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dc.contributor.author Tamer, Nazif C
dc.contributor.author Özer, Yigitcan
dc.contributor.author Müller, Meinard
dc.contributor.author Serra, Xavier
dc.date.accessioned 2023-04-25T13:55:00Z
dc.date.available 2023-04-25T13:55:00Z
dc.date.issued 2023-04-25
dc.identifier.uri http://hdl.handle.net/10230/56565
dc.description This work has been accepted at the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023), at Rhodes, Greece. June 4-10, 2023.
dc.description.abstract Pitch estimation of a target musical source within a multi-source polyphonic signal is of great interest for music performance analysis. One possible approach for extracting the pitch of a target source is to first perform source separation and then estimate the pitch of the separated track. However, as we will show, this typically leads to poor results. As an alternative to this approach, we introduce a timbre-aware pitch estimator (TAPE), which estimates the pitch of a target source in an end-to-end manner without the need for an explicit source separation step. Opposed to existing approaches that assume the predominance of a lead voice, our approach builds upon other cues that only rely on the timbral characteristics. Our results on real violin-piano duets show that, without any pre-processing step, TAPE outperforms the sequential procedure of source separation and pitch estimation under many settings, even if the target source is not predominant.
dc.description.sponsorship This research is funded by the project Musical AI - PID2019-111403GB-I00/AEI/10.13039/501100011033 funded by the Spanish Ministerio de Ciencia, Innovacion y Universidades (MCIU) and the Agencia Estatal de Investigacion (AEI), and by the German Research Foundation (DFG MU 2686/10-2).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.title TAPE: An End-to-End Timbre-Aware Pitch Estimator
dc.type info:eu-repo/semantics/preprint
dc.subject.keyword Pitch Estimation
dc.subject.keyword Audio Source Separation,
dc.subject.keyword Music Performance Analysis
dc.subject.keyword Weakly Supervised Learning
dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2019-111403GB-I00
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/submittedVersion

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