本文選題：信號處理 + 自適應(yīng)學(xué)習(xí)　； 參考：《杭州電子科技大學(xué)》2017年碩士論文

【摘要】：傳統(tǒng)語音信號處理直接利用語音信號的時(shí)域參數(shù)并加以頻譜參數(shù)輔助判決以達(dá)到改善語音質(zhì)量的目的,但是針對實(shí)際情況中噪聲強(qiáng)且非平穩(wěn)的特性,其較弱的魯棒性導(dǎo)致系統(tǒng)性能受信噪比的影響較為敏感。本文首先從低信噪比條件下語音信號檢測的應(yīng)用領(lǐng)域、實(shí)用價(jià)值以及研究現(xiàn)狀等角度對國內(nèi)外相關(guān)文獻(xiàn)進(jìn)行梳理,剖析了強(qiáng)噪聲環(huán)境下語音信號模型的復(fù)雜性,總結(jié)了非平穩(wěn)噪聲環(huán)境中語音信號檢測的關(guān)鍵問題。其次,研究改進(jìn)了基于短時(shí)信噪比的自適應(yīng)閾值和自適應(yīng)判決語音端點(diǎn)檢測算法,根據(jù)自適應(yīng)短時(shí)能量,并加以短時(shí)過零率和自適應(yīng)判決校驗(yàn),得到最終的端點(diǎn)檢測結(jié)果。然后,針對語音信號在低信噪比條件下結(jié)構(gòu)的復(fù)雜性,基于自適應(yīng)學(xué)習(xí)和基本譜減法研究改進(jìn)了一種基于子帶譜熵的語音增強(qiáng)算法,該算法將帶噪語音分成若干個(gè)子帶分別進(jìn)行自適應(yīng)加權(quán),并計(jì)算子帶譜熵值以用來估計(jì)噪聲譜能量。最后,實(shí)驗(yàn)結(jié)果表明,自適應(yīng)閾值端點(diǎn)檢測算法在不同信噪比的平穩(wěn)噪聲和非平穩(wěn)噪聲中均能有效檢測出語音中的有話段和無話段之間的端點(diǎn),且算法準(zhǔn)確性和魯棒性明顯優(yōu)于傳統(tǒng)的端點(diǎn)檢測算法,另外,自適應(yīng)子帶譜熵語音增強(qiáng)算法在不同真實(shí)混合噪聲源分別影響下,同樣能達(dá)到較優(yōu)的性能,且對語音信號質(zhì)量提高效果顯著。
[Abstract]:The traditional speech signal processing directly utilizes the time domain parameters of the speech signal and adjusts the spectrum parameters to improve the speech quality, but in view of the characteristics of strong noise and non-stationary in the actual situation, Because of its weak robustness, the system performance is sensitive to the influence of signal-to-noise ratio (SNR). In this paper, the application field, practical value and research status of speech signal detection under low signal-to-noise ratio (SNR) are firstly analyzed, and the complexity of speech signal model in strong noise environment is analyzed. The key problems of speech signal detection in non-stationary noise environment are summarized. Secondly, the adaptive threshold and adaptive decision speech endpoint detection algorithm based on short time signal-to-noise ratio (SNR) are improved. According to the adaptive short time energy and the short time zero crossing rate and adaptive decision check, the final endpoint detection results are obtained. Then, aiming at the complexity of speech signal structure under low SNR, a speech enhancement algorithm based on sub-band spectral entropy is proposed based on adaptive learning and basic spectral subtraction. The algorithm divides the noisy speech into several sub-bands for adaptive weighting and calculates the entropy value of the sub-band spectrum to estimate the noise spectral energy. Finally, the experimental results show that the adaptive threshold endpoint detection algorithm can effectively detect the endpoint between the speech segment and the non-speech segment in different SNR stationary noise and non-stationary noise. The accuracy and robustness of the algorithm are obviously superior to those of the traditional endpoint detection algorithm. In addition, the adaptive sub-band spectrum entropy speech enhancement algorithm can also achieve better performance under the influence of different real mixed noise sources. And the effect of improving the quality of speech signal is remarkable.
【學(xué)位授予單位】：杭州電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN912.3
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本文編號：2014312