This HTML5 document contains 32 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
dctermshttp://purl.org/dc/terms/
n6https://kar.kent.ac.uk/id/eprint/71467#
n2https://kar.kent.ac.uk/id/eprint/
wdrshttp://www.w3.org/2007/05/powder-s#
n21http://purl.org/ontology/bibo/status/
dchttp://purl.org/dc/elements/1.1/
n12https://kar.kent.ac.uk/id/subject/
rdfshttp://www.w3.org/2000/01/rdf-schema#
n19doi:10.1109/
n17https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/
n10http://eprints.org/ontology/
bibohttp://purl.org/ontology/bibo/
n20https://kar.kent.ac.uk/id/publication/
n9https://kar.kent.ac.uk/id/org/
n4https://kar.kent.ac.uk/71467/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
owlhttp://www.w3.org/2002/07/owl#
n11https://kar.kent.ac.uk/id/document/
n13https://kar.kent.ac.uk/id/
xsdhhttp://www.w3.org/2001/XMLSchema#
n16https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/
n8https://kar.kent.ac.uk/id/person/

Statements

Subject Item
n2:71467
rdf:type
bibo:AcademicArticle bibo:Article n10:ArticleEPrint n10:EPrint
rdfs:seeAlso
n4:
owl:sameAs
n19:TASLP.2019.2892241
n10:hasAccepted
n11:3144966
n10:hasDocument
n11:3161247 n11:3161248 n11:3161249 n11:3161250 n11:3144966 n11:3144967
dc:hasVersion
n11:3144966
dcterms:title
Listening and grouping: an online autoregressive approach for monaural speech separation
wdrs:describedby
n16:export_kar_RDFN3.n3 n17:export_kar_RDFN3.n3
dcterms:date
2019-01-10
dcterms:creator
n8:ext-i.v.mcloughlin@kent.ac.uk n8:ext-e42ece4800e904707b920ac07dd53519 n8:ext-3a2bd7709af45d1888f76d56b164ba5e n8:ext-dd64b2348f561f9d69de4aa14b85cab6
bibo:status
n21:peerReviewed n21:published
dcterms:publisher
n9:ext-af0a9a5baed87c407844a3f5db44597c
bibo:abstract
This paper proposes an autoregressive approach to harness the power of deep learning for multi-speaker monaural speech separation. It exploits a causal temporal context in both mixture and past estimated separated signals and performs online separation that is compatible with real-time applications. The approach adopts a learned listening and grouping architecture motivated by computational auditory scene analysis, with a grouping stage that effectively addresses the label permutation problem at both frame and segment levels. Experimental results on the benchmark WSJ0-2mix dataset show that the new approach can outperform the majority of state-of-the-art methods in both closed-set and open-set conditions in terms of signal-to-distortion ratio (SDR) improvement and perceptual evaluation of speech quality (PESQ), even approaches that exploit whole-utterance statistics for separation, with relatively fewer model parameters.
dcterms:isPartOf
n13:repository n20:ext-15587916
dcterms:subject
n12:T
bibo:authorList
n6:authors
bibo:issue
4
bibo:volume
27