Extratores de características acústicas inspirados no sistema periférico auditivo

Abstract

Extracting information from acoustic signals is a common task in signal processing and pattern recognition. Broadly speaking, the processing system has, as initial task, to obtain a low-dimensional representation of the acoustic signal, extracted trough computational methods called feature extractors. This representation aims to present the sound of speech in a more convenient form to extract the information contained in the signal. Considering the initial task of processing systems, this work presents a detailed study of three classic methods for features extracting, namely: the Mel - Frequency Cepstrum Coefficients (MFCC), the Ensemble Interval Histogram (EIH), and the Zero Crossing with Peak amplitudes (ZCPA). Still in the literature review step, a study of the human peripheral auditory system was accomplished, since the EIH and ZCPA methods are based on models of human hearing. Moreover, a new extraction method based on detection of level crossings was developed, called here as Elementary Acoustic Events (EAE). In order to compare the methods, both reviewed and developed, two different experiments were applied in this work. At first, experiments with additive noise and channel effects for robustness analysis methods were performed. Finally, experiments related to the task of isolated word recognition were applied using alignment Dynamic Time Warping (DTW). The results suggest that the proposed method is more robust than the classical methods implemented, for the proposed experiments.