{"id":"https://openalex.org/W4414761927","doi":"https://doi.org/10.48550/arxiv.2509.05079","title":"Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns","display_name":"Lightweight DNN for Full-Band Speech Denoising on Mobile Devices: Exploiting Long and Short Temporal Patterns","publication_year":2025,"publication_date":"2025-09-05","ids":{"openalex":"https://openalex.org/W4414761927","doi":"https://doi.org/10.48550/arxiv.2509.05079"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2509.05079","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.05079","pdf_url":"https://arxiv.org/pdf/2509.05079","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2509.05079","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5108358814","display_name":"Konstantinos Drossos","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Drossos, Konstantinos","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078854716","display_name":"Mikko Heikkinen","orcid":"https://orcid.org/0000-0001-9898-1916"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Heikkinen, Mikko","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5058998799","display_name":"Paschalis Tsiaflakis","orcid":"https://orcid.org/0000-0001-6085-3740"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tsiaflakis, Paschalis","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5108358814"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10860","display_name":"Speech and Audio Processing","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":0.9944999814033508,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10901","display_name":"Advanced Data Compression Techniques","score":0.9584000110626221,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.6301000118255615},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5547000169754028},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42800000309944153},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4180000126361847},{"id":"https://openalex.org/keywords/latency","display_name":"Latency (audio)","score":0.41679999232292175},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.3937000036239624},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.38589999079704285},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.38510000705718994}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8230999708175659},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.6301000118255615},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5547000169754028},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.5295000076293945},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5033000111579895},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42800000309944153},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4180000126361847},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.41679999232292175},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.3937000036239624},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.38589999079704285},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.38510000705718994},{"id":"https://openalex.org/C2776182073","wikidata":"https://www.wikidata.org/wiki/Q7575395","display_name":"Speech enhancement","level":3,"score":0.3174000084400177},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.31380000710487366},{"id":"https://openalex.org/C61328038","wikidata":"https://www.wikidata.org/wiki/Q3358061","display_name":"Speech processing","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.298799991607666},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.2976999878883362},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.29350000619888306},{"id":"https://openalex.org/C2779843651","wikidata":"https://www.wikidata.org/wiki/Q7390335","display_name":"SIGNAL (programming language)","level":2,"score":0.2896000146865845},{"id":"https://openalex.org/C29265498","wikidata":"https://www.wikidata.org/wiki/Q7047719","display_name":"Noise measurement","level":3,"score":0.2786000072956085},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.27619999647140503},{"id":"https://openalex.org/C142433447","wikidata":"https://www.wikidata.org/wiki/Q7806653","display_name":"Time\u2013frequency analysis","level":3,"score":0.2685000002384186},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2651999890804291},{"id":"https://openalex.org/C46637626","wikidata":"https://www.wikidata.org/wiki/Q6693015","display_name":"Low latency (capital markets)","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C204201278","wikidata":"https://www.wikidata.org/wiki/Q1332614","display_name":"Voice activity detection","level":3,"score":0.26100000739097595},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2506999969482422}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2509.05079","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.05079","pdf_url":"https://arxiv.org/pdf/2509.05079","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2509.05079","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2509.05079","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2509.05079","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2509.05079","pdf_url":"https://arxiv.org/pdf/2509.05079","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Speech":[0],"denoising":[1,126,178],"(SD)":[2],"is":[3,95,173],"an":[4,138,189],"important":[5],"task":[6],"of":[7,107],"many,":[8],"if":[9],"not":[10,54],"all,":[11],"modern":[12,167],"signal":[13,123],"processing":[14],"chains":[15],"used":[16],"in":[17,121,187],"devices":[18],"and":[19,28,79,89,110,117,124,156,180,196],"for":[20,35,40,51,83,114,153],"everyday-life":[21],"applications.":[22],"While":[23],"there":[24],"are":[25,38,53],"many":[26],"published":[27],"powerful":[29],"deep":[30],"neural":[31,112],"network":[32],"(DNN)-based":[33],"methods":[34,50],"SD,":[36,85],"few":[37],"optimized":[39],"resource-constrained":[41],"platforms":[42],"such":[43],"as":[44,137],"mobile":[45,168],"devices.":[46],"Additionally,":[47],"most":[48],"DNN-based":[49,81],"SD":[52,199],"focusing":[55],"on":[56,97,131,165],"full-band":[57,84],"(FB)":[58],"signals,":[59],"i.e.":[60],"having":[61],"48":[62],"kHz":[63],"sampling":[64],"rate,":[65],"and/or":[66],"low":[67,77,197],"latency":[68,198],"cases.":[69],"In":[70],"this":[71],"paper":[72],"we":[73],"present":[74],"a":[75,98,132,158,166],"causal,":[76],"latency,":[78],"lightweight":[80],"method":[82,94,172],"leveraging":[86],"both":[87],"short":[88,116],"long":[90,118],"temporal":[91,105,119],"patterns.":[92],"The":[93,128,170],"based":[96],"modified":[99],"UNet":[100],"architecture":[101],"employing":[102],"look-back":[103],"frames,":[104],"spanning":[106],"convolutional":[108],"kernels,":[109],"recurrent":[111],"networks":[113],"exploiting":[115],"patterns":[120],"the":[122,140],"estimated":[125],"mask.":[127],"DNN":[129],"operates":[130],"causal":[133,150],"frame-by-frame":[134],"basis":[135],"taking":[136],"input":[139],"STFT":[141],"magnitude,":[142],"utilizes":[143],"inverted":[144],"bottlenecks":[145],"inspired":[146],"by":[147],"MobileNet,":[148],"employs":[149],"instance":[151],"normalization":[152],"channel-wise":[154],"normalization,":[155],"achieves":[157],"real-time":[159],"factor":[160],"below":[161],"0.02":[162],"when":[163],"deployed":[164],"phone.":[169],"proposed":[171],"evaluated":[174],"using":[175],"established":[176],"speech":[177],"metrics":[179],"publicly":[181],"available":[182],"datasets,":[183],"demonstrating":[184],"its":[185],"effectiveness":[186],"achieving":[188],"(SI-)SDR":[190],"value":[191],"that":[192],"outperforms":[193],"existing":[194],"FB":[195],"methods.":[200]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}