Machine Learning Based Music Categorization Article Swipe
Amiel B. Andias
,
N. K. Singh
,
Satyam Singh Rawat -
,
Anirudh Ratauri -
,
Ritu Rawat
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.36948/ijfmr.2025.v07i03.48623
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.36948/ijfmr.2025.v07i03.48623
Music genre categorization is an essential activity in music data retrieval and recommendation systems. This research focuses on classifying music genres using machine learning techniques, specifically the Support Vector Machine. The GTZAN dataset, comprising 10 distinct genres, is utilized for training and evaluation. We took audio features like MFCCs, spectral contrast, and chroma vectors from the GTZAN dataset and used them to train a Support Vector Machine (SVM) model. The categorization model achieved an accuracy of 81.1% across 10 distinct genres in a multi-class setting the research emphasizes the difficulties of genre convergence and the efficacy of machine learning in automating music categorization. Future developments might explore deep learning techniques, like Convolutional Neural Networks, better ways to choose features, and improving data to make music categorization more effective. assignment in music retrieve information
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.36948/ijfmr.2025.v07i03.48623
- https://www.ijfmr.com/papers/2025/3/48623.pdf
- OA Status
- hybrid
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411490088
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4411490088Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.36948/ijfmr.2025.v07i03.48623Digital Object Identifier
- Title
-
Machine Learning Based Music CategorizationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-19Full publication date if available
- Authors
-
Amiel B. Andias, N. K. Singh, Satyam Singh Rawat -, Anirudh Ratauri -, Ritu RawatList of authors in order
- Landing page
-
https://doi.org/10.36948/ijfmr.2025.v07i03.48623Publisher landing page
- PDF URL
-
https://www.ijfmr.com/papers/2025/3/48623.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://www.ijfmr.com/papers/2025/3/48623.pdfDirect OA link when available
- Concepts
-
Categorization, Computer science, Support vector machine, Artificial intelligence, Convolutional neural network, Machine learning, Text categorization, Contrast (vision), Natural language processingTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4411490088 |
|---|---|
| doi | https://doi.org/10.36948/ijfmr.2025.v07i03.48623 |
| ids.doi | https://doi.org/10.36948/ijfmr.2025.v07i03.48623 |
| ids.openalex | https://openalex.org/W4411490088 |
| fwci | 0.0 |
| type | article |
| title | Machine Learning Based Music Categorization |
| biblio.issue | 3 |
| biblio.volume | 7 |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11309 |
| topics[0].field.id | https://openalex.org/fields/17 |
| topics[0].field.display_name | Computer Science |
| topics[0].score | 0.9998000264167786 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/1711 |
| topics[0].subfield.display_name | Signal Processing |
| topics[0].display_name | Music and Audio Processing |
| topics[1].id | https://openalex.org/T13996 |
| topics[1].field.id | https://openalex.org/fields/12 |
| topics[1].field.display_name | Arts and Humanities |
| topics[1].score | 0.9832000136375427 |
| topics[1].domain.id | https://openalex.org/domains/2 |
| topics[1].domain.display_name | Social Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1210 |
| topics[1].subfield.display_name | Music |
| topics[1].display_name | Diverse Musicological Studies |
| topics[2].id | https://openalex.org/T11349 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9552000164985657 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1707 |
| topics[2].subfield.display_name | Computer Vision and Pattern Recognition |
| topics[2].display_name | Music Technology and Sound Studies |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C94124525 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8755266070365906 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q912550 |
| concepts[0].display_name | Categorization |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.772424578666687 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C12267149 |
| concepts[2].level | 2 |
| concepts[2].score | 0.6935282945632935 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q282453 |
| concepts[2].display_name | Support vector machine |
| concepts[3].id | https://openalex.org/C154945302 |
| concepts[3].level | 1 |
| concepts[3].score | 0.6778439879417419 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[3].display_name | Artificial intelligence |
| concepts[4].id | https://openalex.org/C81363708 |
| concepts[4].level | 2 |
| concepts[4].score | 0.6640790104866028 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q17084460 |
| concepts[4].display_name | Convolutional neural network |
| concepts[5].id | https://openalex.org/C119857082 |
| concepts[5].level | 1 |
| concepts[5].score | 0.6425562500953674 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[5].display_name | Machine learning |
| concepts[6].id | https://openalex.org/C2986744138 |
| concepts[6].level | 3 |
| concepts[6].score | 0.4630148112773895 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q302088 |
| concepts[6].display_name | Text categorization |
| concepts[7].id | https://openalex.org/C2776502983 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4436369240283966 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q690182 |
| concepts[7].display_name | Contrast (vision) |
| concepts[8].id | https://openalex.org/C204321447 |
| concepts[8].level | 1 |
| concepts[8].score | 0.3573603332042694 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q30642 |
| concepts[8].display_name | Natural language processing |
| keywords[0].id | https://openalex.org/keywords/categorization |
| keywords[0].score | 0.8755266070365906 |
| keywords[0].display_name | Categorization |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.772424578666687 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/support-vector-machine |
| keywords[2].score | 0.6935282945632935 |
| keywords[2].display_name | Support vector machine |
| keywords[3].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[3].score | 0.6778439879417419 |
| keywords[3].display_name | Artificial intelligence |
| keywords[4].id | https://openalex.org/keywords/convolutional-neural-network |
| keywords[4].score | 0.6640790104866028 |
| keywords[4].display_name | Convolutional neural network |
| keywords[5].id | https://openalex.org/keywords/machine-learning |
| keywords[5].score | 0.6425562500953674 |
| keywords[5].display_name | Machine learning |
| keywords[6].id | https://openalex.org/keywords/text-categorization |
| keywords[6].score | 0.4630148112773895 |
| keywords[6].display_name | Text categorization |
| keywords[7].id | https://openalex.org/keywords/contrast |
| keywords[7].score | 0.4436369240283966 |
| keywords[7].display_name | Contrast (vision) |
| keywords[8].id | https://openalex.org/keywords/natural-language-processing |
| keywords[8].score | 0.3573603332042694 |
| keywords[8].display_name | Natural language processing |
| language | en |
| locations[0].id | doi:10.36948/ijfmr.2025.v07i03.48623 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210207214 |
| locations[0].source.issn | 2582-2160 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | 2582-2160 |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | International Journal For Multidisciplinary Research |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].license | cc-by-sa |
| locations[0].pdf_url | https://www.ijfmr.com/papers/2025/3/48623.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by-sa |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | International Journal For Multidisciplinary Research |
| locations[0].landing_page_url | https://doi.org/10.36948/ijfmr.2025.v07i03.48623 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5118641518 |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Amiel B. Andias |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Princy tyagi |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5008498213 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-7404-5162 |
| authorships[1].author.display_name | N. K. Singh |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Narendra Singh - |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5042192177 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Satyam Singh Rawat - |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Satyam Singh Rawat - |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5118454768 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Anirudh Ratauri - |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Anirudh Ratauri - |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5039456484 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-0955-2763 |
| authorships[4].author.display_name | Ritu Rawat |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Rohit Singh Rawat - |
| authorships[4].is_corresponding | False |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.ijfmr.com/papers/2025/3/48623.pdf |
| open_access.oa_status | hybrid |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Machine Learning Based Music Categorization |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11309 |
| primary_topic.field.id | https://openalex.org/fields/17 |
| primary_topic.field.display_name | Computer Science |
| primary_topic.score | 0.9998000264167786 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/1711 |
| primary_topic.subfield.display_name | Signal Processing |
| primary_topic.display_name | Music and Audio Processing |
| related_works | https://openalex.org/W2027384988, https://openalex.org/W2360898036, https://openalex.org/W2390857744, https://openalex.org/W2390698788, https://openalex.org/W2133651098, https://openalex.org/W2078570174, https://openalex.org/W2383063829, https://openalex.org/W2138922887, https://openalex.org/W2035261173, https://openalex.org/W2106892947 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.36948/ijfmr.2025.v07i03.48623 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210207214 |
| best_oa_location.source.issn | 2582-2160 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | 2582-2160 |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | International Journal For Multidisciplinary Research |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.license | cc-by-sa |
| best_oa_location.pdf_url | https://www.ijfmr.com/papers/2025/3/48623.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by-sa |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | International Journal For Multidisciplinary Research |
| best_oa_location.landing_page_url | https://doi.org/10.36948/ijfmr.2025.v07i03.48623 |
| primary_location.id | doi:10.36948/ijfmr.2025.v07i03.48623 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210207214 |
| primary_location.source.issn | 2582-2160 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | 2582-2160 |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | International Journal For Multidisciplinary Research |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.license | cc-by-sa |
| primary_location.pdf_url | https://www.ijfmr.com/papers/2025/3/48623.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-sa |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | International Journal For Multidisciplinary Research |
| primary_location.landing_page_url | https://doi.org/10.36948/ijfmr.2025.v07i03.48623 |
| publication_date | 2025-06-19 |
| publication_year | 2025 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 63, 82 |
| abstract_inverted_index.10 | 34, 78 |
| abstract_inverted_index.We | 43 |
| abstract_inverted_index.an | 4, 73 |
| abstract_inverted_index.in | 7, 81, 99, 129 |
| abstract_inverted_index.is | 3, 37 |
| abstract_inverted_index.of | 75, 90, 96 |
| abstract_inverted_index.on | 17 |
| abstract_inverted_index.to | 61, 116, 122 |
| abstract_inverted_index.The | 30, 69 |
| abstract_inverted_index.and | 11, 41, 51, 58, 93, 119 |
| abstract_inverted_index.for | 39 |
| abstract_inverted_index.the | 26, 55, 85, 88, 94 |
| abstract_inverted_index.This | 14 |
| abstract_inverted_index.data | 9, 121 |
| abstract_inverted_index.deep | 107 |
| abstract_inverted_index.from | 54 |
| abstract_inverted_index.like | 47, 110 |
| abstract_inverted_index.make | 123 |
| abstract_inverted_index.more | 126 |
| abstract_inverted_index.them | 60 |
| abstract_inverted_index.took | 44 |
| abstract_inverted_index.used | 59 |
| abstract_inverted_index.ways | 115 |
| abstract_inverted_index.(SVM) | 67 |
| abstract_inverted_index.81.1% | 76 |
| abstract_inverted_index.GTZAN | 31, 56 |
| abstract_inverted_index.Music | 0 |
| abstract_inverted_index.audio | 45 |
| abstract_inverted_index.genre | 1, 91 |
| abstract_inverted_index.might | 105 |
| abstract_inverted_index.model | 71 |
| abstract_inverted_index.music | 8, 19, 101, 124, 130 |
| abstract_inverted_index.train | 62 |
| abstract_inverted_index.using | 21 |
| abstract_inverted_index.Future | 103 |
| abstract_inverted_index.MFCCs, | 48 |
| abstract_inverted_index.Neural | 112 |
| abstract_inverted_index.Vector | 28, 65 |
| abstract_inverted_index.across | 77 |
| abstract_inverted_index.better | 114 |
| abstract_inverted_index.choose | 117 |
| abstract_inverted_index.chroma | 52 |
| abstract_inverted_index.genres | 20, 80 |
| abstract_inverted_index.model. | 68 |
| abstract_inverted_index.Machine | 66 |
| abstract_inverted_index.Support | 27, 64 |
| abstract_inverted_index.dataset | 57 |
| abstract_inverted_index.explore | 106 |
| abstract_inverted_index.focuses | 16 |
| abstract_inverted_index.genres, | 36 |
| abstract_inverted_index.machine | 22, 97 |
| abstract_inverted_index.setting | 84 |
| abstract_inverted_index.vectors | 53 |
| abstract_inverted_index.Machine. | 29 |
| abstract_inverted_index.accuracy | 74 |
| abstract_inverted_index.achieved | 72 |
| abstract_inverted_index.activity | 6 |
| abstract_inverted_index.dataset, | 32 |
| abstract_inverted_index.distinct | 35, 79 |
| abstract_inverted_index.efficacy | 95 |
| abstract_inverted_index.features | 46 |
| abstract_inverted_index.learning | 23, 98, 108 |
| abstract_inverted_index.research | 15, 86 |
| abstract_inverted_index.retrieve | 131 |
| abstract_inverted_index.spectral | 49 |
| abstract_inverted_index.systems. | 13 |
| abstract_inverted_index.training | 40 |
| abstract_inverted_index.utilized | 38 |
| abstract_inverted_index.Networks, | 113 |
| abstract_inverted_index.contrast, | 50 |
| abstract_inverted_index.essential | 5 |
| abstract_inverted_index.features, | 118 |
| abstract_inverted_index.improving | 120 |
| abstract_inverted_index.retrieval | 10 |
| abstract_inverted_index.assignment | 128 |
| abstract_inverted_index.automating | 100 |
| abstract_inverted_index.comprising | 33 |
| abstract_inverted_index.effective. | 127 |
| abstract_inverted_index.emphasizes | 87 |
| abstract_inverted_index.classifying | 18 |
| abstract_inverted_index.convergence | 92 |
| abstract_inverted_index.evaluation. | 42 |
| abstract_inverted_index.information | 132 |
| abstract_inverted_index.multi-class | 83 |
| abstract_inverted_index.techniques, | 24, 109 |
| abstract_inverted_index.developments | 104 |
| abstract_inverted_index.difficulties | 89 |
| abstract_inverted_index.specifically | 25 |
| abstract_inverted_index.Convolutional | 111 |
| abstract_inverted_index.categorization | 2, 70, 125 |
| abstract_inverted_index.recommendation | 12 |
| abstract_inverted_index.categorization. | 102 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.7900000214576721 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.2878458 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |