Abstract
This paper deals with the application of adaptive signal models for parametric speech and audio compression. The matching pursuit algorithm is used for extracting sinusoidal components and transients in audio signals. The resulting residue is perceptually modelled as a noise like signal. When a transient is detected, psychoacoustic-adapted matching pursuits are accomplished using a wavelet-based dictionary followed of an harmonic one. Otherwise, matching pursuit is applied only to the harmonic dictionary. This multi-part model (Sines + Transients + Noise) is successfully applied for speech and audio coding purposes, assuring high perceptual quality at low bit rates (close to 16 kbps for most of the signals considered for testing).
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Vera-Candeas, P., Ruiz-Reyes, N., Rosa-Zurera, M., Cuevas-Martinez, J.C., López-Ferreras, F. (2005). Adaptive Signal Models for Wide-Band Speech and Audio Compression. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds) Pattern Recognition and Image Analysis. IbPRIA 2005. Lecture Notes in Computer Science, vol 3523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11492542_70
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DOI: https://doi.org/10.1007/11492542_70
Publisher Name: Springer, Berlin, Heidelberg
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