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Robust Audio Fingerprinting for Multimedia Recognition Applications

机译:用于多媒体识别应用的强大音频指纹识别

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摘要

For a reliable audio fingerprinting (AFP) system for multimedia service, it is essential to make fingerprints robust to the time mismatch between live audio stream and prior recordings, as well as they should be sensitive to changes in contents for accurate discrimination. This paper presents a new AFP method using line spectral frequencies (LSFs), which are a kind of parameters that capture the underlying spectral shape: the proposed AFP method includes a new systematic scheme for the robust and discriminative fingerprint generation based on the inter-frame LSF difference, and an efficient matching algorithm using the frame concentration measure based on the frame continuity property. The tests on database containing a variety of advertisements are carried out to compare the performances of Phillips Robust Hash (PRH) and the proposed AFP. The test results demonstrate that the proposed AFP can maintain its true matched rate at over 98% even when the overlap ratio as low as 87.5%. It can be concluded that the proposed AFP algorithm is more robust to time mismatch conditions when compared to PRH method.
机译:对于用于多媒体服务的可靠音频指纹(AFP)系统,必须使指纹对现场音频流和现有录制之间的时间不匹配,以及它们应该对内容的变化敏感,以便准确辨别。本文介绍了一种使用线谱频率(LSF)的新AFP方法,这些方法是捕获底层光谱形状的一种参数:所提出的AFP方法包括基于帧间帧的鲁棒和辨别指纹产生的新系统方案LSF差异,以及基于帧连续性属性的帧浓度测量的有效匹配算法。对包含各种广告的数据库进行测试,以比较Phillips鲁棒散列(PRH)和所提出的AFP的性能。测试结果表明,即使重叠率低至87.5%,所提出的AFP也可以将其真实匹配的速率保持在98%以上。可以得出结论,与PRH方法相比,所提出的AFP算法在时间不匹配的时间更加稳健。

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