state-of-the-art techniques trained and tested on the GTZAN dataset. |
| title | Music Genre Classification: Ensemble Learning with Subcomponents-level Attention |
| topic | Sound Information Retrieval Machine Learning Multimedia Audio and Speech Processing |
| url | https://arxiv.org/abs/2412.15602 |