Hardware Design of Advanced Voice Separation and Enhancement System

碩士 === 國立中央大學 === 電機工程學系 === 104 === 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 si...

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Main Authors: Yu-He Chiou, 邱俞閤
Other Authors: 蔡宗漢
Format: Others
Language:zh-TW
Published: 2016
Online Access:http://ndltd.ncl.edu.tw/handle/33271044188066333703
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spelling ndltd-TW-104NCU054420652017-06-10T04:46:49Z http://ndltd.ncl.edu.tw/handle/33271044188066333703 Hardware Design of Advanced Voice Separation and Enhancement System 前瞻性語音分離與增強系統之硬體設計 Yu-He Chiou 邱俞閤 碩士 國立中央大學 電機工程學系 104 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. 蔡宗漢 2016 學位論文 ; thesis 57 zh-TW
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language zh-TW
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description 碩士 === 國立中央大學 === 電機工程學系 === 104 === 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.
author2 蔡宗漢
author_facet 蔡宗漢
Yu-He Chiou
邱俞閤
author Yu-He Chiou
邱俞閤
spellingShingle Yu-He Chiou
邱俞閤
Hardware Design of Advanced Voice Separation and Enhancement System
author_sort Yu-He Chiou
title Hardware Design of Advanced Voice Separation and Enhancement System
title_short Hardware Design of Advanced Voice Separation and Enhancement System
title_full Hardware Design of Advanced Voice Separation and Enhancement System
title_fullStr Hardware Design of Advanced Voice Separation and Enhancement System
title_full_unstemmed Hardware Design of Advanced Voice Separation and Enhancement System
title_sort hardware design of advanced voice separation and enhancement system
publishDate 2016
url http://ndltd.ncl.edu.tw/handle/33271044188066333703
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