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In this paper, the authors present an improved implementation for an auditory-inspired FFT-based model which calculates a noise-robust FFT spectrum. Through the use of Characteristic Frequency (CF) values of the cochlear filters in an Early Auditory (EA) model for power spectrum selection, and the use of a pair of running averages for the implementation of self-normalization, the proposed FFT model allows more flexibility in the extraction of audio features. To evaluate the performance of the proposed FFT model, a speech/music/noise classification task is carried out wherein a decision tree learning algorithm (C4.5) is used as the classifier.
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