1. 集美大学 信息工程学院, 海上通信与智能电子系统福建省高等学校重点实验室, 厦门 361021 ;
2. 清华大学 信息技术研究院, 语音和语言技术中心, 北京 100084 ;
3. 厦门莱亚特医疗器械有限公司, 厦门 361009

Sub-band adaptive noise reduction algorithm to improve speech intelligibility
LIANG Weiqian1 , ZHENG Fang2 , ZHENG Jiachun1 , PIAO Zhigang3
1. Key Laboratory of Maritime Communication and Intelligent Electronic Systems of Fujian Province, College of Information Engineering, Jimei University, Xiamen 361021, China ;
2. Center for Speech and Language Technologies, Research Institute of Information Technology, Tsinghua University, Beijing 100084, China ;
3. Xiamen LA and Associates Medical Equipment Co., Ltd., Xiamen 361009, China
Abstract:Noise reduction algorithms to improve speech intelligibility are needed when sounds are compressed and amplified in hearing aids. A sub-band adaptive noise reduction algorithm was developed with a weighted overlap-add filter bank and psycho-acoustic model for the sub-band splitting. The non-linear noise reduction gains are computed with an estimated a posteriori signal to noise ratio (SNR) and an a priori SNR. The gain floors are determined based on the estimated noise level expressed as the dB sound pressure level (SPL). The final gains are smoothed between the frames by a peak detector with carefully selected attack and release time constants. Listening tests show 12% to 45% improvements in intelligibility by this algorithm for noise corrupted speech. A quantified gain table is also used to replace the non-linear gain computing when the algorithm is implemented on the EZAIRO5900 digital signal processor, with the execution cycle reduced by about 30%.
Key words: noise reduction     sub-band     non-linear gains     speech intelligibility

﻿环境噪声是指日常环境中由多种噪声叠加而成的类平稳底噪，一般音量相对较小，声压级(sound pressure level,SPL)小于50 dB，但经过助听器压缩放大后音量会明显提高，严重影响听觉感受，因此必须对环境噪声进行低失真的抑制，以免环境噪声或处理后的失真被放大而影响言语清晰度[1]

1 WOLA框架的子带降噪算法

 图 1 子带降噪算法框图

 $\left| Y\left( (n,k) \right) \right|=\left| S\left( n.k \right) \right|G\left( (n,k) \right),$ (1)

 \begin{align} & 20\lg \left| Y\left( (n,k) \right) \right|= \\ & 20\lg \left| S\left( n.k \right) \right|+\partial \left( (n,k) \right). \\ \end{align} (2)

2 基于非线性扩展的子带自适应降噪算法 2.1 非线性扩展的降噪增益计算方法

 图 2 降噪增益计算的流程图

1) 对输入的子带带噪语音信号进行噪声能量谱估计。为了消除可能被助听器放大的环境背景噪声，所以采用对环境噪声估计效果更好的最小值跟踪方法[2]估计噪声能量谱。

 $\left| S\left( n.k \right) \right|_{\max }^{2}=\underset{n-{{T}_{1}}+1\le m\le n}{\mathop{\max }}\,\left( {{\left| S\left( m,k \right) \right|}^{2}} \right).$ (3)

 ${{{\tilde{P}}}_{\text{N}}}\left( (n,k) \right)=\underset{n-{{T}_{2}}+1\le m{{T}_{1}}\le n}{\mathop{\max }}\,\left( \left| S\left( m{{T}_{1}},k \right) \right|_{\max }^{2} \right).$ (4)