基于mfcc的事件声音识别芯片中基于基数-2 FFT和mel滤波器组的优化设计

Ta-Wen Kuan, Jhing-Fa Wang, Tsai Shang-Hung
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引用次数: 2

摘要

本文首次提出了一种安全敏感事件声音识别的声音芯片设计,将橙色暖化关怀的交互从人与人之间的感知扩展到环境对人的感知。该芯片可以嵌入智能传感器或家用电器中,探测周围的事件声音,及时照顾独居老人或儿童,主动求助。为了实现芯片的高精度性能、小面积和低功耗,对MFCC的几个子模块,包括基数-2 FFT、mel滤波器组等进行了优化设计,以达到芯片设计所要求的特性。仿真结果表明,基于k-NN框架的MFCC比基于k-NN分类器的LPCC和MP特征具有更高的识别精度。在芯片实现方面,优化后的MFCC子模块确实提高了硬件资源利用率,其中芯片采用verilog进行设计和仿真,并采用台积电90nm库进行合成。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimized radix-2 FFT and Mel-filter bank in MFCC-based events sound recognition chip design for active smart warming care
The paper proposes a first sound chip design for security-sensitive event sounds recognition that extended the interaction of Orange warming care from human-to-human to environment-to-human perception. The proposed chip is fittingly embedded in smart sensors or appliances at home to surroundingly detect the event sounds, which can timely care the elderly or children who live alone thus actively call for assistance. In order to realize the chip in a high-accuracy performance, a small-size area and a low-power dissipation, the MFCC several sub-modules including, radix-2 FFT, Mel-filter bank etc are optimized for chip design to reach the required characteristics. In the simulation results, the proposed MFCC with k-NN framework performs the higher recognition accuracy than LPCC and MP features having k-NN classifier. For chip realization, the optimized MFCC sub-modules indeed improve the hardware resource utilization, where the chip is designed and simulated by verilog and synthesized by TSMC 90nm library.
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