【正文】 特性的估計(jì)、語音激活檢測算法、先驗(yàn)信噪比估計(jì)、增益函數(shù)的修正等，為算法改進(jìn)指明了研究方向。再次，對先驗(yàn)信噪比估計(jì)的反饋因子進(jìn)行最優(yōu)化調(diào)整，引入與長時(shí)統(tǒng)計(jì)信息相關(guān)的自適應(yīng)下限，有效地協(xié)調(diào)了先驗(yàn)信噪比估計(jì)的穩(wěn)定性和快速跟蹤能力。關(guān)鍵詞：語音增強(qiáng) 非平穩(wěn)噪聲 短時(shí)譜幅度估計(jì) 噪聲估計(jì) 語音激活檢測 先驗(yàn)信噪比II Abstract AbstractIn voice munications, speech signals can be contaminated by various noises inevitably, which affects the munication quality. Furthermore, noise interference degrades the performance of speech processing systems, such as low bitrate vocoder and speech recognition. Speech enhancement is an effective preprocessing method to reduce the disturbance of noise.Among the speech enhancement techniques, the methods based on ShortTime Spectral Amplitude(STSA) estimation have been well investigated and brought into wide use. In this dissertation, a lot of research work has been done to improve the performance of STSA estimation against the environments of low SignaltoNoise Ratio(SNR) input and nonstationary noise. The proposed robust approach of speech enhancement has been implemented on the TMS320C55x DSP platform. According to the analysis of the STSAbased speech enhancement algorithm, the key techniques are summarized, including the estimation of noise statistic characteristics, voice activity detection(VAD) algorithm, the estimation of a priori SNR, and the modification of gain function,etc.Firstly, a fast estimation method of noise statistic characteristics is proposed. The method decreases the adaptation time of tracking noise, avoids the overestimation phenomenon to some extent, thus can track the noise characteristics with higher accuracy and less time in low SNR input and nonstationary noise environments.Secondly, a voice activity detector based on Gaussian model and Uniformly Most Powerful(UMP) test is designed. Because of detection threshold related to the noise estimation, the algorithm provides higher detection perfomance, especially in nonstationary noise environments. Furthermore, utilizing the spectral information of DFT in the VAD algorithm, the dual tone multiple frequency(DTMF) signal detector and generator are realized conveniently.Thirdly，to balance the stability and the tracking ability in the estimation of a priori SNR, the feedback factor of a priori SNR estimation is optimally adjusted and the estimation result is limited above an adaptive threshold related to long time statistic information. Moreover, the softdecision modified gain function is obtained by introducing speech presence probability to a priori SNR and a priori SNR. These methods effectively eliminate the“musical” noise and make the enhanced speech smooth, natural and acceptable.Finally, The algorithm mentioned above has been implemented in both C language and assembler language on the PC and TMS320C55 DSP hardware platform. Both simulation experiments and realtime tests show that the improved speech enhancement algorithm is effective in suppressing background noise and increasing the SNR without apparently impairing the intelligibility of speech .The perfomance is significantly enhanced in low SNR input and nonstationary noise environments. Keywords: speech enhancement nonstationary noise STSA noise estimation voice activity detection a priori SNR VI目錄目 錄第1章 引言 1 語音增強(qiáng)課題背景 1 帶噪語音模型 2 語音的主要特性 2 噪聲的主要特性 3 人耳的感知特性 4 背景噪聲對于語音的影響 4 語音增強(qiáng)的發(fā)展歷史 5 語音增強(qiáng)算法分類 5 論文研究工作 7 論文內(nèi)容組織 7 本章小結(jié) 7第2章 基于短時(shí)譜幅度估計(jì)的語音增強(qiáng)算法概述 8 本章引論 8 語音增強(qiáng)算法概述 8 自適應(yīng)噪聲對消法 8 諧波增強(qiáng)法 9 基于語音生成模型的語音增強(qiáng)算法 10 基于短時(shí)譜幅度估計(jì)的語音增強(qiáng)算法 10 其他幾種形式的語音增強(qiáng)算法 11 基于短時(shí)譜幅度估計(jì)的語音增強(qiáng)算法 11 譜減法的一般形式 12 譜減法的改進(jìn)形式 14 維納濾波法 15 MMSE估計(jì)法 16 基于短時(shí)譜幅度估計(jì)的語音增強(qiáng)算法的關(guān)鍵技術(shù) 17 本章小結(jié) 19第3章 噪聲統(tǒng)計(jì)特性估計(jì)的研究 20 本章引論 20 基于語音激活檢測的噪聲統(tǒng)計(jì)特性估計(jì) 20 直接形式的噪聲統(tǒng)計(jì)特性估計(jì) 21 簡單的直接噪聲統(tǒng)計(jì)特性估計(jì) 21 基于最小統(tǒng)計(jì)的噪聲統(tǒng)計(jì)特性估計(jì) 22 噪聲功率譜的快速估計(jì) 25 自適應(yīng)最優(yōu)短時(shí)譜平滑 26 不依賴窗長的最小值搜索 28 引入語音存在概率 28 噪聲功率譜更新 29 語音存在概率的準(zhǔn)確估計(jì)以及噪聲功率譜的迭代更新 29 測試結(jié)果及結(jié)論 30 本章小結(jié) 34第4章 語音激活檢測算法研究 35 本章引論 35 傳統(tǒng)語音激活檢測算法 35 Annex B標(biāo)準(zhǔn)的語音激活檢測算法 35 Annex A標(biāo)準(zhǔn)的語音激活檢測算法 37 GSM標(biāo)準(zhǔn)的語音激活檢測算法 39 基于高斯模型和一致最大勢檢驗(yàn)的語音激活檢測算法 40 基于高斯模型的似然比檢測 41 一致最大勢檢驗(yàn)準(zhǔn)則 41 短時(shí)譜最優(yōu)化平滑以及自適應(yīng)門限平滑 43 拖尾延遲保護(hù) 44 基于高斯模型和UMP檢驗(yàn)的VAD算法流程 45 測試結(jié)果以及結(jié)論 46 雙音多頻信號(hào)的生成與檢測 48 雙音多頻信號(hào)的生成 49 雙音多頻信號(hào)的檢測 50 測試結(jié)果 53 本章小結(jié) 55第5章 語音增強(qiáng)算法的研究與實(shí)現(xiàn) 56 本章引論 56 本文語音增強(qiáng)算法流程 56 分幀和加窗 57 先驗(yàn)/后驗(yàn)信噪比估計(jì) 59 長時(shí)信噪比估計(jì) 61 增益函數(shù)的計(jì)算 61 語音增強(qiáng)算法評測標(biāo)準(zhǔn) 64 增強(qiáng)語音的主觀評測 64 增強(qiáng)語音的客觀評測 65 語音增強(qiáng)算法測試 67 測試環(huán)境 67 測試結(jié)果 67 本章小結(jié) 73第6章 基于TMS320C55x DSP硬件平臺(tái)的實(shí)時(shí)實(shí)現(xiàn) 74 本章引論 74 TMS320C55x DSP的體系結(jié)構(gòu) 74 TMS320C55x內(nèi)部結(jié)構(gòu) 75 TMS320C55x總線結(jié)構(gòu) 76 TMS320C55x流水線操作 77 語音增強(qiáng)算法的DSP實(shí)現(xiàn)和優(yōu)化 78 C代碼定點(diǎn)化 78 TMS320C55x匯編程序編程 79 算法在TMS320C55x DSP上的資源消耗 82 本章小結(jié) 83第7章 結(jié)論與展望 84 結(jié)論 84 展望 85參考文獻(xiàn) 86致 謝 89個(gè)人簡歷、在學(xué)期間發(fā)表的學(xué)術(shù)論文與研究成果 90主要符號(hào)對照表BSD 巴克譜距離（Bark Spectrum Distance）DAM 判斷滿意度測試（Diagnostic Acceptability Measure）DFT 離散傅立葉變換（Discrete Fourier Transform）DRT 診斷押韻測試（Diagnostic Rhyme Test）DSP 數(shù)字信號(hào)處理（Digital Signal Processing）DTFT 序列傅立葉變換（Discrete Time Fourier Transform）DTMF 雙音多頻（Dual Tone Multiple Frequency）FFT 快速傅立葉變換（Fast Fourier Transform）FIR 有限沖激響應(yīng)（Finite Impulse Response）IDFT 逆－離散傅立葉變換（Inverse Discrete Fourier Transform）IFFT 逆－快速傅立葉變換（Inverse Fast Fourier Transform）IIR 無限沖激響應(yīng)（Infinite Impulse Response）KLT 卡－洛變換（Karhunen Loeve Transform）LPC 線形預(yù)測系數(shù)（Linear Prediction Coefficient）LSF 線譜頻率（Line Spectral Frequency）MMSE 最小均方誤差（Minimum MeanSquared Error）MOS 平均意見得分（Mean Opinion Score）NMSE 歸一化均方誤差（Normalized Mean Squared Error）SD 譜失真（Spectrum Distortion）SegSNR 分段式信噪比（Segmental SignaltoNoise Ratio）SNR 信噪比（SignaltoNoise Ratio）SS 譜減法（Spectral Subtraction）STSA 短時(shí)譜幅度（ShortTime Spectral Amplitude）UMP 一致最大勢（Uniformly Most Pow）VAD 語音激活檢測（Voice Activity Detection）VLSI 超大規(guī)模集成電路（Very Large Scale Integration）VII第1章 引言第1章 引言21世紀(jì)的通信是人與人之間、人與機(jī)器之間高質(zhì)量的無縫的信息交換。而語音數(shù)字信號(hào)處理正是其中一項(xiàng)至關(guān)重要的應(yīng)用技術(shù)。語音數(shù)字信號(hào)處理包含的內(nèi)容十分廣泛，如包括語音編碼、語音識(shí)別、語音合成、語音增強(qiáng)等。例如，室內(nèi)會(huì)議電話的交混回響隨同語