  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.
