02829nas a2200421   4500000000100000000000100001008004100002260000800043653004500051653002000096653003400116653003100150653003200181653001500213653002300228653003400251653002100285653002100306653002400327653002100351653002100372653002200393653001100415653002400426653003200450653003600482653002500518100001600543700001700559700001800576700001900594245007800613856015300691300001200844490000800856520152300864022002002387       2019                            d  cdec10aApplication programming interfaces (API)10aAudio acoustics10aAutomatic music transcription10aDiscrete Fourier transform10aDiscrete Fourier transforms10aExtraction10aFeature extraction10aIntangible cultural heritages10aLearning systems10amachine learning10aMobile applications10aMobile computing10aPolyphonic music10aPredicting models10aSopele10aSupervised learning10aSupervised machine learning10aTraditional woodwind instrument10aWoodwind instruments1 aArian Skoki1 aSandi Ljubic1 aJonatan Lerga1 aIvan Stajduhar00aAutomatic music transcription for traditional woodwind instruments sopele  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85072880061&doi=10.1016%2fj.patrec.2019.09.024&partnerID=40&md5=6c8f0da0e86aefbe92e4cfe28e302e84  a340-3470 v1283 aSopela 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.  a01678655 (ISSN)