Improved Two-stage Wiener Filter for Robust Speaker Identification

Nonnegative Matrix Factorization Based Adaptive Noise Sensing over Wireless Sensor Networks

“…That is, the noise basis and activation matrices are obtained by adapting a priori noise basis from the instantaneous noise frames of the noisy input signal. Finally, the third processing stage of the proposed method estimates the noise-reduced speech signal by constructing a Wiener filter [8] using the adaptively estimated noise spectrum. The following subsections describe a priori NMF basis acquisition, NMF-based adaptive noise sensing, and noise reduction in detail.…”

“…In this paper, δ is set to 3 since this value of δ provides the best noise reduction performance. Next, each column of G is applied as a transfer function of the Wiener filter to each i th frame of the noisy input speech, yifalse(nfalse), resulting in an estimation of clean speech si′false(nfalse) [8].…”

“…Next, the average word error rate (WER) of an ASR system employing the proposed method was measured. Finally, the performance of the proposed method was compared with those of the two-stage mel-warped Wiener filter method (Mel-WF) [8] and the NMF-based noise reduction method without noise basis adaptation (NMF-Conv) [14].…”

“…These background noises can be classified into two types—stationary and nonstationary—depending on the variability of their characteristics over time. Many conventional methods, including spectral subtraction [5], minimum mean square error log-spectral amplitude (MMSE-LSA) [6, 7], and Wiener filtering [8], have been reported to effectively reduce stationary noise that was recorded with speech signals. Consequently, they were successfully applied to a front-end of an automatic speech recognition (ASR) system, that is, a typical speech-based application over wireless sensor networks [9].…”