Intelligent Automation of Acoustic Classification Using Independent Component Analysis and Competitive Neural Nets

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In this research, we have set the final goal to achieve the intelligent acoustic classification system with compact size by using independent component analysis (ICA) and competitive neural networks. Using the ICA, we have developed the ICA noise separation system to obtain excellent separation of signals and noises and applied it to real acoustic data of compact shaving machine, which were observed under very noisy condition.Then the separated acoustic signals were transformed into frequency domain by using Fourier transform. From the spectrum we have decided the feature vector to classify the compact shavers into good or not based on the competitive neural networks. The adopted neural network is the Kohonen's network of self-organizing feature map (SOM). Using the SOM, we select the most effective feature vector of the spectrum of acoustic data. Then we apply another competitive neural network of learning vector quantization (LVQ). Finally, we have tested the other products such as massage machine, caps of illumination lamps, bearing shaft, etc.