  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.
