{"id":"https://openalex.org/W7125391757","doi":"https://doi.org/10.48550/arxiv.2601.14786","title":"Training-Efficient Text-to-Music Generation with State-Space Modeling","display_name":"Training-Efficient Text-to-Music Generation with State-Space Modeling","publication_year":2026,"publication_date":"2026-01-21","ids":{"openalex":"https://openalex.org/W7125391757","doi":"https://doi.org/10.48550/arxiv.2601.14786"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2601.14786","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.14786","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2601.14786","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5123578732","display_name":"Wei-Jaw Lee","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lee, Wei-Jaw","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5120340680","display_name":"Fang-Chih Hsieh","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hsieh, Fang-Chih","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123585629","display_name":"Xuanjun Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xuanjun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032009257","display_name":"F.P. Tsai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tsai, Fang-Duo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5123554474","display_name":"Yi-Hsuan Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Yi-Hsuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T11349","display_name":"Music Technology and Sound Studies","score":0.453000009059906,"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"}},"topics":[{"id":"https://openalex.org/T11349","display_name":"Music Technology and Sound Studies","score":0.453000009059906,"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"}},{"id":"https://openalex.org/T11309","display_name":"Music and Audio Processing","score":0.10080000013113022,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.07970000058412552,"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/transformer","display_name":"Transformer","score":0.7621999979019165},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5108000040054321},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.4853000044822693},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.43309998512268066},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.41130000352859497},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.3968000113964081},{"id":"https://openalex.org/keywords/software-portability","display_name":"Software portability","score":0.38339999318122864}],"concepts":[{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.7621999979019165},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7049999833106995},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5108000040054321},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.4853000044822693},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.43309998512268066},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.41130000352859497},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40700000524520874},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3968000113964081},{"id":"https://openalex.org/C63000827","wikidata":"https://www.wikidata.org/wiki/Q3080428","display_name":"Software portability","level":2,"score":0.38339999318122864},{"id":"https://openalex.org/C2781162219","wikidata":"https://www.wikidata.org/wiki/Q26250693","display_name":"Replicate","level":2,"score":0.36480000615119934},{"id":"https://openalex.org/C2780440489","wikidata":"https://www.wikidata.org/wiki/Q5227278","display_name":"Data-driven","level":2,"score":0.3644999861717224},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35440000891685486},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C2781235140","wikidata":"https://www.wikidata.org/wiki/Q275131","display_name":"Scratch","level":2,"score":0.27790001034736633},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.27070000767707825},{"id":"https://openalex.org/C548217200","wikidata":"https://www.wikidata.org/wiki/Q251","display_name":"Java","level":2,"score":0.26440000534057617},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.26190000772476196},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.2596000134944916},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.25940001010894775},{"id":"https://openalex.org/C84976871","wikidata":"https://www.wikidata.org/wiki/Q2015673","display_name":"Openness to experience","level":2,"score":0.2563999891281128}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2601.14786","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.14786","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2601.14786","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2601.14786","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"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":{"Recent":[0],"advances":[1],"in":[2,58,173,208],"text-to-music":[3],"generation":[4],"(TTM)":[5],"have":[6],"yielded":[7],"high-quality":[8],"results,":[9],"but":[10],"often":[11],"at":[12,202],"the":[13,19,28,53,59,66,79,145,154,159,165,198,203,211,222,227,240],"cost":[14],"of":[15,21,32,55,65,82,123,153,158,224],"extensive":[16],"compute":[17],"and":[18,30,44,73,95,156,177,232],"use":[20],"large":[22],"proprietary":[23],"internal":[24],"data.":[25],"To":[26,220],"improve":[27],"affordability":[29],"openness":[31],"TTM":[33,225],"training,":[34],"an":[35],"open-source":[36],"generative":[37,60],"model":[38,61,169,212,230],"backbone":[39,77],"that":[40,64,137,190],"is":[41,46,214],"more":[42],"training-":[43],"data-efficient":[45],"needed.":[47],"In":[48],"this":[49],"paper,":[50],"we":[51,87,135],"constrain":[52],"number":[54],"trainable":[56],"parameters":[57],"to":[62,144,164,197,216],"match":[63],"MusicGen-small":[67,166],"benchmark":[68],"(with":[69],"about":[70],"300M":[71],"parameters),":[72],"replace":[74],"its":[75],"Transformer":[76,146,199],"with":[78,101],"emerging":[80],"class":[81],"state-space":[83],"models":[84,110],"(SSMs).":[85],"Specifically,":[86],"explore":[88],"different":[89],"SSM":[90],"variants":[91],"for":[92],"sequence":[93],"modeling,":[94],"compare":[96],"a":[97,102,116],"single-stage":[98],"SSM-based":[99],"design":[100],"decomposable":[103],"two-stage":[104],"SSM/diffusion":[105],"hybrid":[106],"design.":[107],"All":[108],"proposed":[109],"are":[111,132,235],"trained":[112],"from":[113],"scratch":[114],"on":[115,182,237],"purely":[117],"public":[118],"dataset":[119],"comprising":[120],"457":[121],"hours":[122],"CC-licensed":[124],"music,":[125],"ensuring":[126],"full":[127],"openness.":[128],"Our":[129],"experimental":[130],"findings":[131],"three-fold.":[133],"First,":[134],"show":[136],"SSMs":[138,191],"exhibit":[139],"superior":[140],"training":[141,160,205],"efficiency":[142],"compared":[143,163],"counterpart.":[147],"Second,":[148],"despite":[149],"using":[150],"only":[151],"9%":[152],"FLOPs":[155],"2%":[157],"data":[161],"size":[162,213],"benchmark,":[167],"our":[168,186],"achieves":[170],"competitive":[171,194],"performance":[172,195],"both":[174],"objective":[175],"metrics":[176],"subjective":[178],"listening":[179],"tests":[180],"based":[181],"MusicCaps":[183],"captions.":[184],"Finally,":[185],"scaling-down":[187],"experiment":[188],"demonstrates":[189],"can":[192],"maintain":[193],"relative":[196],"baseline":[200],"even":[201],"same":[204],"budget":[206],"(measured":[207],"iterations),":[209],"when":[210],"reduced":[215],"four":[217],"times":[218],"smaller.":[219],"facilitate":[221],"democratization":[223],"research,":[226],"processed":[228],"captions,":[229],"checkpoints,":[231],"source":[233],"code":[234],"available":[236],"GitHub":[238],"via":[239],"project":[241],"page:":[242],"https://lonian6.github.io/ssmttm/.":[243]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-01-23T00:00:00"}
