A warning signal identification system (WARNSIS) for the hard of hearing and the deaf

The objective of this project has been to design a reliable warning sound recognition system for hard of hearing and deaf people. Commercially available auditory warning devices use simple technologies, which are not able to produce the performance required. The demand for a versatile WARNing Signal Identification System (WARNSIS) that satisfies the needs of hard of hearing and deaf individuals has been well established. This WARNSIS must be "teachable" in order to cope with the many different sounds, and diverse noisy environments. Relevant sounds are telephone rings, sirens, and smoke and fire alarms, and noise includes all other sounds including radio-music, conversation, machinery, etc. In the absence of published data, we studied extensively both timing and spectral characteristics of warning sounds. We found that the average short-time absolute amplitude of warning sounds is useful in providing timing information, and that the short-time spectra yield characteristic patterns for signal classification. The WARNSIS operates in real-time, and embodies two parts: the timing analyzer and the spectral recognizer. The timing analyzer continuously monitors the variations of environmental sounds, from which important timing features are derived. If a potential warning sound is detected, the spectral recognizer is activated to analyze its spectral patterns. When these patterns match one of the learned and pre-stored templates, a warning sound is identified with the known warning sound associated with that template. An advantage of such a recognition scheme is that it avoids unnecessary and computationally intensive spectral analysis work when only noise is present. Evaluation results show that the WARNSIS can reliably recognize warning sounds in random noise with no false alarms. In loud music and conversation backgrounds the WARNSIS can still achieve a high recognition rate, but more false alarms are generated. In household environments where conditions are less demanding than our evaluation criteria, our system is expected to produce very satisfactory results. Since the WARNSIS can be taught to learn and recognize new warning sounds, it may be used in other applications such as noisy industrial sites and traffic light control.Applied Science, Faculty ofElectrical and Computer Engineering, Department ofGraduat