IEEE Transactions on Multimedia · 2005 · 266 citations · 16 references
MusicEngineeringQuantized ChromagramMusicologySpeech RecognitionInformation RetrievalData ScienceData MiningPattern RecognitionOptical Music RecognitionMultimedia MiningAudio ThumbnailingAudio RetrievalComputer SciencePattern Recognition SystemPopular MusicAudio MiningMusic ClassificationArts
Rapid browsing of large multimedia databases is increasingly important, yet existing methods are media‑dependent. The authors develop a system that generates short audio thumbnails for popular music tracks. The system detects structural redundancy such as choruses by applying a chromagram‑based pattern‑recognition algorithm that quantizes spectral energy into 12 pitch classes and evaluates candidate segments against ideal thumbnail locations. The method achieves high accuracy, with most errors arising from songs that lack the expected structural patterns.
With the growing prevalence of large databases of multimedia content, methods for facilitating rapid browsing of such databases or the results of a database search are becoming increasingly important. However, these methods are necessarily media dependent. We present a system for producing short, representative samples (or "audio thumbnails") of selections of popular music. The system searches for structural redundancy within a given song with the aim of identifying something like a chorus or refrain. To isolate a useful class of features for performing such structure-based pattern recognition, we present a development of the chromagram, a variation on traditional time-frequency distributions that seeks to represent the cyclic attribute of pitch perception, known as chroma. The pattern recognition system itself employs a quantized chromagram that represents the spectral energy at each of the 12 pitch classes. We evaluate the system on a database of popular music and score its performance against a set of "ideal" thumbnail locations. Overall performance is found to be quite good, with the majority of errors resulting from songs that do not meet our structural assumptions.
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Content-based classification, search, and retrieval of audio
Erling Wold, Thom Blum, Douglas Keislar et al. · IEEE Multimedia · 1996 · 792 citations
Automatic audio segmentation using a measure of audio novelty
Jonathan Foote · 2002 · 405 citations