Abstract
This thesis focuses on the front-end processing with voice detection of Voicecare system which is able to understand human calls for help. Since we cannot assume that calls for help occur at any pre-determined time, a Voicecare device needs a voice detector to distinguish whether the sound is a daily sound or voice at any time. Moreover, this device receives distant sounds; wrong judgments may be made if only comparing the volume or weighted SNR because the volume and weighted SNR of daily sounds are not definitely lower than the value of voice. This thesis addresses the problem and proposes to use fundamental frequency and harmonic structure to differentiate voice from nonvoice. Fundamental frequency is the pitch of voice; this thesis uses autocorrelation function to calculate the fundamental frequency of signals. Moreover, because of the periodicity of voice signals, there are peaks at fundamental and harmonic frequencies in the spectrum. Voice and nonvoice can be determinate based on the characteristics of signals as mentioned above. Experiments show that the false positive rate is within 28% and the miss rate is within 10%, if the SNR is above 5dB. By observing the magnitude spectrum of instrumental music and voice, we find out that the instrumental music with the characteristics of harmonic structure is misclassified as voice. However, the variation of instrumental music in magnitude spectrum is more dramatic than the variation of voice. Because of this observation, we can classify instrumental music and voice by calculating the total variation in magnitude spectrum. Experiments show that the false positive rate is within 11% and the miss rate is within 20%, if the SNR is 10 dB. A collection of sound files was recorded in Suang-Lien Elderly Center in Taipei (台北雙連安養中心) and Yan-Chai Elderly Center in Taipei (台北仁濟安老所), including voice and daily sounds of the elderly. In addition, voice and daily sounds gathered from classmates in Acoustic and Hearing laboratory and coughing sounds provided by新竹小太陽診所. For hardware development, we use “decimation in time” to calculate the spectrum and observe the harmonic structure. Also, Fast Fourier transform is utilized to shorten the computation time of autocorrelation function. These methods were implemented on a DSP board (Texas Instrument C6416). Hardware communication techniques, including techniques such as Interrupt and Multi-channel Buffered Serial Port (McBSP), are adopted to enable real-time implementation on the DSP board.