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Scalable Parametric Audio Coder Using Sparse Approximation with Frame-to-Frame Perceptually Optimized Wavelet Packet Based Dictionary

This paper is devoted to the development of a scalable parametric audio coder based on a matching pursuit algorithm with a frame-based psychoacoustic optimized wavelet packet dictionary. The main idea is to parameterize audio signal with a minimum number of non-negative elements. This can be done by applying sparse approximation such as matching pursuit algorithm. In contrast with current approaches in audio coding based on sparse approximation we introduce a model of dynamic dictionary forming for each frame of input audio signal individually based on wavelet packet decomposition and dynamic wavelet packet tree transformation with psychoacoustic model. Experimental results of developed encoder and comparison with modern popular audio encoders are provided.

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AES - Audio Engineering Society