Robust sound recognition applied to awareness for health/children/elderly care

This paper presented a robust sound recognition work applied to awareness for health/children/elderly care. Specific sound awareness services can be activated based on recognized sound classes for detecting human activities as health care. To attain this goal, this study developed key technologies as follows: 1) SNR-aware subspace signal enhancement, 2) pitch and power density-based sound/speech discrimination, 3) HMM-based speech recognition, 4) sound recognition with ICA-transformed MFCCs feature and frame-based multiclass SVMs. Each classified sound event is response to human with predefined processes as sound awareness info. Simulations and an experiment are given to illustrate the performance of the proposed robust sound recognition system in a real-world home environment, Aspire Home, NCKU. The overall average resulting accuracy rate was approximately 90.97%.