博碩士論文 106521063 詳細資訊




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姓名 劉家興(Chia-Hsing Liu)  查詢紙本館藏   畢業系所 電機工程學系
論文名稱 以麥克風陣列技術發展項鍊式助聽器
(Development of necklace-type hearing aid with microphone array technology)
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2022-10-30以後開放)
摘要(中) 本論文目的是以麥克風陣列的相關技術發展項鍊式助聽器。項鍊式助聽器因穿戴於胸前,所以助聽器可以有較大的體積,方便手指不靈活的長者使用。此項鍊式助聽器系統聲音訊號處理依序可以分為三個階段,分別為麥克風陣列除噪、後濾波 (語音增強)、語音放大與壓縮。本研究分為兩個部分進行,第一個部分為軟體模擬,利用MATLAB模擬各階段的演算法,其中在麥克風陣列除噪階段使用四種不同的自適應方法,選擇除噪效果最優的方法,關閉其語音活動檢測 (Voice activity detection, VAD) 功能與減少自適應濾波器階數,觀察除噪效果的變化,並且結合後濾波,以選擇效果和運算量都適合實現於硬體中的除噪策略;第二個部分為硬體實現,把軟體模擬所決定的除噪策略實現於硬體TMS320C6713開發板中,實際測試除噪策略在硬體運作效果,之後與語音放大壓縮策略整合,並給予六位聽力正常的受測者實際聆聽測試,以檢驗助聽器系統輸出的聲音在實際人耳中的情況,測試結果在SNR為-5dB到20dB的環境下句子平均答對字數都在9個字以上 (滿分為10個字),主觀評分MOS (Mean opinion score) 的分數也都依序落於3.5到4.6之間 (滿分為5分),皆有不錯的表現。另外,本研究開發了一個簡易APP (application),並且把藍牙通訊模組整合入項鍊式助聽器中,使Android裝置可以透過藍牙無線連接更改助聽器中各個頻帶的增益值。經過上述各個研究階段後,完成項鍊式助聽器的原型。
摘要(英) The purpose of this study is to develop a necklace-type hearing aid with related technologies of microphone array. Because the necklace-type hearing aid is worn over the chest, it can be larger in size, which is more convenient for the elderly to use. The signal processing of the proposed necklace-type hearing aid system includes three stages: microphone array noise cancellation, post-filter (speech enhancement), and amplification and compression for speech. This research was divided into two parts. The first part was software simulation, which used MATLAB to implement the algorithms of the three signal processing stages and to verify the actual effects of each stages algorithm. Four different adaptive methods were used in the stage of microphone array noise cancellation, and the method with best performance was selected and combined with a post-filter. Then we observed the result when its voice activity detection (VAD) function was switched off and the number of taps in the adaptive filter was reduced. Finally, the strategy of noise cancellation, which was the combination of the microphone array noise cancellation stage and the post-filter stage, was decided according to the performance and computational complexity suitable for hardware implement. The other part was hardware implementation. The strategy of the best noise cancellation performance determined by simulation was implemented in the hardware TMS320C6713 DSK to verify the performance of noise cancellation in reality. After that, the strategy of amplification and compression for speech is integrated into the hardware with the noise cancellation strategy. Six normal hearing subjects participated the listening test. The test results showed that the average number of correct words in the sentence test was more than 9 words out of 10 words and the score of the subjective MOS (Mean opinion score), which listeners rate between 1.0 and 5.0, were between 3.5 and 4.6 in the environment with SNR from -5dB to 20dB. In addition, a simple Android APP (application) was developed in this research and the Bluetooth communication module was added to the necklace-type hearing aid so that the gain value of frequency bands in the hearing aid can be adjusted by the Android device via Bluetooth wireless connection. After the previous research stages, the prototype of the necklace hearing aid was completed.
關鍵字(中) ★ 麥克風陣列
★ 語音增強
★ 項鍊式助聽器
★ APP
關鍵字(英) ★ microphone array
★ speech enhancement
★ necklace-type hearing aid
★ APP
論文目次 摘要 i
Abstract ii
誌謝 iv
目錄 v
圖目錄 viii
表目錄 xii
第一章 緒論 1
1.1研究動機 1
1.2助聽器介紹 2
1.3麥克風陣列介紹 5
1.4相關研究與文獻探討 5
1.4.1 麥克風陣列 5
1.4.2 後濾波 9
1.4.3 語音放大補償 12
1.4.4 助聽器的相關發展 14
1.5論文架構 16
第二章 噪音消除與語音放大壓縮策略 17
2.1 麥克風陣列除噪 17
2.1.1 自適應性訊號處理 18
2.1.2 自適應性演算法 19
2.1.3 語音活動檢測 24
2.2 後濾波 26
2.3 語音放大壓縮 29
第三章 軟體模擬 33
3.1 實驗語料與噪音 33
3.2 客觀評估標準 34
3.3 模擬實驗設計 36
3.3.1 實驗一: 麥克風陣列GSC架構除噪模擬 36
3.3.2 實驗二: 後濾波除噪模擬 38
3.3.3 實驗三: 語音放大壓縮 38
3.4 模擬實驗結果與討論 39
3.4.1 實驗一結果與討論 39
3.4.2 實驗二結果與討論 49
3.4.3 實驗三結果與討論 52
第四章 硬體實現 55
4.1 TMS320C6713開發版與麥克風電路 55
4.2 硬體實驗設計 57
4.2.1 實驗一: 硬體實現除噪策略 57
4.2.2 實驗二: 受測者實測 58
4.3 硬體實驗結果與討論 62
4.3.1 實驗一結果與討論 62
4.3.2 實驗二結果與討論 67
4.4 助聽器成品與介紹 71
4.4.1 助聽器成品 71
4.4.2 助聽器增益調整APP (application) 73
第五章 結論與未來展望 75
5.1結論 75
5.2未來展望 76
參考文獻 78
附錄一 84
附錄二 87
參考文獻 ANSI (2003). “ANSI S3.22-2003: Specification of Hearing Aid Characteristics”.
Byrne, D. and Tonnison, Wm. (1976). “Selecting the gain in hearing aids for persons with sensorineural hearing impairments,” Scandinavian Audiology, 5(2), 51-59.
Boll, S. F. (1979). “Suppression of acoustic noise in speech using spectral subtraction,” IEEE Transactions on Acoustics, Speech and Signal Processing, 27(2), 113-120.
Byrne, D. and Dillon, H. (1986). “The National Acoustic Laboratories’(NAL) New Procedure for Selecting the Gain and Frequency Response of a Hearing Aid,” Ear and Hearing, 7(4), 257-265.
Byrne, D., Parkinson, A., Newall, P. (1990). “Hearing aid gain and frequency response requirements for the severely/profoundly hearing impaired,” Ear Hear, 11(1), 40-49.
Berghe, J. V. and Wouters, J. (1998). “An adaptive noise canceller for hearing aids using two nearby microphones,” The Journal of the Acoustical Society of America, 103(6), 3621-3626.
Compernolle, D. V. (1990). “Switching adaptive filters for enhancing noisy and reverberant speech from microphone array recordings,” International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Albuquerque, New Mexico, USA. 3-6 April.
Cornelisse, L. E., Seewald, R. C., and Jamieson, D. G. (1995). “The input/output formula: A theoretical approach to the fitting of personal amplification devices,” The Journal of the Acoustical Society of America, 97(3), 1854-1864.
Cohen, I., Berdugo, B. (2002). “Noise estimation by minima controlled recursive averaging for robust speech enhancement,” IEEE Signal Processing Letters, 9(1), 12-15.
Cohen, I. (2003). “Noise spectrum estimation in adverse environments: improved minima controlled recursive averaging,” IEEE Transactions on Speech and Audio Processing, 11(5), 466-475.
Ching, T. Y. C., Scollie, S. D., Dillon, H., and Seewald, R. (2010). “A cross-over, double-blind comparison of the NAL-NL1 and the DSL v4.1 prescriptions for children with mild to moderately severe hearing loss,” International Journal of Audiology, 49, S4-S15.
Doblinger, G. (1995). “Computationally efficient speech enhancement by spectral minima tracking in subbands,” Proc. Euro-Speech, 2, 1513-1516.
Frost, O.L. (1972). “An algorithm for linearly constrained adaptive array processing,” Proceedings of the IEEE, 60(8), 926-935.
Griffiths, L. and C. Jim (1982). “An alternative approach to linearly constrained adaptive beamforming,” IEEE Transactions on Antennas and Propagation, 30(1), 27- 34.
Hu, Y., Loizou, P. C. (2004). “Speech enhancement based on wavelet thresholding the multitaper spectrum,” IEEE Transactions on Speech and Audio Processing, 12(1), 59-67.
Hamacher, V., Chalupper, J., Eggers, J., Fischer, E., Kornagel, U., Puder, H. and Rass, U. (2005). “Signal processing in high-end hearing aids: state of the art, challenges, and future trends,” EURASIP Journal on Applied Signal Processing, 2005(18), 2915-2929.
Mccandless, G. A. and Lyregaard, P. E. (1983). “Prescription of gain/output (POGO) for hearing aids,” Hearing Instruments, 34(1), 16-21.
Martin, R. (2001). “Noise power spectral density estimation based on optimal smoothing and minimum statistics,” IEEE Transactions on Speech and Audio Processing, 9(5), 504-512.
Park, Jeong-Sik, Yoon, Jung-Seok, Seo, Yong-Ho, Jang, Gil-Jin. (2017). “Spectral energy based voice activity detection for real-time voice interface,” Journal of Theoretical and Applied Information Technology, 95(17), 4304-4312.
Rangachari, S., and Loizou, P. C. (2006). “A noise-estimation alogorithm for highly non-stationary environments,” Speech Communication, 48, 220-231.
Schwartz, D., Lyregaard, P. E., and Lundh, P. (1988). “Hearing aid selection for severe/profound hearing losses,” Hearing Journal, 41(2), 13-17.
Scollie, S., Seewald, R., Cornelisse, L., Moodie, S., Bagatto, M., Laurnagaray, D., Beaulac, S., and Pumford, J. (2005). “The Desired Sensation Level Multistage Input/Output Algorithm,” Trends in Amplification, 9(4), 159-197.
TI (2003). “TMS320C6713 DSK Technical Reference, 506735-0001 Rev. B.”
Taal, C. H., Hendriks, R. C., Heusdens, R., and Jensen, J. (2011). “An Algorithm for Intelligibility Prediction of Time–Frequency Weighted Noisy Speech,” IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, 19(7), 2125-2136.
Widrow, B. and M.E. Hoff, Jr. (1960). “Adaptive Switching Circuits,” IRE WESCON Convention Record, 4, 96-104.
Widrow, B. (2000). “A microphone array for hearing aids,” Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373), Lake Louise, Alberta, Canada, Canada, 4-4 Oct.
Wilcoxon, F. and Wilcox, R. A. (1964), Some Rapid Approximate Statistical Procedure, American Cyanamid Company, N.Y.
Yoganathan, V. and Moir, T. J. (2010). “Speech enhancement using microphone array neural switched Griffiths-Jim beamformer,” 2010 International Conference on Wireless Communications & Signal Processing (WCSP), Suzhou, China, 21-23 Oct.

中華民國內政部戶政司 (2018)。2019年3月1日,取自https://www.moi.gov.tw/chi/chi_news/news_detail.aspx?type_code=02&sn=13723

中華民國衛生福利部統計處 (2018)。2019年3月1日,取自https://dep.mohw.gov.tw/DOS/lp-2976-113.html

徐葳倫 (2016),銀髮福音App 手機藍牙變助聽器 《聯合新聞網 元氣網》。2019年6月17日,取自https://www.chfn.org.tw/news/coverage/0/39

蔡茹涵 (2019),元健大和,犧牲三成營收的數位競爭思維雲端給它機會搞破壞!助聽器小蝦米崛起戰記。2019年6月17日,取自https://www.businessweekly.com.tw/magazine/Article_mag_page.aspx?id=69504

愛電子 (2018),麥克風陣列技術為解決雜訊難題提供新思路。2019年3月1日,取自https://mp.ofweek.com/ee/a245673020466

王永華 (2006),認識助聽器,科林儀器股份有限公司,台灣,台北。

王永生、王進祥、曹貝(譯)(2017),數字信號處理:原理、實現及應用—基於MATLAB/Simulink與TMS3320C55XX DSP的實現方法(原作者:Sen M.Kuo, Bob H, Lee, Wenshun Tian),清華大學出版社,北京。

賀力行、林淑萍、蔡明春 (2013),統計學:觀念、方法、應用,台灣,台北。

張斐章、張麗秋 (2015),類神經網路導論原理與應用,蒼海圖書,台灣,台中。

黃銘緯 (2005),「台灣地區噪音下漢語語音聽辨測試」,國立台北護理學院聽語障礙科學研究所,碩士論文。

黃國原 (2009),「模擬人工電子耳頻道數、刺激速率與雙耳聽對噪音環境下中文語音辨識率之影響」,國立中央大學電機工程研究所,碩士論文。

張若華 (2005),「助聽器補償策略對華語理解度影響比較」,國立陽明大學醫學工程研究所,碩士論文。

郭世傑 (2011),「以軟體為基準的助聽器模擬平台之發展-使用雙麥克風策略之固定以及非固定背景噪音抑制」,國立中央大學電機工程研究所,碩士論文。

劉庭安 (2012),「運用TMS320C6713開發可自動情境分類之雙麥克風除噪系統」,國立中央大學電機工程研究所,碩士論文。

楊彥明 (2014),「運用TMS320C6713開發可自動匹配之雙麥克風除噪系統」,國立中央大學電機工程研究所,碩士論文。

陳宗佑 (2012),「以軟體為基準的助聽器模擬平台之發展-語音放大與補償」,國立中央大學電機工程研究所,碩士論文。

陳政鋒 (2015),「運用TMS320C6713開發可語音增強之雙麥克風除噪系統」,國立中央大學電機工程研究所,碩士論文。

羅偉倫 (2015),「具自動風聲噪音偵測之適應性除噪系統」,國立中央大學電機工程研究所,碩士論文。
指導教授 吳炤民(Chao-Min Wu) 審核日期 2019-11-1
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