Acoustic fingerprint

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An acoustic fingerprint is a condensed digital summary, a

A robust acoustic fingerprint algorithm must take into account the perceptual characteristics of the audio. If two files sound alike to the human ear, their acoustic fingerprints should match, even if their binary representations are quite different. Acoustic fingerprints are not hash functions, which are sensitive to any small changes in the data. Acoustic fingerprints are more analogous to human fingerprints where small variations that are insignificant to the features the fingerprint uses are tolerated. One can imagine the case of a smeared human fingerprint impression which can accurately be matched to another fingerprint sample in a reference database; acoustic fingerprints work similarly.

Perceptual characteristics often exploited by audio fingerprints include average

Generating a signature from the audio is essential for searching by sound. One common technique is creating a time-frequency graph called a spectrogram.

Any piece of audio can be translated into a spectrogram. Each piece of audio is split into segments over time. In some cases, adjacent segments share a common time boundary, in other cases adjacent segments might overlap. The result is a graph that plots three dimensions of audio: frequency vs amplitude (intensity) vs time.

Shazam

Shazam's algorithm picks out points where there are peaks in the spectrogram which represent higher energy content.^[2] Focusing on peaks in the audio greatly reduces the impact that background noise has on audio identification. Shazam builds their fingerprint catalog out as a hash table, where the key is the frequency. They do not just mark a single point in the spectrogram, rather they mark a pair of points: the peak intensity plus a second anchor point.^[3] So their database key is not just a single frequency, it is a hash of the frequencies of both points. This leads to fewer hash collisions improving the performance of the hash table.^[4]

See also

• Chromaprint
• Automatic content recognition
• Digital video fingerprinting
• Feature extraction
• Parsons code
• Perceptual hashing
• Search by sound
• Sound recognition

References

External links

• A Review of Algorithms for Audio Fingerprinting (P. Cano et al. In International Workshop on Multimedia Signal Processing, US Virgin Islands, December 2002)
• Content-Based Retrieval of Music and Audio by Jonathan Foote, ISS, National University of Singapore.