Abstract-In the context of acoustic echo cancellation (AEC), it is shown that the level of sparseness in acoustic impulse responses can vary greatly in a mobile environment. When the response is strongly sparse, convergence of conventional approaches is poor. Drawing on techniques originally developed for network echo cancellation (NEC), we propose a class of AEC algorithms that can not only work well in both sparse and dispersive circumstances, but also adapt dynamically to the level of sparseness using a new sparseness-controlled approach. Simulation results, using white Gaussian noise (WGN) and speech input signals, show improved performance over existing methods. The proposed algorithms achieve these improvement with only a modest increase in computational complexity.Index Terms-Acoustic echo cancellation (AEC), network echo cancellation (NEC), sparse impulse responses, adaptive algorithms.
In this work, we focus on a recent algorithm [Z. Ying and B. P. Ng, "MUSIC-like DOA Estaimation Without Estimating the Number of Sources," IEEE Trans. Signal Process., vol. 58, no. 3, pp. 1668-1676, 2010], which is remarked to have multiple signal classification (MUSIC)-like performance without requiring to segregate the signal and noise subspaces. The optimization problem solved by this algorithm in each look direction is analyzed to obtain insights into the working principle of the algorithm. Besides showing the similarity between this algorithm and the MUSIC algorithm, its distinction from the Capon's estimator is also highlighted. The bounds for the sole parameter embedded within the optimization problem is also discussed. Simulation results evaluate the performance of the technique in comparison with the MUSIC algorithm.
Localization of impacts on solid surfaces is a challenging task due to dispersion where the velocity of wave propagation is frequency dependent. In this work, we develop a source localization algorithm on solids with applications to human-computer interface. We employ surface-mounted piezoelectric shock sensors that, in turn, allow us to convert existing flat surfaces to a low-cost touch interface. The algorithm estimates the time-differences-of-arrival between the signals via onset detection in the time-frequency domain. The proposed algorithm is suitable for vibration signals generated by a metal stylus and a finger.The validity of the algorithm is then verified on an aluminium and a glass plate surface.
Abstract-Selective-tap algorithms employing the MMax tap selection criterion were originally proposed for low-complexity adaptive filtering. The concept has recently been extended to multichannel adaptive filtering and applied to stereophonic acoustic echo cancellation. This paper first briefly reviews least mean square versions of MMax selective-tap adaptive filtering and then introduces new recursive least squares and affine projection MMax algorithms. We subsequently formulate an analysis of the MMax algorithms for time-varying system identification by modeling the unknown system using a modified Markov process. Analytical results are derived for the tracking performance of MMax selective tap algorithms for normalized least mean square, recursive least squares, and affine projection algorithms. Simulation results are shown to verify the analysis.
Abstract-Stereophonic acoustic echo cancellation has generated much interest in recent years due to the nonuniqueness and misalignment problems that are caused by the strong interchannel signal coherence. In this paper, we introduce a novel adaptive filtering approach to reduce interchannel coherence which is based on a selective-tap updating procedure. This tap-selection technique is then applied to the normalized least-mean-square, affine projection and recursive least squares algorithms for stereophonic acoustic echo cancellation. Simulation results for the proposed algorithms have shown a significant improvement in convergence rate compared with existing techniques.
Acoustic applications on a multi-rotor unmanned aerial vehicle (UAV) have been hindered by its low input signalto-noise ratio (SNR). Such low SNR condition poses prominent challenges for beamforming algorithms, statistical methods, and existing mask-based deep learning algorithms. We propose the small model on low SNR (SMoLnet), a compact convolutional neural network (CNN) to suppress UAV noise in noisy speech signals recorded off a microphone array mounted on the UAV. The proposed SMoLnet employs a large analysis window to achieve high spectral resolution since the loud UAV noise exhibits a narrow-band harmonic pattern. In the proposed SMoLnet model, exponentially-increasing dilated convolution layers were adopted to capture the global relationship across the frequency dimension. Furthermore, we performed direct spectral mapping between noisy and clean complex spectrogram to cater to the low SNR scenario. Simulation results show that the proposed SMoLnet outperforms existing dilation-based models in terms of speech quality and objective speech intelligibility metrics for UAV noise reduction. In addition, the proposed SMoLnet requires fewer parameters and achieves lower latency than the compared models.
Abstract-We investigate stereophonic acoustic echo cancellation in which solutions for the system can be nonunique and propose the use of selective-tap adaptive filters to address this problem. The main concept is to employ tap selection to optimize jointly for minimum interchannel coherence and maximum -norm of the subselected tap-input vectors. The exclusive maximum (XM) tap-selection approach is proposed and applied to normalized least-mean squares (NLMS) and recursive least-squares (RLS) algorithm. We propose an approach for solving the nonuniqueness problem employing XM tap selection in combination with a nonlinear preprocessor. Simulation results show a significant improvement in convergence rate compared with existing techniques.Index Terms-Adaptive filtering, selective-tap, stereophonic acoustic echo cancellation.
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