Dissertation
Dissertation > Industrial Technology > Radio electronics, telecommunications technology > Communicate > Electro-acoustic technology and speech signal processing > Speech Signal Processing

Microelectronic System Design and Optimization for Monitoring of Mental Fatigue on Speech Signal

Author LiZhiHai
Tutor LiWenShi
School Suzhou University
Course Microelectronics and Solid State Electronics
Keywords Mental Fatigue Speech Signal MATLAB Voice Characteristics Hardware Modeling Optimization
CLC TN912.3
Type Master's thesis
Year 2011
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The research direction on voice-based human mental fatigue detection has grasped many eyes of experts in the world’s academic and industry fields, for reasons of on one hand the trends of modern health monitoring and of on other hand the test-easy of voice signals.Our objectives aim at two goals: After combing the known advantages and disadvantages of the normal methods on human mental fatigue testing, we measure speech signals in condition of fatigue-free and fatigue, then attract key features, and then analyze their validities. More interestingly works address corresponding feature-modules design with EDA tools.The methods and processes are outlined as following: In detail in order to recognize voices in mental fatigue-free and fatigue, 4 ~5 seconds data are sampled by PC embedded MATLAB software platform. The mean-value ratio of pre- and post-features is defined into evaluation thresholds. And with the assistances of famous EDA tool named of System Generator and Xilinx ISE 11 our corresponding feature-modules design optimization are marched onto complexities’report.The results show that five key features’validity-sequence can list as (1) short-time energy(mean-bias 298%), (2) short-time mean amplitude(41.9%), (3) short-time cross-zero ratio(15.9%), (4) short-time self-correlation(12%), and (5) pitch(10%), just about same as their modules’structure-sophistications.Compared with other advanced tricks on mental fatigue tests, our works may accelerate low power consumption combined with trade-off design thinking for future study of cerebral health monitoring.

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