Toward musically-motivated audio fingerprints

In this paper, we investigate to which extent well-known audio fingerprinting techniques, which aim at identifying a specific audio recording, can be modified to also deal with more musical variations. To this end, we replace the standard peak fingerprints based on a spectrogram by peak fingerprints based on other more “musical” feature representations. Our systematic experiments show that such modified peak fingerprints allow for a robust identification of different versions and performances of the same piece of music if the query length is at least 15 seconds. This indicates that highly efficient audio fingerprinting techniques can also be applied to accelerate tasks such as audio matching or cover song identification.