Neural-based approach to perceptual sparse coding of audio signals
Ramin Pichevar, Hossein Najaf-Zadeh, Frédéric Mustière · 2010
We propose a neural architecture for the perceptual sparse coding of audio signals based on a previously proposed technique called Local Competitive Algorithm (LCA) that was originally applied to image and video coding. LCAs are designed to be implemented in a dynamical system composed of many neuron-like elements operating in parallel. For the processing of audio signals, we here use gamma-tone filters that mimic the behavior of the auditory pathway as the receptive field of our neurons. We also adapted LCA to time-varying audio signals. Given the fact that LCA does not take into account the difference between perceived coding error and mathematical Mean-Squared Error (MSE), we propose in this article the Perceptual Local Competitive Algorithm (PLCA) and derive a convergence formula, as well as a corresponding neural architecture for it.We show that our proposed PLCA minimizes the perceptual coding error and can model phenomena such as absolute threshold of hearing and masking. Our sparse audio coder based on PLCA compares with the more conventional greedy (i.e., matching pursuit) algorithms for sparse coding in terms of quality and can be implemented in a much faster way, especially when parallel processing units (i.e., embedded circuits) can be afforded. We also show that our proposed PLCA is much more robust to quantization error than the conventional matching pursuit for audio coding.