AI

The Intervalgram: An Audio Feature for Large-Scale Cover-Song Recognition

Abstract

We present a system for representing the musical content of short pieces of audio using a novel chroma-based representation known as the ‘intervalgram’, which is a summary of the local pattern of musical intervals in a segment of music. The intervalgram is based on a chroma representation derived from the temporal profile of the stabilized auditory image [10] and is made locally pitch invariant by means of a ‘soft’ pitch transposition to a local reference. Intervalgrams are generated for a piece of music using multiple overlapping windows. These sets of intervalgrams are used as the basis of a system for detection of identical melodic and harmonic progressions in a database of music. Using a dynamic-programming approach for comparisons between a reference and the song database, performance is evaluated on the ‘covers80’ dataset [4]. A first test of an intervalgram-based system on this dataset yields a precision at top-1 of 53.8%, with an ROC curve that shows very high precision up to moderate recall, suggesting that the intervalgram is adept at identifying the easier-to-match cover songs in the dataset with high robustness. The intervalgram is designed to support locality-sensitive hashing, such that an index lookup from each single intervalgram feature has a moderate probability of retrieving a match, with few false matches. With this indexing approach, a large reference database can be quickly pruned before more detailed matching, as in previous content-identification systems.