Abstract:
In order to improve recognition rate of target materials by using reflective sound signal of high pressure water-jet, in view of four common targets of mine, stone, brick and wood block, different feature extraction methods were used to identify target materials. On basis of analyzing basic principles of Mel frequency cepstral coefficients and wavelet packet transform cepstral coefficients, combining with characteristics of reflective sound signal of target, a feature extraction method based on feature fusion of Mel frequency cepstral coefficients and wavelet packet transform cepstral coefficients was presented. The reflective sound signal of original target was decomposed to several sub-bands by using wavelet packet transform, and one of the optimal sub-band was selected as separate layer of low frequency and high frequency. Mel frequency cepstral coefficients were calculated as eigenvalues in low frequency part, and wavelet packet transform cepstral coefficients were calculated as eigenvalues in high frequency part. The two groups of eigenvalues were merged into a new set of linear feature vector, and the new vector was input into target identification model. LS-SVM classification model was built to evaluate recognition rate of the feature extraction methods based on single characteristic and feature fusion. The experiment results show that when the best division layer between low frequency and high frequency was acquired, the average recognition rate of feature extraction method based on feature fusion reaches 82.812 5%, there was a increase of 10.312 5% and 7.812 5% compared with using Mel frequency cepstral coefficients or wavelet packet transform cepstral coefficients as feature vector.
