Discovery of syllabic percussion patterns in tabla solo recordings

We address the unexplored problem of percussion pattern/ndiscovery in Indian art music. Percussion in Indian art music uses onomatopoeic oral mnemonic syllables for the transmission of repertoire and technique. This is utilized for the/ntask of percussion pattern discovery from audio recordings./nFrom a parallel corpus of audio and expert curated scores/nfor 38 tabla solo recordings, we use the scores to build a/nset of most frequent syllabic patterns of different lengths./nFrom this set, we manually select a subset of musically representative query patterns. To discover these query patterns/nin an audio recording, we use syllable-level hidden Markov/nmodels (HMM) to automatically transcribe the recording/ninto a syllable sequence, in which we search for the query/npattern instances using a Rough Longest Common Subsequence (RLCS) approach. We show that the use of RLCS/nmakes the approach robust to errors in automatic transcrip-/ntion, significantly improving the pattern recall rate and F-/nmeasure. We further propose possible enhancements to improve the results.This work is partly supported by the European Research/nCouncil under the European Union’s Seventh Framework/nProgram, as a part of the CompMusic project (ERC grant/nagreement 267583

Discovery of syllabic percussion patterns in tabla solo recordings

We address the unexplored problem of percussion pattern/ndiscovery in Indian art music. Percussion in Indian art music uses onomatopoeic oral mnemonic syllables for the transmission of repertoire and technique. This is utilized for the/ntask of percussion pattern discovery from audio recordings./nFrom a parallel corpus of audio and expert curated scores/nfor 38 tabla solo recordings, we use the scores to build a/nset of most frequent syllabic patterns of different lengths./nFrom this set, we manually select a subset of musically representative query patterns. To discover these query patterns/nin an audio recording, we use syllable-level hidden Markov/nmodels (HMM) to automatically transcribe the recording/ninto a syllable sequence, in which we search for the query/npattern instances using a Rough Longest Common Subsequence (RLCS) approach. We show that the use of RLCS/nmakes the approach robust to errors in automatic transcrip-/ntion, significantly improving the pattern recall rate and F-/nmeasure. We further propose possible enhancements to improve the results.This work is partly supported by the European Research/nCouncil under the European Union’s Seventh Framework/nProgram, as a part of the CompMusic project (ERC grant/nagreement 267583