arXiv subjects

Dang Thoai Phan

Publications and source records attributed to Dang Thoai Phan.

3 recordsLinked to original sources

arXiv2025

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

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.

eess.AS↗

arXiv2025

Comparison Performance of Spectrogram and Scalogram as Input of Acoustic Recognition Task

Acoustic recognition has emerged as a prominent task in deep learning research, frequently utilizing spectral feature extraction techniques such as the spectrogram from the Short-Time Fourier Transform and the scalogram from the Wavelet Transform. However, there is a notable deficiency in studies that comprehensively discuss the advantages, drawbacks, and performance comparisons of these methods. This paper aims to evaluate the characteristics of these two transforms as input data for acoustic recognition using Convolutional Neural Networks. The performance of the trained models employing both transforms is documented for comparison. Through this analysis, the paper elucidates the advantages and limitations of each method, provides insights into their respective application scenarios, and identifies potential directions for further research.

eess.AS↗

arXiv2025

Reduce Computational Complexity for Continuous Wavelet Transform in Acoustic Recognition Using Hop Size

In recent years, the continuous wavelet transform (CWT) has been employed as a spectral feature extractor for acoustic recognition tasks in conjunction with machine learning and deep learning models. However, applying the CWT to each individual audio sample is computationally intensive. This paper proposes an approach that applies the CWT to a subset of samples, spaced according to a specified hop size. Experimental results demonstrate that this method significantly reduces computational costs while maintaining the robust performance of the trained models.

eess.AS↗