Recommendation of Algorithm for Efficient Retrieval of Songs from Musical Dataset Article Swipe
Swathy Vodithala
,
Vaishnavi Gudimalla
,
Y. Bhavani
,
Preethi Madadi
,
Sharfuddin Waseem Mohammed
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.17762/ijritcc.v11i5s.6654
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.17762/ijritcc.v11i5s.6654
Now-a-days, the research is more towards the entertainment like music, songs, movies, etc. There are many existing works that suggest good songs, movies to people depending on their mood, nature and time that has been savior for the society during the days of lockdown. The existing algorithms used in the literature for basic clustering are K-means, TSNE (T- distributed Stochastic Neighborhood Embedding), PCA (Principal Component Analysis).In this paper, the music dataset considered, consists of songs with attributes as song name, genres, artists, mode, tempo, valence, year, liveness, loudness, popularity, acousticness, danceability, duration, energy, explicit, instrumentalness, key. The important feature is extracted from the other features with the support of literature survey i.e., number of music listeners, types of the songs and type of the music. Later, the dataset is divided into clusters using traditional technique that is k-means based on genre, an important attribute which is selected from the above attributes. The different classifier models like Random Forest, Extra Trees, LightGBM, XGBoost, CatBoost classifier are applied on the clustered dataset and the results have been evaluated on each individual algorithm. Thus the paper recommends not only the group of relevant songs but also suggests the best accurate classification algorithm that can be used for any mentioned musical dataset. The paper also compares all the said ensemble algorithms by calculating the precision, recall, f1-score and support. The accuracy is also calculated for all said ensemble algorithms and based on the accuracy the best suitable algorithm is suggested.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.17762/ijritcc.v11i5s.6654
- https://ijritcc.org/index.php/ijritcc/article/download/6654/5926
- OA Status
- diamond
- References
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4383888701
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4383888701Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.17762/ijritcc.v11i5s.6654Digital Object Identifier
- Title
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Recommendation of Algorithm for Efficient Retrieval of Songs from Musical DatasetWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-05-17Full publication date if available
- Authors
-
Swathy Vodithala, Vaishnavi Gudimalla, Y. Bhavani, Preethi Madadi, Sharfuddin Waseem MohammedList of authors in order
- Landing page
-
https://doi.org/10.17762/ijritcc.v11i5s.6654Publisher landing page
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https://ijritcc.org/index.php/ijritcc/article/download/6654/5926Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
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https://ijritcc.org/index.php/ijritcc/article/download/6654/5926Direct OA link when available
- Concepts
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Computer science, Random forest, Music information retrieval, Classifier (UML), Cluster analysis, Popularity, Artificial intelligence, Algorithm, Timbre, Pattern recognition (psychology), Musical, Machine learning, Visual arts, Social psychology, Psychology, ArtTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
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1Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.has | 33 |
| abstract_inverted_index.not | 184 |
| abstract_inverted_index.the | 1, 6, 37, 40, 49, 68, 102, 106, 118, 123, 126, 148, 167, 171, 181, 186, 194, 213, 219, 238, 240 |
| abstract_inverted_index.TSNE | 56 |
| abstract_inverted_index.Thus | 180 |
| abstract_inverted_index.also | 192, 210, 228 |
| abstract_inverted_index.been | 34, 174 |
| abstract_inverted_index.best | 195, 241 |
| abstract_inverted_index.days | 41 |
| abstract_inverted_index.each | 177 |
| abstract_inverted_index.etc. | 12 |
| abstract_inverted_index.from | 101, 147 |
| abstract_inverted_index.good | 20 |
| abstract_inverted_index.have | 173 |
| abstract_inverted_index.into | 130 |
| abstract_inverted_index.key. | 95 |
| abstract_inverted_index.like | 8, 155 |
| abstract_inverted_index.many | 15 |
| abstract_inverted_index.more | 4 |
| abstract_inverted_index.only | 185 |
| abstract_inverted_index.said | 214, 232 |
| abstract_inverted_index.song | 78 |
| abstract_inverted_index.that | 18, 32, 135, 199 |
| abstract_inverted_index.this | 66 |
| abstract_inverted_index.time | 31 |
| abstract_inverted_index.type | 121 |
| abstract_inverted_index.used | 47, 202 |
| abstract_inverted_index.with | 75, 105 |
| abstract_inverted_index.Extra | 158 |
| abstract_inverted_index.There | 13 |
| abstract_inverted_index.above | 149 |
| abstract_inverted_index.based | 138, 236 |
| abstract_inverted_index.basic | 52 |
| abstract_inverted_index.group | 187 |
| abstract_inverted_index.i.e., | 111 |
| abstract_inverted_index.mode, | 82 |
| abstract_inverted_index.mood, | 28 |
| abstract_inverted_index.music | 69, 114 |
| abstract_inverted_index.name, | 79 |
| abstract_inverted_index.other | 103 |
| abstract_inverted_index.paper | 182, 209 |
| abstract_inverted_index.songs | 74, 119, 190 |
| abstract_inverted_index.their | 27 |
| abstract_inverted_index.types | 116 |
| abstract_inverted_index.using | 132 |
| abstract_inverted_index.which | 144 |
| abstract_inverted_index.works | 17 |
| abstract_inverted_index.year, | 85 |
| abstract_inverted_index.Later, | 125 |
| abstract_inverted_index.Random | 156 |
| abstract_inverted_index.Trees, | 159 |
| abstract_inverted_index.during | 39 |
| abstract_inverted_index.genre, | 140 |
| abstract_inverted_index.models | 154 |
| abstract_inverted_index.movies | 22 |
| abstract_inverted_index.music, | 9 |
| abstract_inverted_index.music. | 124 |
| abstract_inverted_index.nature | 29 |
| abstract_inverted_index.number | 112 |
| abstract_inverted_index.paper, | 67 |
| abstract_inverted_index.people | 24 |
| abstract_inverted_index.savior | 35 |
| abstract_inverted_index.songs, | 10, 21 |
| abstract_inverted_index.survey | 110 |
| abstract_inverted_index.tempo, | 83 |
| abstract_inverted_index.Forest, | 157 |
| abstract_inverted_index.applied | 165 |
| abstract_inverted_index.dataset | 70, 127, 169 |
| abstract_inverted_index.divided | 129 |
| abstract_inverted_index.energy, | 92 |
| abstract_inverted_index.feature | 98 |
| abstract_inverted_index.genres, | 80 |
| abstract_inverted_index.k-means | 137 |
| abstract_inverted_index.movies, | 11 |
| abstract_inverted_index.musical | 206 |
| abstract_inverted_index.recall, | 221 |
| abstract_inverted_index.results | 172 |
| abstract_inverted_index.society | 38 |
| abstract_inverted_index.suggest | 19 |
| abstract_inverted_index.support | 107 |
| abstract_inverted_index.towards | 5 |
| abstract_inverted_index.CatBoost | 162 |
| abstract_inverted_index.K-means, | 55 |
| abstract_inverted_index.XGBoost, | 161 |
| abstract_inverted_index.accuracy | 226, 239 |
| abstract_inverted_index.accurate | 196 |
| abstract_inverted_index.artists, | 81 |
| abstract_inverted_index.clusters | 131 |
| abstract_inverted_index.compares | 211 |
| abstract_inverted_index.consists | 72 |
| abstract_inverted_index.dataset. | 207 |
| abstract_inverted_index.ensemble | 215, 233 |
| abstract_inverted_index.existing | 16, 45 |
| abstract_inverted_index.f1-score | 222 |
| abstract_inverted_index.features | 104 |
| abstract_inverted_index.relevant | 189 |
| abstract_inverted_index.research | 2 |
| abstract_inverted_index.selected | 146 |
| abstract_inverted_index.suggests | 193 |
| abstract_inverted_index.suitable | 242 |
| abstract_inverted_index.support. | 224 |
| abstract_inverted_index.valence, | 84 |
| abstract_inverted_index.Component | 64 |
| abstract_inverted_index.LightGBM, | 160 |
| abstract_inverted_index.algorithm | 198, 243 |
| abstract_inverted_index.attribute | 143 |
| abstract_inverted_index.clustered | 168 |
| abstract_inverted_index.depending | 25 |
| abstract_inverted_index.different | 152 |
| abstract_inverted_index.duration, | 91 |
| abstract_inverted_index.evaluated | 175 |
| abstract_inverted_index.explicit, | 93 |
| abstract_inverted_index.extracted | 100 |
| abstract_inverted_index.important | 97, 142 |
| abstract_inverted_index.liveness, | 86 |
| abstract_inverted_index.lockdown. | 43 |
| abstract_inverted_index.loudness, | 87 |
| abstract_inverted_index.mentioned | 205 |
| abstract_inverted_index.technique | 134 |
| abstract_inverted_index.(Principal | 63 |
| abstract_inverted_index.Stochastic | 59 |
| abstract_inverted_index.algorithm. | 179 |
| abstract_inverted_index.algorithms | 46, 216, 234 |
| abstract_inverted_index.attributes | 76 |
| abstract_inverted_index.calculated | 229 |
| abstract_inverted_index.classifier | 153, 163 |
| abstract_inverted_index.clustering | 53 |
| abstract_inverted_index.individual | 178 |
| abstract_inverted_index.listeners, | 115 |
| abstract_inverted_index.literature | 50, 109 |
| abstract_inverted_index.precision, | 220 |
| abstract_inverted_index.recommends | 183 |
| abstract_inverted_index.suggested. | 245 |
| abstract_inverted_index.Embedding), | 61 |
| abstract_inverted_index.Now-a-days, | 0 |
| abstract_inverted_index.attributes. | 150 |
| abstract_inverted_index.calculating | 218 |
| abstract_inverted_index.considered, | 71 |
| abstract_inverted_index.distributed | 58 |
| abstract_inverted_index.popularity, | 88 |
| abstract_inverted_index.traditional | 133 |
| abstract_inverted_index.Analysis).In | 65 |
| abstract_inverted_index.Neighborhood | 60 |
| abstract_inverted_index.acousticness, | 89 |
| abstract_inverted_index.danceability, | 90 |
| abstract_inverted_index.entertainment | 7 |
| abstract_inverted_index.classification | 197 |
| abstract_inverted_index.instrumentalness, | 94 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.4300000071525574 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.09142791 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |