Artificial Intelligence Applications: Bone-Conducted Microphone and Senior Smart Band

• Main Authors: Hung-Ping Liu, 劉鴻儐
• Other Authors: Chiou-Shann Fuh
• Format: Others
• Language: en_US
• Published: 2019
• Online Access: http://ndltd.ncl.edu.tw/handle/hyur96

博士 === 國立臺灣大學 === 生醫電子與資訊學研究所 === 107 === Artificial Intelligence (AI) study starts from the middle of 20 century after computers and computer languages are created. Computer calculation power and parallel processing make AI superior to human in specific domain. Many different neural networks make computers learn the rules from data. Computers learn a little in every loop. After thousands and millions of loops, they finally learn the rules and perform better than human. We use AI technologies in the following applications: bone-conducted microphone and senior smart band. Bone-Conducted Microphone (BCM): BCMs capture speech signals based on the vibrations of the talker’s skull, and they exhibit better noise-resistance capability than normal Air-Conducted Microphones (ACMs) when transmitting speech signals. Because BCMs only capture the low-frequency portion of speech signals, their frequency response is quite different from that of ACMs. When replacing an ACM with a BCM, we may obtain satisfactory results with respect to noise suppression, but the speech quality and intelligibility may be degraded owing to the nature of the solid vibration. The mismatched characteristics of BCM and ACM can also impact the Automatic Speech Recognition (ASR) performance, and it is infeasible to recreate a new ASR system using the voice data from BCMs. In this study, we propose a novel Deep-Denoising AutoEncoder (DDAE) approach to bridge BCM and ACM in order to improve speech quality and intelligibility, and the current ASR could be employed directly without recreating a new system. Experimental results first demonstrate that the DDAE approach can effectively improve speech quality and intelligibility based on standardized evaluation metrics. Moreover, our proposed system can significantly improve the ASR performance with a notable 48.28% relative Character Error Rate (CER) reduction (from 14.50% to 7.50%) under quiet conditions. In an actual noisy environment (sound pressure from 61.7 dBA to 73.9 dBA), our proposed system with a BCM outperforms an ACM, yielding an 84.46% reduction in the relative CER (our proposed system: 9.13% and ACM: 58.75%). Senior Smart Band: Modern medicine enables a larger segment of the population to survive beyond 65 years old. Senior care has become one of the single most relevant challenges globally. There are many relevant issues related to monitoring and managing the daily activities of senior citizens. In this study, we will examine the role of advanced activity sensor platform to monitor their daily activity levels. It will assist in understanding the lifestyle of the individual seniors to promote safety and improve the quality of life through measurement of physical strength and independence. We investigated the requirements of wearable technology for the seniors, employing Inertial Measurement Unit (IMU) sensor for senior care. Our proposed system includes a wearable device for each senior, Internet of Thing (IoT) receiver environment, smart alert, machine learning algorithm, and Application Processing Interface (API) with remote Internet access. The primary application of our proposed IMU sensor application includes precise measurement of individual physical activity level.