We present a topological audio fingerprinting approach for robustly identifying duplicate audio tracks. Our method applies persistent homology on local spectral decompositions of audio signals, using filtered cubical complexes computed from mel-spectrograms. By encoding the audio content in terms of local Betti curves, our topological audio fingerprints enable accurate detection of time-aligned audio matchings. Experimental results demonstrate the accuracy of our algorithm in the detection of tracks with the same audio content, even when subjected to various obfuscations. Our approach outperforms existing methods in scenarios involving topological distortions, such as time stretching and pitch shifting.

翻译：我们提出了一种拓扑音频指纹方法，用于稳健地识别重复音频轨道。该方法对音频信号的局部频谱分解应用持久同调，利用从梅尔频谱图计算的滤波立方复形构建指纹。通过将音频内容编码为局部贝蒂曲线，我们的拓扑音频指纹能够精确检测时间对齐的音频匹配。实验结果表明，即使音频轨道受到多种干扰，该算法仍能准确检测具有相同音频内容的轨道。在涉及时间拉伸和音高偏移等拓扑扭曲的场景中，我们的方法优于现有技术。