Music source separation on classical guitar duets

Γλυτσός, Μάριος; Glytsos, Marios

dc.contributor.author	Γλυτσός, Μάριος	el
dc.contributor.author	Glytsos, Marios	en
dc.date.accessioned	2024-09-03T06:16:35Z
dc.date.available	2024-09-03T06:16:35Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/60093
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.27789
dc.rights	Default License
dc.subject	Ανάκτηση Μουσικής Πληροφορίας	el
dc.subject	Μουσική και Τεχνητή Νοημοσύνη	el
dc.subject	Διαχωρισμός Πηγών Μουσικής	el
dc.subject	Music Information Retrieval	en
dc.subject	Music and AI	en
dc.subject	Music Source Separation	en
dc.subject	Music Transcription	en
dc.subject	Monotimbral Music Source Separation	en
dc.subject	Διαχωρισμός Πηγών Μουσικής Κοινής Χροιάς	el
dc.subject	Μεταγραφή Μουσικής	el
dc.title	Music source separation on classical guitar duets	en
heal.type	bachelorThesis
heal.classification	Computer Science	en
heal.classification	Machine Learning	en
heal.language	el
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2024-03-28
heal.abstract	The dissertation presents an in-depth study on the separation of sources in classical guitar duets, addressing the unique challenge posed by the similar timbral characteristics of the instruments involved. This research introduces two new datasets comprised of real and synthetic recordings of guitar duets, designed to facilitate the exploration and evaluation of source separation techniques. Furthermore we propose a novel augmentation technique, OppositePanning, to enhance the separation process by exploiting the spatial distribution of sound, thereby offering a new avenue for improving source separation in settings where instruments share similar timbral characteristics. We propose a model pipeline which is is motivated by the understanding that guitar duet separation is inherently a hybrid task for humans. In practice, a human listener would naturally perceive the symbolic score from the audio, leveraging this score to aid in the separation process. This insight forms the foundation for the proposed dual-model pipeline, which aims to mimic this human approach by incorporating symbolic musical information directly into the separation algorithm. This approach is a significant departure from traditional source separation techniques, which primarily focus on the acoustic signal without considering the underlying musical structure. By employing a comparative analysis of Signal-to-Distortion Ratio (SDR) metrics, we evaluate the perfor- mance of the proposed dual-model pipeline against traditional methods. The findings demonstrate that incorporating symbolic musical information significantly improves separation accuracy, highlighting the im- portance of considering the musical context in source separation tasks. Moreover, this research posits that the methodologies and insights gained from the study of classical guitar duet separation could potentially be applied in other related fields as of speaker separation and voice singing separation, offering new perspectives and techniques for achieving more robust separation in complex auditory environments. The exploration of OppositePanning and the dual-model pipeline not only advances the understanding and methodology of monotimbral music source separation but also opens up avenues for further research in polyphonic music analysis and beyond, potentially leading to significant improvements in various applications of audio separation technology.	en
heal.advisorName	Μαραγκός, Πέτρος	el
heal.committeeMemberName	Μαραγκός, Πέτρος	el
heal.committeeMemberName	Ροντογιάννης, Αθανάσιος	el
heal.committeeMemberName	Ποταμιάνος, Αλέξανδρος	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών. Τομέας Σημάτων, Ελέγχου και Ρομποτικής	el
heal.academicPublisherID	ntua
heal.numberOfPages	131 σ.	el
heal.fullTextAvailability	false