Raw Audio Classification with Cosine Convolutional Neural Network (CosCovNN)
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909410318614528 |
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| author | Haque, Kazi Nazmul Rana, Rajib Jarin, Tasnim Schuller Jr, Bjorn W. |
| author_facet | Haque, Kazi Nazmul Rana, Rajib Jarin, Tasnim Schuller Jr, Bjorn W. |
| contents | This study explores the field of audio classification from raw waveform using Convolutional Neural Networks (CNNs), a method that eliminates the need for extracting specialised features in the pre-processing step. Unlike recent trends in literature, which often focuses on designing frontends or filters for only the initial layers of CNNs, our research introduces the Cosine Convolutional Neural Network (CosCovNN) replacing the traditional CNN filters with Cosine filters. The CosCovNN surpasses the accuracy of the equivalent CNN architectures with approximately $77\%$ less parameters. Our research further progresses with the development of an augmented CosCovNN named Vector Quantised Cosine Convolutional Neural Network with Memory (VQCCM), incorporating a memory and vector quantisation layer VQCCM achieves state-of-the-art (SOTA) performance across five different datasets in comparison with existing literature. Our findings show that cosine filters can greatly improve the efficiency and accuracy of CNNs in raw audio classification. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00312 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Raw Audio Classification with Cosine Convolutional Neural Network (CosCovNN) Haque, Kazi Nazmul Rana, Rajib Jarin, Tasnim Schuller Jr, Bjorn W. Sound Artificial Intelligence Audio and Speech Processing This study explores the field of audio classification from raw waveform using Convolutional Neural Networks (CNNs), a method that eliminates the need for extracting specialised features in the pre-processing step. Unlike recent trends in literature, which often focuses on designing frontends or filters for only the initial layers of CNNs, our research introduces the Cosine Convolutional Neural Network (CosCovNN) replacing the traditional CNN filters with Cosine filters. The CosCovNN surpasses the accuracy of the equivalent CNN architectures with approximately $77\%$ less parameters. Our research further progresses with the development of an augmented CosCovNN named Vector Quantised Cosine Convolutional Neural Network with Memory (VQCCM), incorporating a memory and vector quantisation layer VQCCM achieves state-of-the-art (SOTA) performance across five different datasets in comparison with existing literature. Our findings show that cosine filters can greatly improve the efficiency and accuracy of CNNs in raw audio classification. |
| title | Raw Audio Classification with Cosine Convolutional Neural Network (CosCovNN) |
| topic | Sound Artificial Intelligence Audio and Speech Processing |
| url | https://arxiv.org/abs/2412.00312 |