关键词: 主成分分析, BP神经网络, 示例优选, 多媒体, 音频检索

Abstract: This paper proposes the example preferred method based on Principal Component Analysis（PCA） and BP Neural Network（BPNN） to solve computational cost, long retrieval time and poor noise robustness by the amount of example data in audio sample retrieval study. The paper builds the segment level features by principal component analysis, eliminates redundant data, and reduces the input variables, then models and predicts reserved ingredients by the BPNN. It tests the experimental data by the PCA-BPNN model confirmatory. Finally, experimental results show that the method can select optimization example from an audio accurately and efficiently.

Key words: Principal Component Analysis（PCA）, Back Propagation Neural Network（BPNN）, optimum selection of audio sample, multimedia, audio retrieval