优化小波核长度与步长，降低声学识别中小波变换的计算开销。
Optimal Scalogram for Computational Complexity Reduction in Acoustic Recognition Using Deep Learning
- 通过调整小波核长度和输出尺度图步长来减少CWT计算量。
- 实验显示计算成本显著下降，模型在声学识别任务中性能保持稳定。
- 适合需要高效音频特征提取的深度学习应用开发者参考。
连续小波变换（CWT）是利用卷积神经网络（CNN）进行声学识别时有效的特征提取工具，尤其适用于非平稳音频。然而，其高计算成本带来了显著挑战，常导致研究者转向短时傅里叶变换（STFT）等替代方法。本文提出一种方法，通过优化小波核长度与输出尺度图的步长，降低CWT的计算复杂度。实验结果表明，该方法显著减少了计算开销，同时保持了训练模型在声学识别任务中的鲁棒性能。
原文摘要 · Abstract (English)
The Continuous Wavelet Transform (CWT) is an effective tool for feature extraction in acoustic recognition using Convolutional Neural Networks (CNNs), particularly when applied to non-stationary audio. However, its high computational cost poses a significant challenge, often leading researchers to prefer alternative methods such as the Short-Time Fourier Transform (STFT). To address this issue, this paper proposes a method to reduce the computational complexity of CWT by optimizing the length of the wavelet kernel and the hop size of the output scalogram. Experimental results demonstrate that the proposed approach significantly reduces computational cost while maintaining the robust performance of the trained model in acoustic recognition tasks.
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