前瞻性語音分離與增強系統之硬體設計

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姓名

邱俞閤(Yu-He Chiou) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

前瞻性語音分離與增強系統之硬體設計
(Hardware Design of Advanced Voice Separation and Enhancement System)

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摘要(中)

盲訊號分離是以摺積性混合訊號為假設基礎，去做訊號重建之技術。混合訊號會經過短時傅立葉轉換，轉換到頻域，因為訊號源有稀疏性特性，我們可以根據空間特徵，來聚集這些特徵時頻點。一般來說，可以用各個聲源到兩個麥克風的相位差和強度比作為空間特徵。本系統為一智慧電子系統，我們在未知聲源位置情況下，使用信號時頻分佈稀疏性的二元時頻遇遮罩技術分離訊號，達到抑制噪音的目的。
本系統針對於駕駛時，手持電話通話具有危險性並且違法，而免持通話方法解決了這個問題，如車用電話耳機，然而，行車通訊時常會有背景噪音，像是鳴喇叭汽車與高速公路的雜音，導致通訊品質下降，也造成駕駛人無法專注，延伸出許多困擾。為方便行車通訊，使用車用電話耳機通訊也逐漸普及，因此我們發展一個雙麥克風抗噪技術的系統，系統能將噪音與語音分離出來。將上述演算法以硬體架構實現，以TSMC 90nm的製程去實現我們的設計，我們的設計操作在10MHz在不包含memory的情況下約為119.71K的gate count，消耗功率約為2.92mW，memory使用量為69Kbits。

摘要(英)

Blind source separation uses convolutive mixture signals as assumptions to reconstruct different signals. The mixture signals will go through a short time Fourier transform, and then being transferred into frequency domain. Because of the haracteristics of the signal sources are sparse. We can gather time-frequency point by spatial characteristics. Generally speaking, we can apply various sound sources to the different phase between the two microphones and the intensity ratio as the spatial characteristics. Our system is a smart electronic system. We can apply frequency masking techniques in case of binary frequency distribution sparse signal to separate signals without knowing where the source is.
We have a complete system-level solution on algorithm and VLSI implementation. This design is using TSMC 90 nm library with 10 MHz operation frequency. Without calculating memory of gate count about 119.71K. Power consumption about 2.92mW and memory usage is 69Kbits.

關鍵字(中)

★ 盲訊號分離
★ 硬體架構
★ 陣列式麥克風

關鍵字(英)

論文目次

摘要 I
ABSTRACT II
TABLE OF CONTENTS III
LIST OF FIGURES IIV
LIST OF TABLES V
CHAPTER 1 Introduction 1
1.1 MOTIVATION 2
1.1.1. Typology of mixtures 3
1.2 THESIS ORGANIZATION 6
CHAPTER 2 Background 7
2.1 BSS BASED ON INDEPENDENT COMPONENT ANALYSIS 9
2.2 BSS BASED ON SPARSENESS 11
CHAPTER 3 Overall of the Proposed Architecture Design for Blind Source Separation 16
3.1 SHORT-TIME FOURIER TRANSFORM (STFT) 29
3.1.1. Algorithm of STFT 19
3.1.2. Architecture of STFT 21
3.2 FEATURE EXTRACTION 25
3.1.1. Algorithm of Feature extraction 25
3.1.2. Architecture of Feature extraction 26
3.3 CLUSTERING 30
3.1.1. Algorithm of Clustering 30
3.1.2. Architecture of Clustering 31
3.4 BINARY MASK 34
CHAPTER 4 Experimental Results 43
4.1 EXPERIMENTAL SETTING 35
4.1 RESULT OF BLIND SOURCE SEPARATION 37
4.3 CHIP SPECIFICATION 40
CHAPTER 5 Conclusion 43
REFERENCES 45

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指導教授

蔡宗漢

審核日期

2016-7-22

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