This paper introduces a new audio watermarking technique based on a perceptual kernel representation of audio signals (spikegram). Spikegram is a recent method to represent audio signals. It is combined with a dictionary of gammatones to construct a robust representation of sounds. In traditional phase embedding methods, the phase of coefficients of a given signal in a specific domain (such as Fourier domain) is modified. In the encoder of the proposed method (two-dictionary approach), the signs and the phases of gammatones in the spikegram are chosen adaptively to maximize the strength of the decoder. Moreover, the watermark is embedded only into kernels with high amplitudes, where all masked gammatones have been already removed. The efficiency of the proposed spikegram watermarking is shown via several experimental results. First, robustness of the proposed method is shown against 32 kb/s MP3 with an embedding rate of 56.5 b/s. Second, we showed that the proposed method is robust against unified speech and audio codec (24-kb/s USAC, linear predictive, and Fourier domain modes) with an average payload of 5-15 b/s. Third, it is robust against simulated small real room attacks with a payload of roughly 1 b/s. Last, it is shown that the proposed method is robust against a variety of signal processing transforms while preserving quality.

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.

We propose a sparse non-negative image coding based on simulated annealing and matrix pseudo-inversion. We show that sparsity and non-negativity are both important to obtain part-based coding and we also show the impact of each of them on the coding. In contrast with other approaches in the literature, our method can constrain both weights and basis vectors to generate part-based bases suitable for image recognition and fiducial point extraction. We also propose a speed-up of the algorithm by implementing a hybrid system that mixes simulated annealing and pseudo-inverse computation of matrices.