CLASSIFICATION OF REAL SOUNDS FOR HEARING AIDS BASED ON TIME-FREQUENCY IMAGE PROCESSING

This paper presents features of sound data for a sound classification equipped for hearing aids. The features are extracted by using image processing techniques to timefrequency images. As an application of hearing aids in mind, four classes of “classical music”, “speech”, “multitalker noise” and “speech in the noise” are prepared in order to classify the input signal of a hearing aid into useful classes. Although there are several possible ways to figure out which class the current input signal belongs to, an approach from image processing is utilized to find out appropriate features because 2D image (time-frequency image) can contain multifaceted information compared to 1D information (waveform or frequency response of sound), and can be regarded as comprehensive data. It is found that eight features are required to meet a certain quality of sound classification according to our investigation. Experimental results of the sound classification by some clustering machines using the proposed features have shown that accuracy of the classification was more than 95 % with every clustering machine.