摘要
依据噪声能量谱密度(PSD)分布的拖尾特性,本文采用瑞利(Rayleigh)分布表示噪声能量谱密度的分布,推导出基于Rayleigh分布的新判决阈值更新表达式,并提出一种改进的有音区检测(VAD)算法。由于Rayleigh分布下虚警概率具有解析表达式,从而避免了计算逆互补误差函数,降低了算法的复杂度。实验结果表明,在非平稳噪声环境下,其性能指标值优于文献[8]的算法。
In this paper we propose an improved voice activity detection （VAD） algorithm by modeling the noise power spectrum density （PSD） distribution with Rayleigh model. It is well known that the probability density function （PDF） of noise PSD obtained from Welch approach has the ＇ tail＇ characteristic, It can be modeled more accurately by using Rayleigh distribution. Under the Rayleigh distribution a new expression for the decision threshold update is derived. Due to the analytical expression of the false alarm probability, the computation of the inverse complementary error function is avoided and the computational complexity of the proposed VAD is reduced. Experimental results show that the proposed VAD outperforms the VAD scheme mentioned in [ 5 ] under non-stationary noise environments.
出处
《信号处理》
CSCD
北大核心
2009年第11期1809-1813,共5页
Journal of Signal Processing
基金
国家自然科学基金资助项目(No.60575006)
广州市留学人员科技创业资助计划(No.2006V11I0831)资助项目
关键词
统计有音区检测
自适应判决阈值
瑞利分布
算法复杂度
Statistical voice activity detection
adaptive decision threshold
Rayleigh distribution
computational complexity
