Audio Similarity Measure Based on Renyis Quadratic Entropy

摘要：

Considering noise interference often exists in audio processing, it is not robust enough to calculate audio similarity by using distance measure directly. In this paper, basing on Renyis quadratic entropy, a novel scheme for audio similarity measure is proposed. In our work, we extract Mel Frequency Cepstral Coefficients (MFCCs) to represent each audio, and then calculate the similarity based on the entropy of audio samples by probability density function (pdf) of MFCCs which can be estimated by Parzen window. The experimental results show that: (a) our approach has better performance than the one based on Euclidean distance in the common SNR condition, (b) our approach can achieve 94.00％ matching accuracy even when the signal to noise ratio (SNR) is Odb. In addition, our algorithm also can be applied in audio retrieval and musical cluster.

作者: Xiaoqing Yu Xueqian Pan Wei Yang Wanggen Wan Jing Zhang

作者单位: School of Communication and Information Engineering, Shanghai University Shanghai 200072, China

会议类型: 国际会议

会议名称: 2010 International Conference on Audio,Language and Image Processing(2010年音频、语言与图像处理国际会议 ICALIP 2010)

会议地点: 上海

会议语种:英文

页码: 722-726

在线出版日期: 2010-11-23（万方平台首次上网日期，不代表论文的发表时间）