Corn impact acoustic signal classification based on PCA-KICA dual space feature extraction

In this paper,a dual space feature extraction method was put forward based on principal component analysis (PCA) and kernel independent principal component analysis (KICA),the PCA-KICA method.Impact acoustic signal device was used to collect signals of undamaged corn kernels,insect-damaged kernels and mildew-damaged kernels.First,the samples were performed to the PCA space for feature extraction and then performed to the KPCA space for feature extraction.Subsequently,these features were input to support vector machine,which was optimized by particle swarm optimization.Experimental results showed,single subspace could not get a better classification accuracy rate,but applying dual space feature extraction method could overcome the limits of single subspace.PCA-KICA method emerged the highest recognition rates,which were 95.00%,96.40%,97.80% for undamaged kernels,insect-damaged kernels and mildew-damaged kernels respectively.