發(fā)布時(shí)間：2018-08-12 15:18

【摘要】：隨著老齡化社會(huì)的到來(lái),老齡聽損患者的數(shù)量持續(xù)增加。聽損對(duì)老年人精神上造成的困擾僅次于位居第一的消化系統(tǒng)疾病,長(zhǎng)期患有老年性聽損會(huì)嚴(yán)重影響老年人的身心健康并降低其生活質(zhì)量。對(duì)于輕度、中度甚至重度感音性聽損患者來(lái)說(shuō),配戴助聽器是最有效的聽力干預(yù)和康復(fù)手段。近一個(gè)世紀(jì)以來(lái),助聽器技術(shù)取得了長(zhǎng)足進(jìn)步,用戶對(duì)助聽器的滿意度從1980年的59.6%上升至2008年的74.0%。但是,現(xiàn)有助聽器的性能因受到嘯叫、信號(hào)畸變以及噪聲的影響而難以提高。部分患者已經(jīng)驗(yàn)配了助聽器,但是沒(méi)有佩戴,其中一個(gè)重要的原因就是助聽器不能提供足夠的益處。因此,面向聽損患者的數(shù)字助聽器技術(shù)亟需進(jìn)一步研究。本文從響度補(bǔ)償、自適應(yīng)聲反饋抑制、非線性頻率壓縮算法和語(yǔ)音可懂度增強(qiáng)等方面對(duì)漢語(yǔ)數(shù)字助聽器的幾種關(guān)鍵技術(shù)進(jìn)行了研究,主要研究?jī)?nèi)容如下:1、論文提出了一種自適應(yīng)響度補(bǔ)償方法,用于緩解傳統(tǒng)的響度補(bǔ)償算法中信號(hào)可聽和畸變之間的矛盾。首先,利用gammatone濾波器組對(duì)信號(hào)進(jìn)行多通道分解來(lái)模擬人耳聽覺(jué)特性;然后,根據(jù)患者的聽覺(jué)動(dòng)態(tài)范圍和通道信號(hào)的聲壓級(jí)來(lái)確定補(bǔ)償方法。若采用非線性補(bǔ)償方法,則在補(bǔ)償過(guò)程中使用自適應(yīng)寬動(dòng)態(tài)范圍壓縮(Wide Dynamic Range Compression,WDRC)技術(shù),該技術(shù)根據(jù)患者的聽覺(jué)范圍和輸出信號(hào)的聲壓級(jí)自適應(yīng)地調(diào)整壓縮比,使其盡量接近于1。自適應(yīng)調(diào)整壓縮比的目的是在信號(hào)可聽的前提下,減小信號(hào)的畸變。并且,自適應(yīng)WDRC技術(shù)可以提高噪聲環(huán)境下的輸出信噪比(Signalto Noise Ratio,SNR)。實(shí)驗(yàn)結(jié)果表明,與WDRC補(bǔ)償方法相比,所提方法在語(yǔ)噪聲環(huán)境下提高了輸出SNR;與線性和WDRC補(bǔ)償方法相比,提出的方法提高了語(yǔ)音可懂度和語(yǔ)音質(zhì)量。2、論文提出了一種應(yīng)用于助聽器聲反饋抑制的變步長(zhǎng)算法,用于緩解自適應(yīng)濾波算法在快收斂速度和低失調(diào)量之間的矛盾。算法根據(jù)自適應(yīng)濾波器系數(shù)歸一化的短時(shí)平均和長(zhǎng)時(shí)平均之間的距離,將濾波器的更新狀態(tài)分為收斂狀態(tài)、過(guò)渡狀態(tài)和穩(wěn)定狀態(tài),并在不同的自適應(yīng)狀態(tài)下使用不同的步長(zhǎng)。在收斂狀態(tài)使用大步長(zhǎng)保證收斂速度;在過(guò)渡狀態(tài)使用階梯狀步長(zhǎng),以進(jìn)一步降低失調(diào)量;在穩(wěn)定狀態(tài)使用小步長(zhǎng),以保證系統(tǒng)收斂至穩(wěn)態(tài)解。該變步長(zhǎng)算法分別被應(yīng)用于歸一化的最小均方誤差(Normalized Least Mean Error,NLMS)自適應(yīng)濾波器和歸一化的子帶自適應(yīng)濾波器(Normalized Sub-band Adaptive Filter,NSAF)算法,形成了變步長(zhǎng) NLMS(Variable Step Size NLMS,VSS-NLMS)和變步長(zhǎng)NSAF(Variable Step Siz eNSAF,VSS-NSAF)算法。實(shí)驗(yàn)結(jié)果表明,本文提出的算法具有收斂速度快,穩(wěn)態(tài)失調(diào)量低,輸出語(yǔ)音的語(yǔ)音感知質(zhì)量(Perceptual Evaluation of Speech Quality,PESQ)評(píng)價(jià)值高等特點(diǎn)。3、論文提出了一種自適應(yīng)非線性頻率壓縮算法,以充分利用聽損患者殘余的可聽頻帶,提高高頻聽損患者的語(yǔ)音可懂度。首先,對(duì)高于截止頻率的信號(hào)按照BARK尺度進(jìn)行臨界帶分割;其次,根據(jù)患者的截止頻率和最大可聽頻率確定全局壓縮比;再次,根據(jù)全局壓縮比和子帶信號(hào)歸一化的平均能量自適應(yīng)地確定子帶壓縮比,從而確定子帶信號(hào)壓縮后的頻帶范圍;最后,通過(guò)映射的方法將高頻信號(hào)壓縮至低頻,使得高頻不可聽的信號(hào)變得可聽。語(yǔ)音可懂度實(shí)驗(yàn)結(jié)果表明,與WDRC算法和非線性頻率壓縮算法相比,本文提出的算法可顯著提高聲母以及不同信噪比條件下句子的可懂度。4、論文提出了一種新的語(yǔ)音特征——多分辨率功率歸一化倒譜系數(shù)(Multi-Resolution Power Normalized Cepstram Coefficients,MRPNCC)特征來(lái)獲取語(yǔ)音的局部信息和上下文信息。并且,基于MRPNCC特征,提出了一種語(yǔ)音可懂度增強(qiáng)算法,以提高聽損患者的語(yǔ)音可懂度。首先,利用MRPNCC特征向量訓(xùn)練支撐矢量機(jī)(Support Vector Machine,SVM)模型;其次,利用訓(xùn)練好的模型對(duì)語(yǔ)音的時(shí)頻單元進(jìn)行分類,移除以噪聲為主導(dǎo)的時(shí)頻單元,保留以語(yǔ)音為主導(dǎo)的時(shí)頻單元,并使用維納濾波器對(duì)其進(jìn)行去噪;最后,將濾波后的時(shí)頻信號(hào)重構(gòu)得到增強(qiáng)的語(yǔ)音。實(shí)驗(yàn)結(jié)果表明,MRPNCC特征優(yōu)于傳統(tǒng)的譜特征,而且語(yǔ)音可懂度實(shí)驗(yàn)結(jié)果表明,所提出的語(yǔ)音可懂度增強(qiáng)算法可以顯著提高聽損患者的語(yǔ)音可懂度。
[Abstract]:With the advent of an aging society, the number of elderly patients with hearing loss continues to increase. The mental distress of the elderly is second only to that of the digestive system. Chronic suffering from hearing loss in the elderly will seriously affect their physical and mental health and reduce their quality of life. For mild, moderate and even severe sensorineural hearing loss patients Hearing aids are the most effective means of hearing intervention and rehabilitation. In the past century, the technology of hearing aids has made great progress. The satisfaction of users with hearing aids has increased from 59.6% in 1980 to 74.0% in 2008. However, the performance of existing hearing aids is difficult to improve due to the effects of howling, signal distortion and noise. Hearing AIDS have been tested but not worn. One of the important reasons is that they do not provide sufficient benefits. Therefore, digital hearing aids for hearing impaired patients need to be further studied. Several key techniques of Chinese digital hearing aids are studied. The main research contents are as follows: 1. An adaptive loudness compensation method is proposed to alleviate the contradiction between the signal audibility and distortion in the traditional loudness compensation algorithm. Firstly, the Gammatone filter banks are used to decompose the signals into multi-channels to simulate human hearing. The compensation method is then determined according to the patient's auditory dynamic range and the sound pressure level of the channel signal. If the nonlinear compensation method is used, the adaptive wide dynamic range compression (WDRC) technique is used in the compensation process, which is based on the patient's auditory range and the sound pressure level of the output signal. Adaptive compression ratio can be adjusted as close as possible to 1. The purpose of adaptive compression ratio is to reduce signal distortion while the signal is audible. Moreover, adaptive WDRC technology can improve the output signal to Noise Ratio (SNR) in noisy environments. Experimental results show that the proposed method is more effective than WDRC compensation method in terms of noise. Compared with linear and WDRC compensation methods, the proposed method improves speech intelligibility and speech quality. 2. A variable step size algorithm for hearing aids acoustic feedback suppression is proposed to alleviate the contradiction between fast convergence speed and low offset. The distance between the normalized short-term average and the long-term average of the filter coefficients divides the update state of the filter into convergence state, transition state and stable state, and uses different step sizes in different adaptive states. The variable step size algorithm is applied to the normalized Least Mean Error (NLMS) adaptive filter and the normalized subband adaptive filter (NSAF) algorithm respectively to form the variable step size algorithm. Variable Step Size NLMS (VSS-NLMS) and Variable Step Size NSAF (VSS-NSAF) algorithms are proposed. Experimental results show that the proposed algorithm has the advantages of fast convergence, low steady-state offset, and high Perceptual Evaluation of Speech Quality (PESQ) of output speech. An adaptive nonlinear frequency compression algorithm is proposed to make full use of the residual audible frequency band of the patients with hearing loss and improve the speech intelligibility of the patients with high frequency hearing loss. According to the global compression ratio and the normalized average energy of the sub-band signal, the sub-band compression ratio is adaptively determined to determine the frequency range of the compressed sub-band signal. Finally, the high-frequency signal is compressed to low-frequency by mapping method, which makes the high-frequency non-audible signal audible. Compared with the linear frequency compression algorithm, the proposed algorithm can significantly improve the intelligibility of consonants and sentences under different SNR conditions. 4. A new speech feature, multi-resolution power Normalized Cepstram Coefficients (MRPNCC), is proposed to obtain speech localization. Furthermore, a speech intelligibility enhancement algorithm based on MRPNCC features is proposed to improve the speech intelligibility of hearing impaired patients. Firstly, the support vector machine (SVM) model is trained by using MRPNCC feature vectors; secondly, the time-frequency units of speech are classified and removed by using the trained model. The noise-dominated time-frequency unit retains the speech-dominated time-frequency unit and uses Wiener filter to denoise it. Finally, the filtered time-frequency signal is reconstructed to enhance the speech. The experimental results show that the MRPNCC features are superior to the traditional spectral features, and the speech intelligibility experimental results show that the proposed speech is intelligible. The degree enhancement algorithm can significantly improve the speech intelligibility of hearing impaired patients.
【學(xué)位授予單位】：東南大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：R764.5;TN912.3

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