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Robust Quad-Based Audio Fingerprinting

机译:强大的基于四核的音频指纹识别

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摘要

We propose an audio fingerprinting method that adapts findings from the field of blind astrometry to define simple, efficiently representable characteristic feature combinations called quads. Based on these, an audio identification algorithm is described that is robust to noise and severe time-frequency scale distortions and accurately identifies the underlying scale transform factors. The low number and compact representation of content features allows for efficient application of exact fixed-radius near-neighbor search methods for fingerprint matching in large audio collections. We demonstrate the practicability of the method on a collection of 100,000 songs, analyze its performance for a diverse set of noise as well as severe speed, tempo and pitch scale modifications, and identify a number of advantages of our method over two state-of-the-art distortion-robust audio identification algorithms.
机译:我们提出了一种音频指纹识别方法,该方法可以适应盲天文测量领域的发现,以定义称为四边形的简单,有效可表示的特征特征组合。基于这些,描述了一种音频识别算法,该算法对噪声和严重的时频标度失真具有鲁棒性,并且可以准确地识别潜在的标度转换因子。内容特征的数量少且结构紧凑,可以有效地应用精确的固定半径近邻搜索方法进行大型音频集合中的指纹匹配。我们展示了该方法在100,000首歌曲中的实用性,分析了其在各种噪音以及严重的速度,速度和音高音阶修改情况下的性能,并确定了该方法在两种状态下的诸多优势最新的失真鲁棒音频识别算法。

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