An audio fingerprinting system for live version identification using image processing techniques

Suppose that you are at a music festival checking on an artist, and you would like to quickly know about the song that is being played (e.g., title, lyrics, album, etc.). If you have a smartphone, you could record a sample of the live performance and compare it against a database of existing recordings from the artist. Services such as Shazam or SoundHound will not work here, as this is not the typical framework for audio fingerprinting or query-by-humming systems, as a live performance is neither identical to its studio version (e.g., variations in instrumentation, key, tempo, etc.) nor it is a hummed or sung melody. We propose an audio fingerprinting system that can deal with live version identification by using image processing techniques. Compact fingerprints are derived using a log-frequency spectrogram and an adaptive thresholding method, and template matching is performed using the Hamming similarity and the Hough Transform.