Transcription of Polyphonic Vocal Music with a Repetitive Melodic Structure

Bohak C, Marolt M. Transcription of Polyphonic Vocal Music with a Repetitive Melodic Structure. J Audio Eng Soc. 2016;64(9):664-672. doi:10.17743/jaes.2016.0033

@article{Bohak2016_18375,
  author = {Bohak, Ciril and Marolt, Matija},
  title = {{Transcription of Polyphonic Vocal Music with a Repetitive Melodic Structure}},
  journal = {Journal of the Audio Engineering Society},
  volume = {64},
  number = {9},
  pages = {664--672},
  year = {2016},
  month = sep,
  publisher = {Audio Engineering Society},
  doi = {10.17743/jaes.2016.0033},
  url = {https://doi.org/10.17743/jaes.2016.0033}
}

TY  - JOUR
TI  - Transcription of Polyphonic Vocal Music with a Repetitive Melodic Structure
AU  - Bohak, Ciril
AU  - Marolt, Matija
T2  - Journal of the Audio Engineering Society
J2  - J. Audio Eng. Soc.
VL  - 64
IS  - 9
SP  - 664
EP  - 672
PY  - 2016
DA  - 2016/09/06
DO  - 10.17743/jaes.2016.0033
UR  - https://doi.org/10.17743/jaes.2016.0033
PB  - Audio Engineering Society
LA  - en
AB  - Automatic music transcription transforms an acoustic music signal into a symbolic notation that typically involves the detection of multiple concurrent pitches, the detection of note onsets and offsets, as well as recognition of the instruments. This paper presents a novel method for transcribing folk music. In contrast to most commercial music, folk music recordings may contain various inaccuracies because they are usually performed by amateur musicians and recorded in the field. The proposed method fuses three sources of information: frame-based multiple F0 estimates, song structure, and pitch drift estimates. Using song structure can improve transcription accuracy. The method uses two strategies: exploiting repetitions aligned in the time and pitch domains for improving F0 estimates and incorporating a probabilistic model based on explicit duration hidden Markov models (EDHMM) to estimate notes from F0. A representative segment of the analyzed song is used to align other segments. Information from these segments is summarized and used in a two-layer probabilistic EDHMM to segment frame-based information into notes.
ER  -