Bridging the Audio-Symbolic Gap: The Discovery of Repeated Note Content Directly from Polyphonic Music Audio
Algorithms for the discovery of musical repetition have been developed in audio and symbolic domains more or less independently for over a decade. In this paper we combine algorithms for multiple F0 estimation, beat tracking, quantisation, and pattern discovery, so that for the first time, the note content of motifs, themes, and repeated sections can be discovered directly from polyphonic music audio. Testing on deadpan and expressive piano renditions of pieces, we compared pattern discovery performance against runs on symbolic representations of the same pieces. Comparing deadpan audio with deadpan-symbolic representations, establishment precision and recall fell by ~25%, and by ~50% when comparing expressive audio with deadpan-symbolic representations. The music data and evaluation results establish a benchmark for future work that attempts to bridge the audio-symbolic gap.
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