{"id":"https://openalex.org/W7212291382","doi":"https://doi.org/10.48550/arxiv.2609.10826","title":"Processing and classifying bird songs using wavelet techniques and supervised learning","display_name":"Processing and classifying bird songs using wavelet techniques and supervised learning","publication_year":2026,"publication_date":"2026-09-09","ids":{"openalex":"https://openalex.org/W7212291382","doi":"https://doi.org/10.48550/arxiv.2609.10826"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2609.10826","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.10826","pdf_url":"https://arxiv.org/pdf/2609.10826","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2609.10826","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5151285715","display_name":"Laura Lucia Dominguez Barrios","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Barrios, Laura Lucia Dominguez","raw_affiliation_strings":["Department of Statistics , State University of Campinas , Campinas , Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics , State University of Campinas , Campinas , Brazil","institution_ids":["https://openalex.org/I181391015"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5081688441","display_name":"Federico Barrios","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Barrios, Fidel Aniano Causil","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044720728","display_name":"Alex Rodrigo dos Santos Sousa","orcid":"https://orcid.org/0000-0001-5887-3638","observed_orcids":["https://orcid.org/0000-0001-5887-3638"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sousa, Alex Rodrigo dos Santos","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5083493250","display_name":"Mariana Rodrigues Motta","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I181391015","display_name":"Universidade Estadual de Campinas (UNICAMP)","ror":"https://ror.org/04wffgt70","country_code":"BR","type":"education","lineage":["https://openalex.org/I181391015"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Motta, Mariana Rodrigues","raw_affiliation_strings":["Department of Statistics , State University of Campinas , Campinas , Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistics , State University of Campinas , Campinas , Brazil","institution_ids":["https://openalex.org/I181391015"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"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/T11309","display_name":"Music and Audio Processing","score":0.9922000169754028,"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/T11309","display_name":"Music and Audio Processing","score":0.9922000169754028,"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/T11665","display_name":"Animal Vocal Communication and Behavior","score":0.006800000090152025,"subfield":{"id":"https://openalex.org/subfields/1105","display_name":"Ecology, Evolution, Behavior and Systematics"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10201","display_name":"Speech Recognition and Synthesis","score":9.999999747378752e-05,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/invasive-birds","display_name":"invasive birds","score":0.9129999876022339},{"id":"https://openalex.org/keywords/bioacoustic-monitoring","display_name":"bioacoustic monitoring","score":0.7789999842643738},{"id":"https://openalex.org/keywords/wavelet-thresholding","display_name":"wavelet thresholding","score":0.7590000033378601},{"id":"https://openalex.org/keywords/environmental-noise","display_name":"environmental noise","score":0.6299999952316284},{"id":"https://openalex.org/keywords/mel-frequency-cepstral-coefficients","display_name":"Mel-frequency cepstral coefficients","score":0.8939999938011169},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"feature extraction","score":0.6129999756813049},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.7639999985694885},{"id":"https://openalex.org/keywords/random-forest","display_name":"random forest","score":0.8009999990463257},{"id":"https://openalex.org/keywords/multinomial-logit-model","display_name":"multinomial logistic regression","score":0.9940000176429749},{"id":"https://openalex.org/keywords/leiothrix-lutea","display_name":"Leiothrix lutea","score":0.9950000047683716},{"id":"https://openalex.org/keywords/passer-domesticus","display_name":"Passer domesticus","score":0.9980000257492065},{"id":"https://openalex.org/keywords/biological-invasion-management","display_name":"biological invasion management","score":0.7210000157356262}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6833000183105469},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6654999852180481},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6406000256538391},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5812000036239624},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.5781999826431274},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.46239998936653137},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.4447000026702881},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.43470001220703125},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4156999886035919},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.399399995803833},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.3950999975204468},{"id":"https://openalex.org/C151989614","wikidata":"https://www.wikidata.org/wiki/Q440370","display_name":"Mel-frequency cepstrum","level":3,"score":0.39410001039505005},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.38989999890327454},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3781999945640564},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.3714999854564667},{"id":"https://openalex.org/C104267543","wikidata":"https://www.wikidata.org/wiki/Q208163","display_name":"Signal processing","level":3,"score":0.36570000648498535},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.3628999888896942},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.34779998660087585},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.34599998593330383},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.3402000069618225},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3361000120639801},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.33390000462532043},{"id":"https://openalex.org/C46286280","wikidata":"https://www.wikidata.org/wiki/Q2414958","display_name":"Discrete wavelet transform","level":4,"score":0.3328000009059906},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.3244999945163727},{"id":"https://openalex.org/C117568660","wikidata":"https://www.wikidata.org/wiki/Q1650843","display_name":"Multinomial logistic regression","level":2,"score":0.3131999969482422},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28929999470710754},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.28380000591278076},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.25429999828338623}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2609.10826","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.10826","pdf_url":"https://arxiv.org/pdf/2609.10826","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2609.10826","doi":"https://doi.org/10.48550/arxiv.2609.10826","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2609.10826","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,"listed_in":[],"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":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2609.10826","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2609.10826","pdf_url":"https://arxiv.org/pdf/2609.10826","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","display_name":"Life on land","score":0.9599999785423279}],"study_designs":[],"awards":[{"id":"https://openalex.org/G5926969170","display_name":"Center for Plant Molecular Breeding (CeM\u00b2P)","funder_award_id":"22/04006-2","funder_id":"https://openalex.org/F4320320997","funder_display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo"}],"funders":[{"id":"https://openalex.org/F4320320997","display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","ror":"https://ror.org/02ddkpn78"},{"id":"https://openalex.org/F4320321091","display_name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","ror":"https://ror.org/00x0ma614"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7212291382.pdf","grobid_xml":"https://content.openalex.org/works/W7212291382.grobid-xml"},"referenced_works_count":1,"referenced_works":["https://openalex.org/W7125111791"],"related_works":["https://openalex.org/W4409919068","https://openalex.org/W4394674972","https://openalex.org/W3122040135","https://openalex.org/W7117368713","https://openalex.org/W2766040302","https://openalex.org/W7167667330","https://openalex.org/W3174327527","https://openalex.org/W2414102402","https://openalex.org/W7219553705","https://openalex.org/W3202820684"],"abstract_inverted_index":{"This":[0,156],"study":[1],"proposes":[2],"an":[3],"integrated":[4],"framework":[5],"for":[6,57,163],"the":[7,28,42,73,131,141,145,168],"processing":[8,58],"and":[9,53,80,98,104,116,167],"classification":[10,138],"of":[11,23,30,68,91,170],"invasive":[12],"bird":[13],"species":[14,70],"vocalizations":[15],"within":[16],"natural":[17],"soundscapes,":[18],"characterized":[19],"by":[20,33],"high":[21,54],"levels":[22],"environmental":[24],"noise.":[25],"We":[26],"address":[27],"challenge":[29],"signal":[31,84],"degradation":[32],"employing":[34],"a":[35,48,88,152,159],"Bayesian":[36],"wavelet":[37,133],"shrinkage":[38],"methodology":[39,63],"based":[40,134],"on":[41],"Epanechnikov":[43],"kernel":[44],"prior,":[45],"which":[46],"offers":[47],"closed":[49],"form":[50],"decision":[51],"rule":[52],"computational":[55],"efficiency":[56],"large":[59],"bioacoustic":[60],"datasets.":[61],"The":[62],"was":[64],"applied":[65],"to":[66,149],"recordings":[67],"three":[69],"obtained":[71],"from":[72],"iNaturalist":[74],"platform:":[75],"Euphonia":[76],"violacea,":[77],"Leiothrix":[78],"lutea,":[79],"Passer":[81],"domesticus.":[82],"After":[83],"denoising,":[85],"we":[86],"extracted":[87],"comprehensive":[89],"set":[90],"features,":[92],"including":[93],"Mel-Frequency":[94],"Cepstral":[95],"Coefficients":[96],"(MFCCs)":[97],"spectral":[99],"indices":[100],"such":[101],"as":[102],"entropy":[103],"zero-crossing":[105],"rate.":[106],"Several":[107],"supervised":[108],"learning":[109],"models:":[110],"Random":[111],"Forest,":[112],"Multinomial":[113],"Logistic":[114],"Regression":[115],"Support":[117],"Vector":[118],"Machine":[119],"(SVM)":[120],"were":[121],"evaluated":[122],"across":[123],"different":[124],"feature":[125],"dimensionalities.":[126],"Our":[127],"results":[128],"demonstrate":[129],"that":[130],"proposed":[132],"preprocessing":[135],"significantly":[136],"enhances":[137],"performance,":[139],"with":[140],"SVM":[142],"model":[143],"achieving":[144],"highest":[146],"accuracy":[147],"(up":[148],"0.9398)":[150],"under":[151],"10-dimensional":[153],"MFCC":[154],"configuration.":[155],"research":[157],"provides":[158],"robust":[160],"statistical":[161],"tool":[162],"automated":[164],"ecological":[165],"monitoring":[166],"management":[169],"biological":[171],"invasions.":[172]},"counts_by_year":[],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2026-09-12T00:00:00"}
