A CASA-Based System for Long-Term SNR Estimation
A CASA-Based System for Long-Term SNR Estimation
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DOI：
10.1109/tasl.2012.2205242
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发表时间：
2012-11
期刊：
影响因子：
--
通讯作者：
A. Narayanan;Deliang Wang
中科院分区：
文献类型：
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作者：
A. Narayanan;Deliang Wang
提出了一种基于计算听觉场景分析（CASA）的鲁棒信噪比估计系统。该算法利用对理想二值掩模的估计，将噪声信号的时频表示分离为语音主导和噪声主导区域。将这些区域内的能量相加，得到滤波后的全局信噪比。引入信噪比变换，将估计的滤波信噪比转换为含噪信号的真实宽带信噪比。将该算法进一步扩展到估计子带信噪比。使用TIMIT语音语料库和NOISEX92噪声数据库进行评估。结果表明，在低信噪比条件下，全局和子带信噪比估计都优于现有方法。
We present a system for robust signal-to-noise ratio (SNR) estimation based on computational auditory scene analysis (CASA). The proposed algorithm uses an estimate of the ideal binary mask to segregate a time-frequency representation of the noisy signal into speech dominated and noise dominated regions. Energy within each of these regions is summated to derive the filtered global SNR. An SNR transform is introduced to convert the estimated filtered SNR to the true broadband SNR of the noisy signal. The algorithm is further extended to estimate subband SNRs. Evaluations are done using the TIMIT speech corpus and the NOISEX92 noise database. Results indicate that both global and subband SNR estimates are superior to those of existing methods, especially at low SNR conditions.