基于变窗长搜索的改进型噪声估计算法
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摘要
MCRA最小值递归平均算法对噪声的估计值较为准确,而且对一段话音内噪声功率谱的变化也能准确的追踪.但是面对噪声功率谱突然陡增这种情况,需要经过一段时间的自适应才能得到准确的噪声估计值,而在这个自适应期间,会留下较强的残留噪声,影响人的听感.本文在MCRA算法的基础上,引入一种利用最大对数似然比结合能零比的VAD(Voice activity Detection)辅助算法,得到一种改进型噪声估计算法.实验仿真结果也表明,改进的噪声估计算法在噪声估计速度方面优于MCRA算法.
The MCRA minimum recursive algorithm is accurate for the noise estimation, and the changes of noise power spectrum in a speech can be tracked accurately. However, if the noise power spectrum increases too much suddenly, the original algorithm needs a period of time to get the accurate noise, and in this adaptive period, it will leave strong residual noise and affect people's hearing experience. This paper introduces a Voice Activity Detection(VAD) algorithm which uses the maximum log-likelihood ratio with energy-zero ratio, and an improved noise estimation algorithm on the basis of MCRA is obtained. Experimental simulation also proves that the improved algorithm is better than the original algorithm in noise estimation speed.
The MCRA minimum recursive algorithm is accurate for the noise estimation, and the changes of noise power spectrum in a speech can be tracked accurately. However, if the noise power spectrum increases too much suddenly, the original algorithm needs a period of time to get the accurate noise, and in this adaptive period, it will leave strong residual noise and affect people's hearing experience. This paper introduces a Voice Activity Detection(VAD) algorithm which uses the maximum log-likelihood ratio with energy-zero ratio, and an improved noise estimation algorithm on the basis of MCRA is obtained. Experimental simulation also proves that the improved algorithm is better than the original algorithm in noise estimation speed.
引文
1Martin R. Noise power spectral density estimation based onoptimal smoothing and minimum statistics. IEEETransactions on Speech and Audio Processing, 2001, 9(5):504–512. [doi: 10.1109/89.928915]
2Cohen I, Berdugo B. Noise estimation by minima controlledrecursive averaging for robust speech enhancement. IEEESignal Processing Letters, 2002, 9(1): 12–15. [doi: 10.1109/97.988717]
3郑成诗, 李晓东, 莫福源, 等. 一种噪声谱估计的快速跟踪算法. 中国声学学会 2006 年全国声学学术会议论文集. 厦门, 中国. 2006. 401–402.
4Rangachari S, Loizou PC. A noise-estimation algorithm forhighly non-stationary environments. Speech Communication,2006, 48(2): 220–231. [doi: 10.1016/j.specom.2005.08.005]
5贺慧芳, 马建芬. 基于语音帧检测和子带谱跟踪的噪声估计算法. 计算机工程, 2009, 35(20): 281–282. [doi: 10.3969/j.issn.1000-3428.2009.20.099]
6熊晶. 语音增强中噪声估计的研究[硕士学位论文]. 兰州:兰州交通大学, 2015.
7王旭贞. 汽车噪声下语音增强算法的研究与应用[硕士学位论文]. 杭州: 杭州电子科技大学, 2015.
8Maragos P. Fractal aspects of speech signals: Dimension andinterpolation. Proceedings of 1991 International Conferenceon Acoustics, Speech, and Signal Processing. Toronto, ON,Canada. 1991. 417–420. [doi: 10.1109/ICASSP.1991.150365]
9Chang JH, Kim NS, Mitra SK. Voice activity detection basedon multiple statistical models. IEEE Transactions on SignalProcessing, 2006, 54(6): 1965–1976. [doi: 10.1109/TSP.2006.874403]
10夏丙寅, 鲍长春. 适应噪声强度突变的噪声估计加速方法.信号处理, 2013, 29(10): 1336–1345. [doi: 10.3969/j.issn.1003-0530.2013.10.010]
11Ephraim Y, Malah D. Speech enhancement using aminimum-mean square error short-time spectral amplitudeestimator. IEEE Transactions on Acoustics, Speech, andSignal Processing, 1984, 32(6): 1109–1121. [doi: 10.1109/TASSP.1984.1164453]
12张建伟,陶亮,周健,等. 基于改进谱平滑策略的IMCRA 算法及其语音增强. 计算机工程与应用, 2017,53(1): 153–157. [doi: 10.3778/j.issn.1002-8331.1504-0029]13Cohen I, Berdugo B. Speech enhancement for non-stationarynoise environments. Signal Processing, 2001, 81(11):2403–2418. [doi: 10.1016/S0165-1684(01)00128-1]
2Cohen I, Berdugo B. Noise estimation by minima controlledrecursive averaging for robust speech enhancement. IEEESignal Processing Letters, 2002, 9(1): 12–15. [doi: 10.1109/97.988717]
3郑成诗, 李晓东, 莫福源, 等. 一种噪声谱估计的快速跟踪算法. 中国声学学会 2006 年全国声学学术会议论文集. 厦门, 中国. 2006. 401–402.
4Rangachari S, Loizou PC. A noise-estimation algorithm forhighly non-stationary environments. Speech Communication,2006, 48(2): 220–231. [doi: 10.1016/j.specom.2005.08.005]
5贺慧芳, 马建芬. 基于语音帧检测和子带谱跟踪的噪声估计算法. 计算机工程, 2009, 35(20): 281–282. [doi: 10.3969/j.issn.1000-3428.2009.20.099]
6熊晶. 语音增强中噪声估计的研究[硕士学位论文]. 兰州:兰州交通大学, 2015.
7王旭贞. 汽车噪声下语音增强算法的研究与应用[硕士学位论文]. 杭州: 杭州电子科技大学, 2015.
8Maragos P. Fractal aspects of speech signals: Dimension andinterpolation. Proceedings of 1991 International Conferenceon Acoustics, Speech, and Signal Processing. Toronto, ON,Canada. 1991. 417–420. [doi: 10.1109/ICASSP.1991.150365]
9Chang JH, Kim NS, Mitra SK. Voice activity detection basedon multiple statistical models. IEEE Transactions on SignalProcessing, 2006, 54(6): 1965–1976. [doi: 10.1109/TSP.2006.874403]
10夏丙寅, 鲍长春. 适应噪声强度突变的噪声估计加速方法.信号处理, 2013, 29(10): 1336–1345. [doi: 10.3969/j.issn.1003-0530.2013.10.010]
11Ephraim Y, Malah D. Speech enhancement using aminimum-mean square error short-time spectral amplitudeestimator. IEEE Transactions on Acoustics, Speech, andSignal Processing, 1984, 32(6): 1109–1121. [doi: 10.1109/TASSP.1984.1164453]
12张建伟,陶亮,周健,等. 基于改进谱平滑策略的IMCRA 算法及其语音增强. 计算机工程与应用, 2017,53(1): 153–157. [doi: 10.3778/j.issn.1002-8331.1504-0029]13Cohen I, Berdugo B. Speech enhancement for non-stationarynoise environments. Signal Processing, 2001, 81(11):2403–2418. [doi: 10.1016/S0165-1684(01)00128-1]