The Subjective Response Of The Human Ear Auditory Feature And Its Application In Target Recognition

Automatic underwater targets recognition has long been an important research topic in the field of acoustic signal processing. Feature extraction is the key step in the underwater targets recognition. Unfortunately, traditional approaches using signal processing and signal transformatinon technique applied to target recognition have drawbacks. A feature extraction approach based on auditory properties and psychoacoustic model is proposed to enhance underwater target recognition ability in this thesis and the research work focuses on the applications of auditory theory into acoustic targets recognition.Firstly, the features of the human auditory system are investigated, expecially hearing process and hearing perception mechanism. Human auditory system's special configuration effect the characteristics of sound in ear, such as frequency distinguish, pitch recognition, strength distinguish, time delay and so on. Human's subjective perceptions can mainly be described by loudness, pitch and timber, which can be calculated based on Zwicker's theory, and pitch period is estimated by the dyadic wavelet transform. Meanwhile, human brain plays a very important role in human auditory recogniton system. Therefore three-layer back-propogation neural network classifier is used to recognize the acoustic target in the thesis.Secondly, in order to find effective features, aimed at ship-radiated noise, automobile noise and radio noise, target recognition is examined experimentally. The conclusions are as follow: 1. Specific loudness, specific sharpness, loudness and sharpness, the human hearing features based on psychoacoustics are effective features in the targets recognition. 2. Pitch as a correlation feature, reflects the acoustic targets' frequency characteristic. 3. Loudness feature combine with pitch feature is used to improve the target recognition.Finally, specific loudness feature is selected by genetic algorithm, which can reduce the features' dimension and enhance the accuracy rate. And from more experiments, it is concluded that samples' time length and frequency analysis bandwidth affect the recognition results. It is shown that from such experiments that the specific loudness based on critical-band rate and 1/3 octave-band rate is most effective for recognizing acoustic targets. Furthermore, the critical-band rate analysis is applicable for the ordinary targets recognition.