Inverse Filtering for Speech Dereverberation Less Sensitive to Noise and Room Transfer Function Fluctuations
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Research Article Inverse Filtering for Speech Dereverberation Less Sensitive to Noise and Room Transfer Function Fluctuations Takafumi Hikichi, Marc Delcroix, and Masato Miyoshi Media Information Laboratory, NTT Communication Science Laboratories, NTT Corporation, 2-4 Hikaridai, Seika-cho, Soraku-gun, Kyoto 619-0237, Japan Received 16 November 2006; Accepted 2 February 2007 Recommended by Liang-Gee Chen Inverse filtering of room transfer functions (RTFs) is considered an attractive approach for speech dereverberation given that the time invariance assumption of the used RTFs holds. However, in a realistic environment, this assumption is not necessarily guaranteed, and the performance is degraded because the RTFs fluctuate over time and the inverse filter fails to remove the effect of the RTFs. The inverse filter may amplify a small fluctuation in the RTFs and may cause large distortions in the filter’s output. Moreover, when interference noise is present at the microphones, the filter may also amplify the noise. This paper proposes a design strategy for the inverse filter that is less sensitive to such disturbances. We consider that reducing the filter energy is the key to making the filter less sensitive to the disturbances. Using this idea as a basis, we focus on the influence of three design parameters on the filter energy and the performance, namely, the regularization parameter, modeling delay, and filter length. By adjusting these three design parameters, we confirm that the performance can be improved in the presence of RTF fluctuations and interference noise. Copyright © 2007 Takafumi Hikichi et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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INTRODUCTION
Inverse filtering of room acoustics is useful in various applications such as sound reproduction, sound-field equalization, and speech dereverberation. Usually, room transfer functions (RTFs) are modeled as finite impulse response (FIR) filters, and inverse filters are designed to remove the effect of the RTFs. When the RTFs are known a priori or are capable of being accurately estimated, this approach has been shown to achieve high inverse filtering performance [1– 4]. However, in actual acoustic environments, there are disturbances that affect the inverse filtering performance. One cause of these disturbances is the fluctuation in the RTFs resulting from changes in such factors as source position and temperature [5–9]. As a result, an inverse filter correctly designed for one condition may not work well for another condition, and compensation or adaptation processing may become necessary. The sensitivity issue with inverse filtering in relation to the movement of a sound source or microphone has been addressed in several papers. In [8, 9], the sensitivity of inverse filters is quantified in terms of the mean-squared error (MSE), defined as the power of the deviation of the equal-
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