Abstract
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.
Highlights
Inverse filtering of room acoustics is useful in various applications such as sound reproduction, sound-field equalization, and speech dereverberation
When the regularization parameter is smaller than 10−2, the performance monotonically decreased as the regularization parameter decreased, according to the increase in the filter energy
The regularization parameter, modeling delay, and filter length were selected to improve the performance when the room transfer functions (RTFs) fluctuated and when slight interference noise was present at the microphone signals
Summary
Inverse filtering of room acoustics is useful in various applications such as sound reproduction, sound-field equalization, and speech dereverberation. 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 [5,6,7,8,9]. An inverse filter correctly designed for one condition may not work well for another condition, and compensation or adaptation processing may become necessary
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