Cavitation noise classification based on spectral statistic features and PCA algorithm
Xiangdong Jiang, Qiang Wang, Xiangyang Zeng · 2013
Small amount of training data confines the performance of auto noise classification system, especially when the dimensions of features are in a large scale. In this paper, 26-dimensional features are extracted from cavitation noise spectrum and line spectrum from three classes of cavitation noises. Principal component analysis (PCA) based method is applied to deal with the high-dimensional features which may lead to a high risk of over-fitting. Experiments using noise signals indicated that feature extracting method proposed in this paper performs well, and PCA processing is efficient to deal with the high-dimensional problem and can achieve a high recognition rate under the cases such as auto classification when the amount of training data is limited.