Optimal Scalogram for Computational Complexity Reduction in Acoustic Recognition Using Deep Learning

TL;DR

This paper introduces an optimized scalogram method that reduces the computational complexity of the Continuous Wavelet Transform for acoustic recognition, maintaining performance while improving efficiency.

Contribution

It proposes a novel approach to optimize wavelet kernel length and hop size, significantly lowering computational costs of CWT in deep learning-based acoustic recognition.

Findings

Reduced computational cost of CWT by optimizing parameters

Maintained recognition performance with the new method

Demonstrated effectiveness on acoustic recognition tasks

Abstract

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.