This landmark-based audio fingerprinting system is able to match short, noisy snippets to a reference database in near-constant time.
This is my implementation of the music audio matching algorithm developed by Avery Wang for the Shazam service. Shazam can identify apparently any commercial music track from a short snippet recorded via your cell phone in a noisy bar. I don't have the database to check if my version is quite that good, but it is able to rapidly match and locate a poor-quality excerpt from within a database of (at least) hundreds of tracks.
See http://labrosa.ee.columbia.edu/~dpwe/resources/matlab/fingerprint/ for the "published" output of the demo script.
Notes for running under Windows (from Rob Macrae) are at http://labrosa.ee.columbia.edu/matlab/fingerprint/windows-notes.txt .
Dan Ellis (2022). Robust Landmark-Based Audio Fingerprinting (https://www.mathworks.com/matlabcentral/fileexchange/23332-robust-landmark-based-audio-fingerprinting), MATLAB Central File Exchange. Retrieved .
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