Toon van Waterschoot

Fifty Years of Acoustic Feedback Control: State of the Art and Future Challenges

The acoustic feedback problem has intrigued researchers over the past five decades, and a multitude of solutions has been proposed. In this survey paper, we aim to provide an overview of the state of the art in acoustic feedback control, to report results of a comparative evaluation with a selection of existing methods, and to cast a glance at the challenges for future research.

Nonlinear Acoustic Echo Cancellation Based on a Sliding-Window Leaky Kernel Affine Projection Algorithm

Acoustic echo cancellation (AEC) is used in speech communication systems where the existence of echoes degrades the speech intelligibility. Standard approaches to AEC rely on the assumption that the echo path to be identified can be modeled by a linear filter. However, some elements introduce nonlinear distortion and must be modeled as nonlinear systems. Several nonlinear models have been used with more or less success. The kernel affine projection algorithm (KAPA) has been successfully applied to many areas in signal processing but not yet to nonlinear AEC (NLAEC). The contribution of this paper is three-fold: (1) to apply KAPA to the NLAEC problem, (2) to develop a sliding-window leaky KAPA (SWL-KAPA) that is well suited for NLAEC applications, and (3) to propose a kernel function, consisting of a weighted sum of a linear and a Gaussian kernel. In our experiment set-up, the proposed SWL-KAPA for NLAEC consistently outperforms the linear APA, resulting in up to 12 dB of improvement in ERLE at a computational cost that is only 4.6 times higher. Moreover, it is shown that the SWL-KAPA outperforms, by 4-6 dB, a Volterra-based NLAEC, which itself has a much higher 413 times computational cost than the linear APA.

Adaptive feedback cancellation for audio applications

Acoustic feedback occurs in many audio applications involving musical sound signals. However, research efforts in acoustic feedback control have mainly been focused on speech applications. Since sound quality is of prime importance in audio applications, a proactive approach to acoustic feedback control is preferred to avoid ringing, howling, and excessive reverberation. Adaptive feedback cancellation (AFC) using a prediction-error-method (PEM)-based approach is a promising proactive solution, but existing algorithms are again designed for speech applications only. We propose to replace the all-pole near-end speech signal model in the PEM-based approach with a cascade of two near-end signal models: a tonal components model and a noise components model. We derive the identifiability conditions for joint identification of the acoustic feedback path and the cascaded near-end signal models. Depending on the model structure that is used for the near-end tonal components, three different PEM-based AFC algorithms are considered. By applying some relevant model approximations, the computational overhead of the proposed algorithms compared to the normalized least mean squares (NLMS) algorithm can be reduced to 25% of the NLMS complexity. Simulation results for both room acoustic and hearing aid scenarios indicate a significant performance improvement in terms of the misadjustment and the maximum stable gain increase.

Optimally regularized adaptive filtering algorithms for room acoustic signal enhancement

In many room acoustic signal processing applications, a room impulse response identification is needed to eliminate undesired effects such as echo, feedback, or reverberation. This is typically done using an adaptive filter driven by a speech or audio input signal. However, such signals exhibit poor excitation properties, which cause standard adaptive filtering algorithms to be very sensitive to disturbing signals, especially in the underdetermined case. A popular remedy is regularization, which is usually implemented with a scaled identity regularization matrix. This type of regularization is governed by a single regularization parameter, the value of which is often chosen in an arbitrary way. We propose to regularize the adaptive filter using a non-identity regularization matrix, in which prior knowledge on the unknown room impulse response may be incorporated. When knowledge of the disturbing signal is also used to add prefiltering and weighting in the adaptation, a new family of regularized adaptive filtering algorithms is obtained, which is shown to be optimal in a mean square error sense. Existing regularized algorithms can then be obtained as special cases, assuming limited or no prior knowledge is available. When combined with a recently proposed method of extracting prior knowledge from the acoustic setup, our algorithms exhibit superior convergence behaviour compared to existing algorithms in different simulation scenarios, while the additional computational cost is small.

Declipping of Audio Signals Using Perceptual Compressed Sensing

The restoration of clipped audio signals, commonly known as declipping, is important to achieve an improved level of audio quality in many audio applications. In this paper, a novel declipping algorithm is presented, jointly based on the theory of compressed sensing (CS) and on well-established properties of human auditory perception. Declipping is formulated as a sparse signal recovery problem using the CS framework. By additionally exploiting knowledge of human auditory perception, a novel perceptual compressed sensing (PCS) framework is devised. A PCS-based declipping algorithm is proposed which uses

Comparison of Linear Prediction Models for Audio Signals

\ell _{1}

Multi-channel linear prediction-based speech dereverberation with sparse priors

-norm type reconstruction. Comparative objective and subjective evaluation experiments reveal a significant audio quality increase for the proposed PCS-based declipping algorithm compared to CS-based declipping algorithms.

Improved prediction error filters for adaptive feedback cancellation in hearing aids

While linear prediction (LP) has become immensely popular in speech modeling, it does not seem to provide a good approach for modeling audio signals. This is somewhat surprising, since a tonal signal consisting of a number of sinusoids can be perfectly predicted based on an (all-pole) LP model with a model order that is twice the number of sinusoids. We provide an explanation why this result cannot simply be extrapolated to LP of audio signals. If noise is taken into account in the tonal signal model, a low-order all-pole model appears to be only appropriate when the tonal components are uniformly distributed in the Nyquist interval. Based on this observation, different alternatives to the conventional LP model can be suggested. Either the model should be changed to a pole-zero, a high-order all-pole, or a pitch prediction model, or the conventional LP model should be preceded by an appropriate frequency transform, such as a frequency warping or downsampling. By comparing these alternative LP models to the conventional LP model in terms of frequency estimation accuracy, residual spectral flatness, and perceptual frequency resolution, we obtain several new and promising approaches to LP-based audio modeling.

Blind CP-OFDM and ZP-OFDM parameter estimation in frequency selective channels

The quality of speech signals recorded in an enclosure can be severely degraded by room reverberation. In this paper, we focus on a class of blind batch methods for speech dereverberation in a noiseless scenario with a single source, which are based on multi-channel linear prediction in the short-time Fourier transform domain. Dereverberation is performed by maximum-likelihood estimation of the model parameters that are subsequently used to recover the desired speech signal. Contrary to the conventional method, we propose to model the desired speech signal using a general sparse prior that can be represented in a convex form as a maximization over scaled complex Gaussian distributions. The proposed model can be interpreted as a generalization of the commonly used time-varying Gaussian model. Furthermore, we reformulate both the conventional and the proposed method as an optimization problem with an lp-norm cost function, emphasizing the role of sparsity in the considered speech dereverberation methods. Experimental evaluation in different acoustic scenarios show that the proposed approach results in an improved performance compared to the conventional approach in terms of instrumental measures for speech quality.

Distributed estimation of static fields in wireless sensor networks using the finite element method

Acoustic feedback is a well-known problem in hearing aids, caused by the undesired acoustic coupling between the hearing aid loudspeaker and microphone. Acoustic feedback produces annoying howling sounds and limits the maximum achievable hearing aid amplification. This paper is focused on adaptive feedback cancellation (AFC) where the goal is to adaptively model the acoustic feedback path and estimate the feedback signal, which is then subtracted from the microphone signal. The main problem in identifying the acoustic feedback path model is the correlation between the near-end signal and the loudspeaker signal caused by the closed signal loop, in particular when the near-end signal is spectrally colored as is the case for a speech signal. This paper adopts a prediction-error method (PEM)-based approach to AFC, which is based on the use of decorrelating prediction error filters (PEFs). We propose a number of improved PEF designs that are inspired by harmonic sinusoidal modeling and pitch prediction of speech signals. The resulting PEM-based AFC algorithms are evaluated in terms of the maximum stable gain (MSG), filter misadjustment, and computational complexity. Simulation results for a hearing aid scenario indicate an improvement up to 5-7dB in MSG and up to 6-8dB in terms of filter misadjustment.