Development of a sound coding strategy based on a deep recurrent neural network for monaural source separation in cochlear implants

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To access the full text documents, please follow this link: http://hdl.handle.net/10230/33115

dc.contributor.author	Nogueira, Waldo
dc.contributor.author	Gajecki, Tom
dc.contributor.author	Krüger, Benjamin
dc.contributor.author	Janer Mestres, Jordi
dc.contributor.author	Büchner, Andreas
dc.date	2016
dc.identifier.citation	Nogueira W, Gajecki T, Krueger B, Janer J, Buechner A. Development of a sound coding strategy based on a deep recurrent neural network for monaural source separation in cochlear implants. In: 12th ITG Conference on Speech Communication; 2016 Oct 5-7; Paderborn, Germany. Berlin (Germany): IEEE; 2016. p. 155-9.
dc.identifier.uri	http://hdl.handle.net/10230/33115
dc.format	application/pdf
dc.language.iso	eng
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation	12th ITG Conference on Speech Communication; 2016 Oct 5-7; Paderborn, Germany. Berlin (Germany): IEEE; 2016. p. 155-9.
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	© 20165 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. The final published article can be found at http://ieeexplore.ieee.org/document/7776166/
dc.subject	Música -- Anàlisi
dc.title	Development of a sound coding strategy based on a deep recurrent neural network for monaural source separation in cochlear implants
dc.type	info:eu-repo/semantics/conferenceObject
dc.type	info:eu-repo/semantics/acceptedVersion
dc.description.abstract	Comunicació presentada a la 12th ITG Conference on Speech Communication, celebrada els dies 5 a 7 d'octubre de 2016 a Paderborn, Alemanya.
dc.description.abstract	The aim of this study is to investigate whether a source separation algorithm based on a deep recurrent neural network (DRNN) can provide a speech perception benefit for cochlear implant users when speech signals are mixed with another competing voice. The DRNN is based on an existing architecture that is used in combination with an extra masking layer for optimization. The approach has been evaluated using the HSM sentence test (male voice) mixed with a competing voice (female voice) for a monaural speech separation task. Two DRNNs with two levels of complexity have been used. The algorithms have been evaluated in 8 normal hearing listeners using a Vocoder and in 3 CI users. Both DRNNs show a large and significant improvement in speech intelligibility using Vocoded speech. Preliminary results in 3 CI users seem to confirm the improvement observed using Vocoded simulations.
dc.description.abstract	This work was supported by the DFG Cluster of Excellence EXC 1077/1 Hearing4all.

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