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研究生: 蔡尚宏
Tsai, Shang-Hung
論文名稱: 具噪聲消除之低功率及低面積聲音辨識晶片設計應用於居家照護系統
A Low-Power and Small Area Sound Recognition Chip Design with Noise Cancellation for Home Care System
指導教授: 王駿發
Wang, Jhing-Fa
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年: 2015
畢業學年度: 103
語文別: 英文
論文頁數: 60
中文關鍵詞: 低功率低面積雜訊消除聲音辨識晶片設計
外文關鍵詞: low-power, small area, noise cancellation, sound recognition, chip design
相關次數: 點閱:91下載:4
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  • 台灣已走向老年化社會並逐步加重社會負擔,高齡社會已成為急需解決之社會議題。因此我們提出「具噪聲消除之低功率及低面積聲音辨識晶片設計」,期許此晶片能給予老人、幼童等較需要幫助之族群,即時的照護與救助。
    本篇研究提出之自動化聲音辨識(Automatic Sound Recognition, ASR)晶片設計將採用梅爾規格倒頻譜係數(Mel-Frequency Cepstral Coefficients, MFCC)做為聲音辨識特徵,並針對雜訊干擾、聲音辨識率、未知聲音判斷、晶片功耗及面積大小等問題,提出硬體電路改進。最後,本篇研究提出一結合聲音活性檢測(VAD)與clock gating技術之晶片架構,能有效降低晶片待機狀態功耗,使其具備長時間運作能力,提升晶片之居家照護實用性。
    實現方面,我們利用國家晶片系統設計中心(Chip Implementation Center, CIC)與台灣積體電路公司(TSMC)所提供的90奈米製程完成本晶片實作下線。晶片面積為1.15*1.15 mm2,以40支接腳封裝,閘總數(Gate Count)約為188k,消耗功率為2.803 mW,最高工作頻率為10 MHz。

    Aged people has increased vastly in Taiwan and the social burden has increased, too. Aging society has become an urgent social issue which needs to be solved. Therefore, we proposed an “A Low-Power and Small Area Sound Recognition Chip Design with Noise Cancellation for Home Care System”, and we hope the chip can provide timely aid and care for elderly or children who need help.
    This study proposed the automatic sound recognition (ASR) chip design that it adopts the Mel-frequency cepstral coefficients (MFCC) feature. Then, we modify the hardware circuit for the problem of noise interference, recognition accuracy, unknown sound detection, power consumption and area of chip. Finally, this study proposed a chip architecture that combines voice activity detection (VAD) and clock gating technique to reduce power consumption in idle state of chip, make it can operate longer and enhance the practicality for home care.
    For implementation, we has been tape-out in TSMC’s 90nm process via Chip Implementation Center (CIC). The chip area is 1.15*1.15 mm2, 40-pin package, gate count is 188k, the power dissipation is 2.803 mW and the highest operation frequency is 10 MHz.

    中文摘要 I Abstract II 誌 謝 III Content IV Table List VI Figure List VII Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Related Works 1 1.3 Research Contributions 2 1.4 Thesis Organization 5 Chapter 2 Automatic Sound Recognition System 6 2.1 System Overview 6 2.2 LMS Algorithm for Noise Cancellation 7 2.3 Voice Activity Detection 9 2.4 Mel-Frequency Cepstral Coefficients (MFCC) Feature Extraction 10 2.4.1 Frame Blocking 10 2.4.2 Hamming window 11 2.4.3 Fast Fourier Transform (FFT) 11 2.4.4 Mel-Frequency Filter Bank 13 2.4.5 Take Logarithm 14 2.4.6 Recursive Discrete Cosine Transform (DCT) 15 2.5 K-means Clustering 18 2.6 K-Nearest Neighbors Classifier 19 Chapter 3 Chip Design for ASR System 21 3.1 Chip Architecture Overview 21 3.2 Noise Cancellation Module 22 3.3 Voice Activity Detection Module 25 3.4 Mel-Frequency Cepstral Coefficients Module 26 3.4.1 Hamming Window Hardwar Design 27 3.4.2 Fast Fourier Transform Hardwar Design 28 3.4.3 Mel-Filter Bank Hardware Design 31 3.4.4 Logarithm Hardware Design 33 3.4.5 RDCT Hardware Design 34 3.4.6 MFCC Hardwar Reuse Architecture 35 3.5 K-Nearest Neighbors and Out of Sound Module 38 3.6 Low-Power Chip Architecture 40 Chapter 4 Experimental Results and Chip Tape Out 42 4.1 Experimental Environment Setup 42 4.2 Feature Extraction Methods Evaluation 43 4.3 KNN Classifier Evaluation 44 4.4 Noise Cancellation Effectiveness Evaluation 45 4.5 Fixed-Point Error Analysis 46 4.6 Hardware Cost of ASR Chip 47 4.7 ASR Chip Tape Out 48 4.7.1 Specification of the Proposed ASR Chip 50 4.7.2 Testing Circuit 51 4.7.3 Chip Layout and Verification 54 Chapter 5 Conclusions and Future Works 57 5.1 Conclusions 57 5.2 Future Works 58 References 59

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