Field Mixed Acoustic identification hybrid Systems Based on ICA and Improved GCA

With independent component analysis (ICA) to realize the blind separation from mixed acoustic objects, an identification method based on improved gray correlation analysis (IGCA) is proposed through extracting linear prediction coefficient (LPC) feature. It is revealed that LPC is consistently better than wavelet energy feature, ICA is efficient algorithm to estimate the unknown signal level and IGCA which gets over the shortcomings of GCA model may reflect the difference and similarity of the influences of factors or characteristics effectively. The validity of the new systems is verified via examples in mixed acoustic objects identification system.