An ideal audio retrieval system identifies a short query snippet from a massive audio database with both robustness and efficiency. Unfortunately, none of the existing systems could robustly handle all distortions while being efficient. Enhanced sampling and counting (eSC), the state-of-the-art audio retrieval method proposed for Philips-like fingerprints, has achieved both high efficiency and strong robustness, featuring time-stretch resistance, however, suffers from pitch-shift attacks. This article proposes a peak-point-based energy bands computation method to enhance Philips fingerprint with resistance to pitch-shift, and the resulting fingerprint is called peak-point based Philips fingerprint (PPF). Experimental results show that PPF can resist pitch-shift ranging from 70% to 130%, which enhances eSC with pitch-shift resistance while retaining all its benefits.

中文翻译：

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基于峰值的飞利浦指纹识别功能坚固耐用，可进行音高转换，以进行音频识别

理想的音频检索系统会从健壮和高效的音频数据库中识别出简短的查询片段。不幸的是，现有系统都无法在高效的同时稳健地处理所有失真。增强型采样和计数（eSC）是针对飞利浦式指纹技术提出的最新音频检索方法，已实现了高效率和强大的鲁棒性，具有耐时间拉伸性，但是受到音高偏移攻击。本文提出了一种基于峰点的能带计算方法，以增强飞利浦指纹的抗点距偏移能力，所得指纹称为基于峰点的飞利浦指纹（PPF）。实验结果表明，PPF可以抵抗70％至130％的音高变化，