Christelle Yemdji

Robust and low-cost cascaded non-linear acoustic echo cancellation

This paper addresses the problem of acoustic echo cancellation in non-linear environments. The first contribution relates to the use of a cascaded model which divides the loudspeaker enclosure microphone system into two main blocks; the first models the downlink transducers which are assumed to be the main source of nonlinearity. The second block includes the acoustical channel and uplink transducers which are assumed to be linear and have a comparatively longer impulse response and higher time variability. The second contribution is a new non-linear adaptive echo canceler which is based on the cascaded model and has greater robustness to changes in the acoustic channel than an existing power filter approach.

Dual amplifier and loudspeaker compensation using fast convergent and cascaded approaches to non-linear acoustic echo cancellation

This paper focuses on cascaded approaches to non-linear acoustic echo cancellation (AEC) for mobile communications. The contributions in this paper are two-fold. They relate (i) to computationally efficient pre-processing and clipping compensation which aims to improve non-linear modelling and (ii) decorrelation filtering which aims to improve the tracking performance of a conventional linear AEC algorithm. While well-established in the literature the two modules require significant development in order that they function coherently in a cascaded approach. This paper presents new, adaptive parameterisation procedures for both modules and demonstrates significant improvements in terms of echo return loss enhancement when the two modules are combined.

Non-linear acoustic echo cancellation using online loudspeaker linearization

This paper presents an approach to non-linear acoustic echo cancellation (AEC).We first present the model of a loudspeaker enclosure microphone system which is divided into two blocks: a non-linear, power filter model for the down-link path (loudspeaker and amplifiers) and a linear model for the acoustic channel and up-link path. Using this model we propose an approach that uses loudspeaker linearization and linear AEC to improve performance of an otherwise classical approach to linear AEC. The novel contribution in this paper relates to a new on-line linearization pre-processing algorithm that adapts to long-term variations in the loudspeaker characteristics. This feature contrasts with fixed pre-processor aglorithms which have been reported previously.

A scalable frequency domain-based linear convolution architecture for speech enhancement

Most speech enhancement algorithms consist of a time-varying filter which is applied to the signal in the frequency domain. One of the motivations for filtering in the frequency domain compared to convolution in the time domain is to reduce the computational complexity. Filtering in the frequency domain, however, can introduce distortion if the linear convolution condition is not fulfilled. Although there are standard approaches to linear convolution in the frequency domain, they tend to be computationally prohibitive for small terminals. In this paper, we present a new and more efficient approach. The proposed approach is derived from the equivalence of zero-padding and interpolation in time and frequency domains. A distinct advantage of the approach proposed in this paper relates to its scalability which is exploited to manage computational complexity with only moderate degradation in speech quality.

A comparative assessment of noise and non-linear echo effects in acoustic echo cancellation

This paper addresses the problem of adaptive filtering for acoustic echo cancellation in noisy and non-linear environments. The first contribution relates to a new analysis on the comparative impact of additive noise and non-linear echo on the performance of adaptive filtering for linear acoustic echo cancellation (AEC). A comprehensive performance assessment is reported, including echo return loss enhancement (ERLE), convergence time and system distance metrics. This work better highlights differences between algorithm performance than previously published work and sheds new light on algorithm behavior. Results show that, in non-linear and noisy environments, the normalized-least mean square (NLMS) algorithm gives similar performance to the more complex affine projection algorithm (APA). The more computationally efficient frequency block least mean square (FBLMS) algorithm is particularly adversly effected and gives poorer performance than the basic least mean square (LMS) approach. These observations question the persuit of increased computational efficiency and reduced convergence time over robustness to distortions. The second contribution relates to an original account of the effects of non-linear echo and noise which, perhaps surprisingly, are greater for the latter. This observation highlights the need for more comprehensive studies on the effects of non-linear distortion and supports continuing efforts to tackle non-linear echo.

An experimental framework for the derivation of perceptually-optimal noise suppression functions

This paper presents a novel experimental framework designed to derive, through subjective testings, noise suppression functions which are perceptually optimal under specific experimental conditions. Noisy speech sequences are continuously processed according to a gain curve function of the a priori SNR that listeners are required to adjust two points at a time with respect to specified perceptual criteria. An experiment based on this framework is reported testing one specific combination of speech and noise signals. The specified perceptual criterion was the suitability for a phone conversation. The resulting mean experimental gain function shows a statistically significant deviation from an ideal Wiener filter. Experiments based on this framework are repeatable, suit untrained listeners and are considerably faster than conventional subjective testing methods, without the necessity to place restrictive assumptions on the assessed noise suppression function.