语音增强相关问题研究
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摘要
在语音通信中,纯净语音的编解码技术、传输技术和识别技术都已经非常发达,但在背景噪声和信道噪声存在的情况下,信号处理系统的性能都会急剧下降,并最终严重影响语音信号质量。尽管在该方面的研究已经进行了多年,在稳态的噪声环境或随时间变化缓慢的噪声环境中取得了一些成果,但是在随时间变化极快的复杂的非平稳噪声环境下,现有的技术还存在很多缺陷,经过去噪处理的语音信号的质量和可懂度都会受到严重影响,与纯净语音在信号处理系统中的性能相差甚远。针对上述问题,本文重点关注语音的前端处理过程,对语音增强及相关问题信息研究,具体的工作及创新包括以下方面：
1.对已有的语音增强算法进行详细讨论,并根据接收端所使用的麦克风数目,将语音增强算法分为单通道语音增强算法和多通道语音增强算法。对其进行详细介绍和研究,并以此作为进行深入研究的理论基础。
2.先验信噪比是语音增强算法中的重要参数,先验信噪比估计对语音增强系统的性能起决定性作用。为了解决现有先验信噪比算法中存在的时延问题和平滑因子不能与噪声环境变化自适应的问题,进而导致增强后语音具有混响或失真,使得语音质量、清晰度与可懂度受损的情况,提出了基于后验补偿的先验信噪比估计算法。该算法综合考虑了预估的先验信噪比和后验信噪比对信噪比估计的影响,弥补了后验信噪比估计具有抖动的不足和先验信噪比估计具有时延的不足；同时,联合考虑帧间相关性和频率点间相关性,针对不同的帧计算相应的平滑因子,进而对带噪语音进行不同程度的平滑处理,使得新的先验信噪比算法能够与噪声环境自适应；最后将更新后的先验信噪比用于语音增强系统中,并对其性能进行仿真。本论文提出的先验信噪比估计算法可以广泛用于基于短时谱估计的语音增强系统中,处理后的语音质量、清晰度与可懂度均有所提高,且更加适用于非平稳的噪声环境。
3.频域内语音增强算法因其计算复杂度低,是目前最为常用的算法。针对在现有的频域算法中,由于频域内语音和噪声信息混合在一起,不能以此有效判断幅度的变化来自于语音或者噪声,从而使频域内的平滑算法容易因误判而产生短时的频谱尖峰,由此导致音乐噪声和语音失真等缺点,提出了基于倒谱平滑的语音增强算法。该算法针对信号在倒谱域内的特点,将信号由频域变换至倒谱域中,分别对信号的包络、细节特征和噪声进行不同程度的平滑,在尽可能消除频谱尖峰、保护语音起始段信息和低能量语音信号的基础上对噪声进行抑制。所提的算法可广泛用于现有的频域内语音增强算法和先验信噪比估计算法中,可有效抑制音乐噪声,保护语音的特性,提高语音增强系统的性能,解决现有算法在低信噪比和非平稳噪声环境中性能恶化的问题。
4.语音激活检测技术是语音增强系统的第一步,也是其重要组成部分,语音激活检测技术的准确度直接影响了语音增强系统的性能。本文在对语音激活检测算法进行分类、研究和比较的基础上,探讨传统基于直接判决似然比测试的语音激活检测算法存在的问题。针对采用了固定判决门限而使得这类算法不能很好跟踪噪声变化情况,当受噪声影响比较严重时或者在非平稳噪声环境中,容易导致误判的问题进行改进,提出了基于信噪比自适应的似然比测试的语音激活检测算法。该算法分别根据每一帧中噪声成分的多少设定相应的判决门限,进而进行判决。实验证明,本文算法适用于现有基于似然比测试语音激活检测算法,能够提高语音激活检测算法性能,有效解决了已有基于似然比测试语音激活检测算法容易在非平稳环境下产生的误判问题。
Encoding and decoding, transmission and speech recognition technologies of pure speech have been well developed in the speech communication system. However, in the circumstances with background noise and channel noise, the performance of signal process system will degrade dramatically, and then impact on speech signal quality. The research on the speech enhancement has lasted for many years, and there are some achievements on better performance in the case of stationary and slow time-varying noise environment. However, in the complicated environment with fast time-varying and non-stationary noise environment, there are many deficiencies by using the existing noise reduction technologies, and it may cause big influence on the speech quality and intelligibility. The performance of enhanced speech in the signal processing system is worse than that of pure speech. Based on the above issues, this thesis mainly focuses on the front-end process of speech in the speech enhancement and related issues.
1. Based on the discussion of speech enhancement algorithms, we summarized and classified them into two types, single-channel speech enhancement algorithm and multi-channel speech enhancement algorithm. These two algorithms are described in this thesis, and they are the basic theory of further research.
2. The estimation of the a priori signal-to-noise (SNR) is a crucial part of speech enhancement algorithms. In order to solve the delay issue in the existing a priori SNR estimation algorithms and trace the speech signal in the noise environment, a new a priori SNR estimation algorithm is proposed. It takes the influence of both a priori SNR and posterior SNR to calculate smoothing factor, and can solve the problem of jitter caused by posterior SNR and the problem of delay caused by a priori SNR. The correlation of inter-frame and intra-frame is also considered in the proposed algorithm. Smoothing factor is calculated each frame, and make the noisy speech be handled with different smoothing. At last, the updated a priori SNR is applied to the speech enhancement system, and simulation is made to evaluate the performance in the modified speech enhancement system. The simulation results show that the proposed algorithm improved the performance of speech enhancement system and is better in the non-stationary noise environment. The proposed algorithm can be widely used in the speech enhancement system based on short-time spectrum estimation.
3. The frequency domain speech enhancement algorithms is one of the most widely used algorithms. The existing frequency domain speech enhancement algorithms do not consider the speech signal, and lead to short-time spectral peaks, which is caused by the smoothing algorithm in frequency domain. The proposed algorithm transforms the related parameters of frequency domain speech enhancement algorithm to the cepstral domain first. Then make different smoothing to the envelope, fine characteristic and noise in order to restrain the spectral peaks, make compensation on the frequency domain algorithm and then restrain the noise while protecting the speech characteristics. The proposed algorithm can be widely applied to the existing frequency domain speech enhancement algorithms and estimation of the a priori SNR, and can effectively decrease the musical noise and improve the performance of speech enhancement system.
4. Voice activity detection (VAD) is an important enabling technology for a variety of speech-based applications. Considering that VAD based on direct-decision likelihood test cannot well catch the variation of noise, that results in error decisions, we propose a new VAD algorithm based on the adaptive threshold likelihood ratio test. In the proposed algorithm, different decision thresholds are set according to the SNR each frame; then decisions are made using the adaptive threshold. Simulation results show that the proposed algorithm improved the performance of VAD algorithm, and solved the problem of error decisions which exist in the VAD based on direct-decision likelihood test.
1.对已有的语音增强算法进行详细讨论,并根据接收端所使用的麦克风数目,将语音增强算法分为单通道语音增强算法和多通道语音增强算法。对其进行详细介绍和研究,并以此作为进行深入研究的理论基础。
2.先验信噪比是语音增强算法中的重要参数,先验信噪比估计对语音增强系统的性能起决定性作用。为了解决现有先验信噪比算法中存在的时延问题和平滑因子不能与噪声环境变化自适应的问题,进而导致增强后语音具有混响或失真,使得语音质量、清晰度与可懂度受损的情况,提出了基于后验补偿的先验信噪比估计算法。该算法综合考虑了预估的先验信噪比和后验信噪比对信噪比估计的影响,弥补了后验信噪比估计具有抖动的不足和先验信噪比估计具有时延的不足；同时,联合考虑帧间相关性和频率点间相关性,针对不同的帧计算相应的平滑因子,进而对带噪语音进行不同程度的平滑处理,使得新的先验信噪比算法能够与噪声环境自适应；最后将更新后的先验信噪比用于语音增强系统中,并对其性能进行仿真。本论文提出的先验信噪比估计算法可以广泛用于基于短时谱估计的语音增强系统中,处理后的语音质量、清晰度与可懂度均有所提高,且更加适用于非平稳的噪声环境。
3.频域内语音增强算法因其计算复杂度低,是目前最为常用的算法。针对在现有的频域算法中,由于频域内语音和噪声信息混合在一起,不能以此有效判断幅度的变化来自于语音或者噪声,从而使频域内的平滑算法容易因误判而产生短时的频谱尖峰,由此导致音乐噪声和语音失真等缺点,提出了基于倒谱平滑的语音增强算法。该算法针对信号在倒谱域内的特点,将信号由频域变换至倒谱域中,分别对信号的包络、细节特征和噪声进行不同程度的平滑,在尽可能消除频谱尖峰、保护语音起始段信息和低能量语音信号的基础上对噪声进行抑制。所提的算法可广泛用于现有的频域内语音增强算法和先验信噪比估计算法中,可有效抑制音乐噪声,保护语音的特性,提高语音增强系统的性能,解决现有算法在低信噪比和非平稳噪声环境中性能恶化的问题。
4.语音激活检测技术是语音增强系统的第一步,也是其重要组成部分,语音激活检测技术的准确度直接影响了语音增强系统的性能。本文在对语音激活检测算法进行分类、研究和比较的基础上,探讨传统基于直接判决似然比测试的语音激活检测算法存在的问题。针对采用了固定判决门限而使得这类算法不能很好跟踪噪声变化情况,当受噪声影响比较严重时或者在非平稳噪声环境中,容易导致误判的问题进行改进,提出了基于信噪比自适应的似然比测试的语音激活检测算法。该算法分别根据每一帧中噪声成分的多少设定相应的判决门限,进而进行判决。实验证明,本文算法适用于现有基于似然比测试语音激活检测算法,能够提高语音激活检测算法性能,有效解决了已有基于似然比测试语音激活检测算法容易在非平稳环境下产生的误判问题。
Encoding and decoding, transmission and speech recognition technologies of pure speech have been well developed in the speech communication system. However, in the circumstances with background noise and channel noise, the performance of signal process system will degrade dramatically, and then impact on speech signal quality. The research on the speech enhancement has lasted for many years, and there are some achievements on better performance in the case of stationary and slow time-varying noise environment. However, in the complicated environment with fast time-varying and non-stationary noise environment, there are many deficiencies by using the existing noise reduction technologies, and it may cause big influence on the speech quality and intelligibility. The performance of enhanced speech in the signal processing system is worse than that of pure speech. Based on the above issues, this thesis mainly focuses on the front-end process of speech in the speech enhancement and related issues.
1. Based on the discussion of speech enhancement algorithms, we summarized and classified them into two types, single-channel speech enhancement algorithm and multi-channel speech enhancement algorithm. These two algorithms are described in this thesis, and they are the basic theory of further research.
2. The estimation of the a priori signal-to-noise (SNR) is a crucial part of speech enhancement algorithms. In order to solve the delay issue in the existing a priori SNR estimation algorithms and trace the speech signal in the noise environment, a new a priori SNR estimation algorithm is proposed. It takes the influence of both a priori SNR and posterior SNR to calculate smoothing factor, and can solve the problem of jitter caused by posterior SNR and the problem of delay caused by a priori SNR. The correlation of inter-frame and intra-frame is also considered in the proposed algorithm. Smoothing factor is calculated each frame, and make the noisy speech be handled with different smoothing. At last, the updated a priori SNR is applied to the speech enhancement system, and simulation is made to evaluate the performance in the modified speech enhancement system. The simulation results show that the proposed algorithm improved the performance of speech enhancement system and is better in the non-stationary noise environment. The proposed algorithm can be widely used in the speech enhancement system based on short-time spectrum estimation.
3. The frequency domain speech enhancement algorithms is one of the most widely used algorithms. The existing frequency domain speech enhancement algorithms do not consider the speech signal, and lead to short-time spectral peaks, which is caused by the smoothing algorithm in frequency domain. The proposed algorithm transforms the related parameters of frequency domain speech enhancement algorithm to the cepstral domain first. Then make different smoothing to the envelope, fine characteristic and noise in order to restrain the spectral peaks, make compensation on the frequency domain algorithm and then restrain the noise while protecting the speech characteristics. The proposed algorithm can be widely applied to the existing frequency domain speech enhancement algorithms and estimation of the a priori SNR, and can effectively decrease the musical noise and improve the performance of speech enhancement system.
4. Voice activity detection (VAD) is an important enabling technology for a variety of speech-based applications. Considering that VAD based on direct-decision likelihood test cannot well catch the variation of noise, that results in error decisions, we propose a new VAD algorithm based on the adaptive threshold likelihood ratio test. In the proposed algorithm, different decision thresholds are set according to the SNR each frame; then decisions are made using the adaptive threshold. Simulation results show that the proposed algorithm improved the performance of VAD algorithm, and solved the problem of error decisions which exist in the VAD based on direct-decision likelihood test.
引文
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[6]Ephraim Y, Malah D. Speech enhancement using a minimum mean-square error short-time spectral amplitude estimator. IEEE Transactions on acoustics, Speech, and Signal Processing,1984,32(6):1109-1121
[7]Ephraim Y, Malah D. Speech enhancement using a minimum mean-square error log-spectral amplitude estimator. IEEE Transactions on acoustics, Speech, and Signal Processing,1985,33(2):443-445
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[9]Ephraim Y. A Bayesian estimation approach for speech enhancement using hidden Markov models. IEEE Transactions on Signal Processing,1992,40(4): 725-735
[10]Wolfe P J, Godsill S J. Efficient alternatives to the Ephraim and Malah suppression rule for audio signal enhancement. EURASIP Journal on Applied Signal Processing,2003,2003(10):1043-1051
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