【摘要】：語(yǔ)音作為一種方便、快捷、有效的交流方式,在人們的日常生活中扮演著非常重要的角色。隨著社會(huì)科技的不斷進(jìn)步及其人工智能的迅猛發(fā)展,語(yǔ)音信號(hào)也逐漸成為人-機(jī)交互的一種重要方式,其較傳統(tǒng)的人-機(jī)交互方式更加的便捷、高效和安全,故被廣泛應(yīng)用于工業(yè)控制、醫(yī)療輔助、安防保障、智能家居等諸多方面。然而在實(shí)際的應(yīng)用場(chǎng)景中語(yǔ)音信號(hào)不可避免的會(huì)受到周圍環(huán)境噪聲的干擾,進(jìn)而影響語(yǔ)音質(zhì)量,導(dǎo)致其無法完成正常的人-機(jī)交互功能。因此語(yǔ)音增強(qiáng)作為一個(gè)能夠有效抑制噪聲分量,提高語(yǔ)音質(zhì)量的方法,具有重要的研究意義和應(yīng)用價(jià)值。針對(duì)車載環(huán)境這一特定的應(yīng)用場(chǎng)景,噪聲信號(hào)具有低頻分布、先驗(yàn)知識(shí)不易獲得、與語(yǔ)音信號(hào)混合情況復(fù)雜等特點(diǎn),造成了許多語(yǔ)音增強(qiáng)算法并不能很好的適用于車載環(huán)境。因此本文在分析車載噪聲和車載聲學(xué)場(chǎng)景的基礎(chǔ)上,建立噪聲信號(hào)和語(yǔ)音信號(hào)的卷積混合模型,研究盲源分離(Blind Source Separation,BSS)技術(shù)在車載環(huán)境下進(jìn)行語(yǔ)音增強(qiáng)的有效性和可行性,以提高車載環(huán)境下帶噪語(yǔ)音信號(hào)的質(zhì)量和可懂度。本文具體開展了以下的工作:(1)車載聲學(xué)場(chǎng)景分析建模和噪聲估計(jì)算法研究。根據(jù)車載環(huán)境所固有的特點(diǎn),分析車載噪聲的來源及其和駕駛員語(yǔ)音信號(hào)在車內(nèi)的傳播路徑,建立噪聲信號(hào)和語(yǔ)音信號(hào)在車內(nèi)的卷積混合模型。由于多數(shù)語(yǔ)音增強(qiáng)算法都需要噪聲的估計(jì)值作為消噪的先驗(yàn)知識(shí),因此噪聲估計(jì)的準(zhǔn)確性將直接影響這些語(yǔ)音增強(qiáng)算法的性能。本文在歸納總結(jié)一些常用的語(yǔ)音處理理論基礎(chǔ)上,對(duì)現(xiàn)有常用的噪聲估計(jì)算法進(jìn)行了研究,包括語(yǔ)音端點(diǎn)檢測(cè)噪聲估計(jì)算法和最小值控制遞歸平均噪聲估計(jì)算法。(2)語(yǔ)音質(zhì)量評(píng)價(jià)和語(yǔ)音增強(qiáng)算法研究。文章歸納總結(jié)了一些常用的語(yǔ)音信號(hào)質(zhì)量主客觀評(píng)價(jià)標(biāo)準(zhǔn),并分析了這些評(píng)價(jià)標(biāo)準(zhǔn)的優(yōu)缺點(diǎn)。同時(shí)針對(duì)真實(shí)環(huán)境下客觀評(píng)價(jià)標(biāo)準(zhǔn)缺少參考源這一問題,本文構(gòu)建了一個(gè)基于隱馬爾科夫模型(Hidden Markov Model,HMM)的小詞匯量語(yǔ)音識(shí)別引擎,并將語(yǔ)音識(shí)別率納入了無參考源語(yǔ)音質(zhì)量的評(píng)價(jià)體系中。對(duì)于語(yǔ)音增強(qiáng)算法的研究,文章首先實(shí)驗(yàn)性分析了譜減法和維納濾波法這兩個(gè)經(jīng)典的語(yǔ)音增強(qiáng)算法,并給出了它們對(duì)車載帶噪語(yǔ)音信號(hào)的消噪結(jié)果;其次針對(duì)一些傳統(tǒng)語(yǔ)音增強(qiáng)算法的不足,本文提出了一個(gè)改進(jìn)的小波閾值函數(shù)語(yǔ)音增強(qiáng)算法,該算法可有效抑制寬帶噪聲和提高語(yǔ)音質(zhì)量;最后文章闡述了獨(dú)立分量分析(Independent Component Analysis,ICA)的基本理論框架和實(shí)現(xiàn)原理,并重點(diǎn)研究了利用基于負(fù)熵的復(fù)值ICA在頻域盲解卷積,實(shí)現(xiàn)語(yǔ)音增強(qiáng)的過程。該ICA語(yǔ)音增強(qiáng)過程不僅可以較好的契合卷積混合模型,而且可以很好的彌補(bǔ)現(xiàn)有語(yǔ)音增強(qiáng)算法在車載環(huán)境中應(yīng)用的不足。(3)基于卷積ICA的車載語(yǔ)音增強(qiáng)算法研究。文章根據(jù)語(yǔ)音信號(hào)和車載噪聲信號(hào)的卷積混合特性以及它們?cè)陬l域的非高斯分布特性,提出利用基于負(fù)熵極大的卷積ICA對(duì)車載帶噪語(yǔ)音信號(hào)進(jìn)行語(yǔ)音增強(qiáng),并對(duì)該增強(qiáng)過程進(jìn)行針對(duì)性的優(yōu)化。文章在仿真環(huán)境,室內(nèi)環(huán)境,真實(shí)車載環(huán)境三種聲學(xué)場(chǎng)景下構(gòu)建了車載帶噪語(yǔ)音信號(hào)語(yǔ)料庫(kù),并采用基于負(fù)熵的卷積ICA進(jìn)行語(yǔ)音消噪。實(shí)驗(yàn)結(jié)果表明,該卷積ICA消噪后語(yǔ)音信號(hào)的識(shí)別率較車載帶噪語(yǔ)音信號(hào)分別最高提高了18.33%,30%,27.5%,展現(xiàn)出該卷積ICA在車載聲學(xué)場(chǎng)景中應(yīng)用的有效性和魯棒性。最后本文針對(duì)頻域盲解卷積ICA的語(yǔ)音消噪效果受語(yǔ)音信號(hào)分幀長(zhǎng)度和幀移大小影響的問題進(jìn)行了實(shí)驗(yàn)性研究和闡述。(4)復(fù)雜環(huán)境下語(yǔ)音增強(qiáng)系統(tǒng)的研究和實(shí)現(xiàn)。本文在所研究的噪聲估計(jì)算法和語(yǔ)音增強(qiáng)算法基礎(chǔ)上,選擇部分算法結(jié)合語(yǔ)音媒體控制邏輯,在Windows平臺(tái)下利用C++實(shí)現(xiàn)了一套復(fù)雜環(huán)境下的語(yǔ)音增強(qiáng)系統(tǒng)。該系統(tǒng)具有語(yǔ)音波形顯示,頻譜顯示,選擇性語(yǔ)音增強(qiáng)、語(yǔ)音播放保存等功能。測(cè)試結(jié)果表明,該系統(tǒng)不但具有較好的語(yǔ)音增強(qiáng)性能,同時(shí)獲得了較強(qiáng)的可靠性和魯棒性。
[Abstract]:As a convenient, fast and effective way of communication, speech plays a very important role in people's daily life. Along with the progress of social science and technology and the rapid development of artificial intelligence, the voice signal gradually becomes an important way of human-machine interaction. It is more convenient, efficient and safe than the traditional man-machine interactive mode, so it is widely used in industrial control and medical assistance. Security and security, smart home and other aspects. However, in the actual application scene, the voice signal is inevitably disturbed by surrounding environmental noise, and then the voice quality is influenced, and the normal person-machine interaction function can not be completed. Therefore, speech enhancement plays an important role in suppressing noise components and improving the quality of speech. Aiming at this particular application scene of vehicle-mounted environment, the noise signal has low frequency distribution, the prior knowledge is not easy to obtain, and the mixing condition of the voice signal is complicated and the like, so that many voice enhancement algorithms do not apply to the vehicle-mounted environment very well. Therefore, based on the analysis of vehicle-mounted noise and vehicle-mounted acoustic scene, this paper establishes the convolution mixture model of noise signal and speech signal, and researches the validity and feasibility of blind source separation (BSS) technology in vehicle-mounted environment. so as to improve the quality and the intelligibility of the noisy speech signal under the vehicle-mounted environment. In this paper, the following work is carried out: (1) on-board acoustic scene analysis modeling and noise estimation algorithm research. According to the inherent characteristics of vehicle-mounted environment, the source of vehicle-mounted noise and the propagation path of the driver's voice signal in the vehicle are analyzed, and the convolution mixture model of the noise signal and the voice signal in the vehicle is established. Since most speech enhancement algorithms require an estimate of noise as a priori knowledge of noise cancellation, the accuracy of the noise estimation will directly affect the performance of these speech enhancement algorithms. On the basis of summarizing some common speech processing theories, this paper studies the existing commonly used noise estimation algorithms, including the speech endpoint detection noise estimation algorithm and the minimum control recursive average noise estimation algorithm. (2) Speech quality evaluation and speech enhancement algorithm research. The main objective evaluation criteria of speech signal quality are summarized in this paper, and the advantages and disadvantages of these evaluation standards are analyzed. At the same time, we construct a small vocabulary speech recognition engine based on Hidden Markov Model (HMM) based on Hidden Markov Model (HMM) and integrate the speech recognition rate into the evaluation system without reference source speech quality. For the research of speech enhancement algorithm, the paper firstly analyzes the two classical speech enhancement algorithms of spectral subtraction and Wiener filtering, and gives their noise elimination results for vehicle-mounted noisy speech signal; secondly, aiming at the deficiency of some traditional speech enhancement algorithms, This paper presents an improved speech enhancement algorithm for small wave threshold functions, which can effectively suppress wideband noise and improve speech quality. Finally, the basic theoretical framework and implementation principle of Independent Component Analysis (ICA) are described. In this paper, we focus on the process of using complex value ICA based on negative entropy in the frequency domain blind deconvolution to realize the speech enhancement. The ICA speech enhancement process not only can better fit the convolution mixing model, but also can make up the deficiency of the existing speech enhancement algorithm in the vehicle-mounted environment. (3) Research on vehicle-mounted speech enhancement algorithm based on convolution ICA. Based on the convolution mixing characteristics of speech signal and vehicle-mounted noise signal and their non-Gaussian distribution in frequency domain, the speech enhancement of vehicle-mounted noisy speech signal using convolution ICA based on negative entropy is proposed, and the enhancement process is optimized. In this paper, an on-board noisy speech signal corpus was constructed under three acoustic scenes of environment, indoor environment and real vehicle environment, and the speech noise was eliminated by convolution ICA based on negative entropy. The experimental results show that the recognition rate of the speech signal after the convolution ICA is improved by 18. 33%, 30% and 27. 5% respectively, which shows the validity and robustness of the convolution ICA in the vehicle-mounted acoustic scene. In the end, the speech noise elimination effect of blind deconvolution ICA in frequency domain is studied and explained by the influence of frame length and frame shift size of speech signal. (4) Research and implementation of speech enhancement system under complex environment. In this paper, based on the studied noise estimation algorithm and speech enhancement algorithm, a part of the algorithm is selected to combine with the speech media control logic, and the speech enhancement system under a complex environment is realized with C ++ under the Windows platform. The system has the functions of voice waveform display, frequency spectrum display, selective speech enhancement, voice play preservation and the like. The test results show that the system not only has better speech enhancement performance, but also has better reliability and robustness.
【學(xué)位授予單位】：安徽大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN912.3

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