Hybrid signal decomposition based on instantaneous harmonic parameters and perceptually motivated wavelet packets for scalable audio coding

  • Authors:

  • Alexey Petrovsky;Elias Azarov;Alexander Petrovsky

  • Affiliations:

  • Belarusian State University of Informatics and Radioelectronics, Computer Engineering Department, 6, P. Brovky str., 220013 Minsk, Belarus;Belarusian State University of Informatics and Radioelectronics, Computer Engineering Department, 6, P. Brovky str., 220013 Minsk, Belarus;Belarusian State University of Informatics and Radioelectronics, Computer Engineering Department, 6, P. Brovky str., 220013 Minsk, Belarus

  • Venue:
  • Signal Processing
  • Year:
  • 2011

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Abstract

The paper presents a complete framework for hybrid representation of audio and speech signals that can be used in coding applications. The parameterization approach is based on the three-part model (sinusoids, transients and noise). The essential contributions of the paper can be summarized as follows: (i) a precise mathematical solution to the problem of instantaneous harmonic parameters estimation that can be applied to nonstationary (amplitude and frequency modulated) signals. The instantaneous harmonic parameters (magnitude, frequency and phase) are calculated as the result of the narrow-band filtering of signals. The frequency-modulated filters synthesis with the closed form impulse response has been proposed. The filter frequency bounds can be determined during the components frequency tracking and can be adjusted according to the fundamental frequency modulations; (ii) a practical technique of instantaneous harmonic analysis and numerical evaluation of its performance; (iii) a new transient parameterization scheme based on matching pursuit with frame-based psychoacoustic optimized wavelet packet dictionary. The choice of most relevant coefficients is based on maximizing the matching between the auditory excitation scalograms of original and modeled signals; (iv) the given hybrid analysis system is applied to speech and audio signals in order to validate the proposed methods.