This project deals with audio fingerprinting (also known as robust audio hashing or perceptual audio hashing). An audio fingerprint  is a compact representation of an audio file which is linked to its perceptual content. This representation is parallel to a cryptographic hash in the sense that it almost uniquely represents the file, but it is different from it in the sense that different instances of the same file which are perceptually equivalent must lead to the same hash value. Audio fingerprinting helps to identify audio files that are noisy or that have been compressed using different formats or parameters, which precludes identification using bit-by bit mapping. Therefore it finds application in areas such as tracking in peer-to-peer networks. Audio fingerprinting can also be used efficiently identify full audio files by means of short sample segments.
In the project, the student will implement and test an audio fingerprinting method based on spectral peaks detection. If possible, s/he will compare this implementation against an open source audio fingerprinting method. Exceptionally, s/he will propose a mathematical model that can be used to assess the performance in a theoretical way.