This paper presents the detection and diagnosis of air-conditioner electromagnetic sound through noise measurements. Electromagnetic sound originating from the motor is an unpleasant source of unwanted noise that should be detected at the manufacturing stage. A detection system using sound measurements was built and a detection algorithm based on FFT analysis is presented. Sound measurements are preferable over direct vibration measurement because it is non-contact and low cost. Experimental results show that our sound measurement system can detect electromagnetic sound effectively compared to using vibration measurements.

This paper proposes a method to extract the personal information using a microphone array. Useful personal information, particularly customers, are age and gender. On the basis of these information, service applications for robots can satisfy users by offering services adaptive to the special needs of specific user groups that may include adults and children as well as females and males. We applied Gaussian Mixture Model (GMM) as a classifier and Mel Frequency Cepstral coefficients (MFCCs) as a voice feature. The major aim of this paper is to discover the voice source parameters of age and gender and to classify these two characteristics simultaneously. For the ubiquitous environment, voices obtained by the selected channels in a microphone array are useful to reduce background noise.