Matching pursuit decomposition of speech signals for compact representation

Ye Shen; Hongmei Ai; C.-C. Jay Kuo

doi:10.1117/12.456500

10 December 2002 Matching pursuit decomposition of speech signals for compact representation

Ye Shen, Hongmei Ai, C.-C. Jay Kuo

Proceedings Volume 4861, Multimedia Systems and Applications V; (2002) https://doi.org/10.1117/12.456500
Event: ITCom 2002: The Convergence of Information Technologies and Communications, 2002, Boston, MA, United States

Abstract

Matching Pursuit (MP) expands a signal over an overcomplete dictionary of normalized atoms in an iterative fashion. A careful selection of dictionary components is critical in the design of the MP algorithm for compact signal representation and manipulation. In this research, the use of MP as an alternative waveform-coding scheme for speech signals is investigated. The improvement of MP over conventional transform coding schemes is due to the use of overcomplete basis functions. Furthermore, the performance of MP representation can be enhanced via a compact MP dictionary obtained from training. Inspired by the popular Vector Quantization (VQ) algorithm, a dictionary-training algorithm is proposed in this paper to find the optimal dictionary for MP in speech coding. The MP decomposition with a trained dictionary is shown to improve the compactness of speech representation over the traditional MP decomposition with a generic Gabor dictionary. A better SNR performance is achieved with a dictionary of a limited size, which has a good potential for future appliations.

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Ye Shen, Hongmei Ai, and C.-C. Jay Kuo "Matching pursuit decomposition of speech signals for compact representation", Proc. SPIE 4861, Multimedia Systems and Applications V, (10 December 2002); https://doi.org/10.1117/12.456500

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KEYWORDS

Associative arrays

Chemical species

Signal to noise ratio

Quantization

Signal processing

Time-frequency analysis

Frequency modulation

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