Abstract
In this paper, a two-stage scheme is proposed to deal with the difficult problem of acoustic echo cancellation (AEC) in single-channel scenario in the presence of noise. In order to overcome the major challenge of getting a separate reference signal in adaptive filter-based AEC problem, the delayed version of the echo and noise suppressed signal is proposed to use as reference. A modified objective function is thereby derived for a gradient-based adaptive filter algorithm, and proof of its convergence to the optimum Wiener-Hopf solution is established. The output of the AEC block is fed to an acoustic noise cancellation (ANC) block where a spectral subtraction-based algorithm with an adaptive spectral floor estimation is employed. In order to obtain fast but smooth convergence with maximum possible echo and noise suppression, a set of updating constraints is proposed based on various speech characteristics (e.g., energy and correlation) of reference and current frames considering whether they are voiced, unvoiced, or pause. Extensive experimentation is carried out on several echo and noise corrupted natural utterances taken from the TIMIT database, and it is found that the proposed scheme can significantly reduce the effect of both echo and noise in terms of objective and subjective quality measures.
Highlights
The phenomenon of acoustic echo occurs when the output speech signal from a loudspeaker gets reflected from different surfaces, like ceilings, walls, and floors and fed back to the microphone
6 Conclusion The problem of echo cancellation in the presence of noise, especially in single-channel environment, is a very challenging task, which has been efficiently tackled in this paper
The single-channel acoustic echo cancellation (AEC) block is designed based on the gradient-based adaptive least mean squares (LMS) filter where to overcome the problem of getting a separate reference signal, we propose to use the delayed version of the echosuppressed signal
Summary
The phenomenon of acoustic echo occurs when the output speech signal from a loudspeaker gets reflected from different surfaces, like ceilings, walls, and floors and fed back to the microphone. There are some methods that deal with both acoustic echo and noise cancellation (AENC) [16,17,18]. An AENC scheme is proposed which can efficiently deal with the single-channel scenario. Unlike conventional LMS algorithm, considering the delayed version of the previously echo- and noisesuppressed signal as reference, a gradient-based adaptive LMS algorithm is developed for single channel AEC. A single-channel ANC algorithm based on spectral subtraction with an adaptive spectral floor estimation is developed, which reduces the effect of noise and some residual echo. Analyzing different speech characteristics of the reference and current frames, multiconditional updating constraints are proposed in order to obtain precise control on convergence characteristics. Extensive experimentation is conducted on several real-life echo and noise corrupted speech signals at different acoustic environments
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