Automatic music transcription for traditional woodwind instruments sopele
•Prospects of sopele woodwind instrument AMT are inspected on a newly acquired dataset.•Unwanted pitch variation is mitigated using DFT and supervised machine learning.•DFT-coupled RF and CNN models achieve F1=0.92 in the polyphonic setup.•A full-stack system for effortless music preservation of sop...
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| Vydáno v: | Pattern recognition letters Ročník 128; s. 340 - 347 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Amsterdam
Elsevier B.V
01.12.2019
Elsevier Science Ltd |
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| ISSN: | 0167-8655, 1872-7344 |
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| Abstract | •Prospects of sopele woodwind instrument AMT are inspected on a newly acquired dataset.•Unwanted pitch variation is mitigated using DFT and supervised machine learning.•DFT-coupled RF and CNN models achieve F1=0.92 in the polyphonic setup.•A full-stack system for effortless music preservation of sopele pieces is presented.•The system performs reasonably well for transcribing sopele traditional music pieces.
Sopela is a traditional hand-made woodwind instrument, commonly played in pair, characteristic to the Istrian peninsula in western Croatia. Its piercing sound, accompanied by two-part singing in the hexatonic Istrian scale, is registered in the UNESCO Representative List of the Intangible Cultural Heritage of Humanity. This paper presents an insight study of automatic music transcription (AMT) for sopele tunes. The process of converting audio inputs into human-readable musical scores involves multi-pitch detection and note tracking. The proposed solution supports this process by utilising frequency-feature extraction, supervised machine learning (ML) algorithms, and postprocessing heuristics. We determined the most favourable tone-predicting model by applying grid search for two state-of-the-art ML techniques, optionally coupled with frequency-feature extraction. The model achieved promising transcription accuracy for both monophonic and polyphonic music sources encompassed in the originally developed dataset. In addition, we developed a proof-of-concept AMT system, comprised of a client mobile application and a server-side API. While the mobile application records, tags and uploads audio sources, the back-end server applies the presented procedure for converting recorded music into a common notation to be delivered as a transcription result. We thus demonstrate how collecting and preserving traditional sopele music, performed in real-life occasions, can be effortlessly accomplished on-the-go. |
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| AbstractList | Sopela is a traditional hand-made woodwind instrument, commonly played in pair, characteristic to the Istrian peninsula in western Croatia. Its piercing sound, accompanied by two-part singing in the hexatonic Istrian scale, is registered in the UNESCO Representative List of the Intangible Cultural Heritage of Humanity. This paper presents an insight study of automatic music transcription (AMT) for sopele tunes. The process of converting audio inputs into human-readable musical scores involves multi-pitch detection and note tracking. The proposed solution supports this process by utilising frequency-feature extraction, supervised machine learning (ML) algorithms, and postprocessing heuristics. We determined the most favourable tone-predicting model by applying grid search for two state-of-the-art ML techniques, optionally coupled with frequency-feature extraction. The model achieved promising transcription accuracy for both monophonic and polyphonic music sources encompassed in the originally developed dataset. In addition, we developed a proof-of-concept AMT system, comprised of a client mobile application and a server-side API. While the mobile application records, tags and uploads audio sources, the back-end server applies the presented procedure for converting recorded music into a common notation to be delivered as a transcription result. We thus demonstrate how collecting and preserving traditional sopele music, performed in real-life occasions, can be effortlessly accomplished on-the-go. •Prospects of sopele woodwind instrument AMT are inspected on a newly acquired dataset.•Unwanted pitch variation is mitigated using DFT and supervised machine learning.•DFT-coupled RF and CNN models achieve F1=0.92 in the polyphonic setup.•A full-stack system for effortless music preservation of sopele pieces is presented.•The system performs reasonably well for transcribing sopele traditional music pieces. Sopela is a traditional hand-made woodwind instrument, commonly played in pair, characteristic to the Istrian peninsula in western Croatia. Its piercing sound, accompanied by two-part singing in the hexatonic Istrian scale, is registered in the UNESCO Representative List of the Intangible Cultural Heritage of Humanity. This paper presents an insight study of automatic music transcription (AMT) for sopele tunes. The process of converting audio inputs into human-readable musical scores involves multi-pitch detection and note tracking. The proposed solution supports this process by utilising frequency-feature extraction, supervised machine learning (ML) algorithms, and postprocessing heuristics. We determined the most favourable tone-predicting model by applying grid search for two state-of-the-art ML techniques, optionally coupled with frequency-feature extraction. The model achieved promising transcription accuracy for both monophonic and polyphonic music sources encompassed in the originally developed dataset. In addition, we developed a proof-of-concept AMT system, comprised of a client mobile application and a server-side API. While the mobile application records, tags and uploads audio sources, the back-end server applies the presented procedure for converting recorded music into a common notation to be delivered as a transcription result. We thus demonstrate how collecting and preserving traditional sopele music, performed in real-life occasions, can be effortlessly accomplished on-the-go. |
| Author | Lerga, Jonatan Ljubic, Sandi Štajduhar, Ivan Skoki, Arian |
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| Cites_doi | 10.1023/A:1010933404324 10.1109/TASL.2011.2174227 10.1016/j.ipm.2009.03.002 10.1007/s10844-013-0258-3 10.1109/TASLP.2016.2533858 10.1109/TASLP.2016.2515514 10.1109/TASLP.2014.2387388 10.1162/COMJ_a_00146 10.1109/TASL.2009.2034186 10.1109/TASLP.2018.2830113 10.1080/09298215.2018.1451546 10.1109/TASLP.2016.2598305 |
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| Keywords | Sopele Traditional woodwind instrument Automatic music transcription Discrete Fourier transform Machine learning |
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| Snippet | •Prospects of sopele woodwind instrument AMT are inspected on a newly acquired dataset.•Unwanted pitch variation is mitigated using DFT and supervised machine... Sopela is a traditional hand-made woodwind instrument, commonly played in pair, characteristic to the Istrian peninsula in western Croatia. Its piercing sound,... |
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| SubjectTerms | Algorithms Applications programs Automatic music transcription Cultural heritage Cultural resources Discrete Fourier transform Feature extraction Frequency Learning algorithms Machine learning Mobile computing Model accuracy Musical scores Piercing Pitch Problem solving Singing Sopele Traditional woodwind instrument Transcription Woodwind music |
| Title | Automatic music transcription for traditional woodwind instruments sopele |
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