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This work investigates data compression algorithms for applications in non-volatile flash memories. The main goal of the data compression is to minimize the amount of user data such that the redundancy of the error correction coding can be increased and the reliability of the error correction can be improved. A compression algorithm is proposed that combines a modified move-to-front algorithm with Huffman coding. The proposed data compression algorithm has low complexity, but provides a compression gain comparable to the Lempel-Ziv-Welch algorithm.
In this paper we propose a method to determine the active speaker for each time-frequency point in the noisy signals of a microphone array. This detection is based on a statistical model where the speech signals as well as noise signals are assumed to be multivariate Gaussian random variables in the Fourier domain. Based on this model we derive a maximum-likelihood detector for the active speaker. The decision is based on the a posteriori signal to noise ratio (SNR) of a speaker dependent max-SNR beamformer.
This paper studies suitable models for the identification of nonlinear acoustic systems. A cascaded structure of nonlinear filters is proposed that contains several parallel branches, consisting of polynomial functions followed by a linear filter for each order of nonlinearity. The second order of nonlinearity is additionally modelled with a parallel branch, containing a Volterra filter. These are followed by a long linear FIR filter that is able to model the room acoustics. The model is applied to the identification of a tube power amplifier feeding a guitar loudspeaker cabinet in an acoustic room. The adaptive identification is performed by the normalized least mean square (NLMS) algorithm. Compared with a generalized polynomial Hammerstein (GPH) model, the accuracy in modelling the dedicated real world system can be improved to a greater extend than increasing the order of nonlinearity in the GPH model.