The current paper focuses on the investigation of wavelet approaches for joint time, frequency, and cepstral audio feature extraction. Wavelets have been thoroughly studied over the last decades as an alternative signal analysis approach. Wavelet-features have also been successfully implemented in a variety of pattern recognition applications, including audio semantics. Recently, wavelet-adapted mel-frequency cepstral coefficients have been proposed as applicable features in speech recognition and general audio classification, incorporating perceptual attributes. In this context, various wavelet configuration-schemes are examined for wavelet-cepstral audio features extraction. Additional wavelet parameters are utilized in the formation of wavelet-feature-vectors and evaluated in terms of salient feature ranking. Comparisons with classical time-frequency and cepstral audio features are conducted in typical audio-semantics scenarios.
https://www.aes.org/e-lib/browse.cfm?elib=16759
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