基于压缩感知和音频指纹的固定音频检索方法

针对现有音频检索中样本音频特征库数据量较大且检索速率慢问题,本文提出一种基于压缩感知和音频指纹降维的固定音频检索方法.在音频检索的训练阶段,首先,对样本音频信号进行稀疏化处理,并通过压缩感知算法对稀疏化后的音频数据进行压缩;其次,提取压缩信号的音频指纹;再次,引入音频指纹离散基尼系数通过计算音频指纹各维度的离散基尼系数对指纹实施降维,最终得到检索特征库.在音频检索阶段用和训练阶段相同的算法提取待检音频的特征与音频特征库数据匹配得出检索结论.实验结果表明,所提音频检索方法在确保较好的检索准确率的基础上,大幅度减小了样本音频数据库的存储量,提高了音频的检索速率.

Specific Audio Retrieval Method Based on Compressed Sensing and Audio Fingerprint

In order to solve the problem of large amount of data and slow retrieval speed in the existing audio retrieval, a fixed audio retrieval method is proposed in this study based on compressed sensing and audio fingerprint dimensionality reduction. In the training stage of audio retrieval, the sample audio signal is sparse processed, and the sparse audio data is compressed by the compression sensing algorithm, then the audio fingerprint is extracted, and then the audio fingerprint discrete Gini coefficient is introduced to reduce the dimension of the fingerprint by calculating the discrete Gini coefficient of each dimension of the audio fingerprint. In the recognition stage of audio retrieval, we use the same algorithm as in the training stage to process the audio to be tested and match with the sample audio fingerprint. The experimental results show that the proposed audio retrieval method greatly reduces the storage of the sample audio database and improves the audio retrieval speed on the basis of ensuring a better retrieval accuracy.