用余弦滤波器替代传统卷积核，大幅减少参数量并提升音频分类效果。
Raw Audio Classification with Cosine Convolutional Neural Network (CosCovNN)
- 用余弦函数构造卷积核，取代传统CNN滤波器。
- 参数量减少77%的同时，准确率仍超等效CNN模型。
- 结合记忆与向量量化，实现五个数据集的SOTA表现。
本研究探索使用卷积神经网络（CNN）对原始音频波形进行分类，避免了预处理阶段提取特定特征的步骤。不同于近期文献中仅针对CNN初始层设计前端或滤波器的趋势，本文提出余弦卷积神经网络（CosCovNN），以余弦滤波器替代传统卷积核。CosCovNN在参数量约减少77%的情况下，性能超越等效的CNN架构。进一步发展出增强版VQCCM模型，引入记忆模块和向量量化层，在五个不同数据集上均达到当前最优（SOTA）性能。结果表明，余弦滤波器能显著提升CNN在原始音频分类中的效率与准确率。
原文摘要 · Abstract (English)
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.
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