Slizovskaia, OlgaHaro Ortega, GloriaGómez Gutiérrez, Emilia, 1975-2021-05-312021Slizovskaia O, Haro G, Gomez E. Conditioned source separation for musical instrument performances. IEEE/ACM Trans. Audio, Speech, Language Process. 2021;29:2083-95. DOI: 10.1109/TASLP.2021.30823312329-9290http://hdl.handle.net/10230/47693In music source separation, the number of sources may vary for each piece and some of the sources may belong to the same family of instruments, thus sharing timbral characteristics and making the sources more correlated. This leads to additional challenges in the source separation problem. This paper proposes a source separation method for multiple musical instruments sounding simultaneously and explores how much additional information apart from the audio stream can lift the quality of source separation. We explore conditioning techniques at different levels of a primary source separation network and utilize two extra modalities of data, namely presence or absence of instruments in the mixture, and the corresponding video stream data.application/pdfeng© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/TASLP.2021.3082331info:eu-repo/semantics/articlehttp://dx.doi.org/10.1109/TASLP.2021.3082331Single Channel Source SeparationAudio-Visual AnalysisConditioned Neural Networksinfo:eu-repo/semantics/openAccess