A flexible and multipurpose bio-inspired hierarchical model for analyzing musical timbre is presented in this paper. Inspired by findings in the fields of neuroscience, computational neuroscience, and psychoacoustics, not only does the model extract spectral and temporal characteristics of a signal, but it also analyzes amplitude modulations on different timescales. It uses a cochlear filter bank to resolve the spectral components of a sound, lateral inhibition to enhance spectral resolution, and a modulation filter bank to extract the global temporal envelope and roughness of the sound from amplitude modulations. The model was evaluated in three applications. First, it was used to simulate subjective data from two roughness experiments. Second, it was used for musical instrument classification using the k-NN algorithm and a Bayesian network. Third, it was applied to find the features that characterize sounds whose timbres were labeled in an audiovisual experiment. The successful application of the proposed model in these diverse tasks revealed its potential in capturing timbral information.

A new bio-inspired speech analysis system that extracts acoustical speech events is proposed and used in the design of a variable frame rate (VFR) speech recognizer. The same speech recognizer (Hidden Markov Model -HMM- and Mel Frequency Cepstrum Coefficients -MFCC-) has been used with the proposed VFR analysis and conventional fixed frame rate (FFR) approach. In comparison with other VFR recognizers, the hierarchical features in the proposed system have the potential to serve as classification parameters of a complete bio-inspired speech recognition system. Also, no voice activity detection is required and there are no hard decisions to be taken by the system. Events are used to label and identify the moments at which the acoustical properties of speech are stable or changing. These events are markers on which an analysis window can be positioned to perform the recognition. Inspired by our knowledge of the auditory and visual systems, hierarchical complex features like transients and energy orientation are used. Training has been done on clean speech and recognition on noisy (from 20dB to −10dB Signal to Noise Ratios -SNR) or reverberated speech by using the TI 46-word database corrupted with 4 noises taken from the Aurora 2 data. In comparison with a FFR recognizer, our VFR system yields more than 50% increase in recognition rates for a speaker independent isolated word recognition task when SNRs are between 0 and 20 dB.