Tomas Fritz Gaensler

A novel perspective on stereophonic acoustic echo cancellation

The stereophonic acoustic echo is due to the coupling between two loudspeakers and two microphones. In the classical approach, this configuration is modelled by a two-input/two-output system with real random variables. In this paper, we propose to redesign this scheme as a single-input/single-output system with complex random variables. In this framework, we illustrate the behavior of some basic adaptive algorithms and present a distortion method which is more suitable for this model.

Time-domain noise reduction based on an orthogonal decomposition for desired signal extraction

This paper addresses the problem of noise reduction in the time domain where the clean speech sample at every time instant is estimated by filtering a vector of the noisy speech signal. Such a clean speech estimate consists of both the filtered speech and residual noise (filtered noise) as the noisy vector is the sum of the clean speech and noise vectors. Traditionally, the filtered speech is treated as the desired signal after noise reduction. This paper proposes to decompose the clean speech vector into two orthogonal components: one is correlated and the other is uncorrelated with the current clean speech sample. While the correlated component helps estimate the clean speech, it is shown that the uncorrelated component interferes with the estimation, just as the additive noise. Based on this orthogonal decomposition, the paper presents a way to define the error signal and cost functions and addresses the issue of how to design different optimal noise reduction filters by optimizing these cost functions. Specifically, it discusses how to design the maximum SNR filter, the Wiener filter, the minimum variance distortionless response (MVDR) filter, the tradeoff filter, and the linearly constrained minimum variance (LCMV) filter. It demonstrates that the maximum SNR, Wiener, MVDR, and tradeoff filters are identical up to a scaling factor. It also shows from the orthogonal decomposition that many performance measures can be defined, which seem to be more appropriate than the traditional ones for the evaluation of the noise reduction filters.

Class of double-talk detectors based on the holder inequality

Most of the echo cancellers are equipped with a double-talk detector (DTD) in order to control the behavior of the adaptive filter during double-talk situations. In this paper, we propose a class of DTDs based on the Holder inequality. These DTDs are simple to implement, have low computational complexity, and perform well even for low echo-to-noise ratios. As a particular case, it is shown that the well-known Geigel algorithm can be obtained from this approach.

On single-channel noise reduction in the time domain

In this paper, we revisit the noise-reduction problem in the time domain and present a way to decompose the filtered speech into two uncorrelated (orthogonal) components: the desired speech and the interference. Based on this new decomposition, we discuss how to form different optimization cost functions and address the issue of how to design different noise-reduction filters by optimizing these new cost functions. Particularly, we cover the design of the maximum signal-to-noise-ratio (SNR), the Wiener, the minimum variance distortionless response (MVDR), and the tradeoff filters. It is interesting that with this new decomposition, we can now design the MVDR filter that can achieve noise reduction without adding speech distortion in the single-channel case, which has never been seen before. We also demonstrate that the maximum SNR, Wiener, and tradeoff filters are identical to the MVDR filter up to a scaling factor. From a theoretical point of view, this scaling factor is not significant and should not affect the output SNR at any processing time. But from a practical viewpoint, the scaling factor can be time-varying due to the nonstationarity of the speech and possibly the noise and can cause discontinuity in the residual noise level, which is unpleasant to listen to. As a result, it is essential to have the scaling factor right from one processing sample (or frame) to another in order to avoid large distortions and for this reason, it is recommended to use the MVDR filter in speech enhancement applications.