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Electrical Engineering and Systems Science > Audio and Speech Processing
Title: Comparison Performance of Spectrogram and Scalogram as Input of Acoustic Recognition Task
(Submitted on 6 Mar 2024 (v1), last revised 26 Apr 2024 (this version, v2))
Abstract: Acoustic recognition is a common task for deep learning in recent researches, with the employment of spectral feature extraction such as Short-time Fourier transform and Wavelet transform. However, not many researches have found that discuss the advantages and drawbacks, as well as performance comparison of them. In this consideration, this paper aims to comparing the attributes of these two transforms, called spectrogram and scalogram. A Convolutional Neural Networks for acoustic faults recognition is implemented, then the performance of them is recorded for comparison. A latest research on the same audio database is considered for benchmarking to see how good the designed spectrogram and scalogram is. The advantages and limitations of them are also analyzed. By doing so, the results of this paper provide indications for application scenarios of spectrogram and scalogram, as well as potential further research directions.
Submission history
From: Dang Thoai Phan [view email][v1] Wed, 6 Mar 2024 11:02:07 GMT (1588kb)
[v2] Fri, 26 Apr 2024 07:06:26 GMT (1622kb)
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