Hardware-Accelerated, Short-Term Processing Voice and Nonvoice Sound Recognitions for Electric Equipment Control

Abstract

We proposed and implemented a sound recognition system for electric equipment control. In recent years, industry 4.0 has propelled a rapid growth in intelligent human–machine interactions. User acoustic voice commands for machine control have been examined the most by researchers. The targeted machine can be controlled through voice without the use of any hand-held device. However, compared with human voice recognition, limited research has been conducted on nonhuman voice (e.g., mewing sounds) or nonvoice sound recognition (e.g., clapping). Processing of such short-term, biometric nonvoice sounds for electric equipment control requires a rapid response with correct recognition. In practice, this could lead to a trade-off between recognition accuracy and processing performance for conventional software-based implementations. Therefore, we realized a field-programmable gate array-based embedded system, such a hardware-accelerated platform, can enhance information processing performance using a dynamic time warping accelerator. Furthermore, information processing was refined for two specific applications (i.e., mewing sounds and clapping) to enhance system performance including recognition accuracy and execution speed. Performance analyses and demonstrations on real products were conducted to validate the proposed system.