A Segment-Based Fitness Measure for Capturing Repetitive Structures of Music Recordings

In this paper, we deal with the task of determining the audio segment that best represents a given music recording (similar to audio thumbnailing). Typically, such a segment has many (approximate) repetitions covering large parts of the music recording. As main contribution, we introduce a novel fitness measure that assigns to each segment a fitness value that expresses how much and how well the segment “explains” the repetitive structure of the recording. In combination with enhanced feature representations, we show that our fitness measure can cope even with strong variations in tempo, instrumentation, and modulations that may occur within and across related segments. We demonstrate the practicability of our approach by means of several challenging examples including field recordings of folk music and recordings of classical music.