Detection of Indonesian Dangdut Music Genre with Foreign Music Genres Through Features Classification Using Deep Learning

Wibowo, Ferry Wahyu; Wihayati,

doi:10.1109/ismode53584.2022.9743085

Cited by 4 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various reviews have been presented to analyse appropriate music features and classification algorithms that are explored in the domain of music genre recognition. Wibowo and Wihayati (2022) [19] proposed a deep learning approach for the classification of music genres. The authors explored the GTZAN dataset with ten foreign music genres.…”

Section: Literature Reviewmentioning

confidence: 99%

Large-Scale Music Genre Analysis and Classification Using Machine Learning with Apache Spark

2022

View full text Add to dashboard Cite

The trend for listening to music online has greatly increased over the past decade due to the number of online musical tracks. The large music databases of music libraries that are provided by online music content distribution vendors make music streaming and downloading services more accessible to the end-user. It is essential to classify similar types of songs with an appropriate tag or index (genre) to present similar songs in a convenient way to the end-user. As the trend of online music listening continues to increase, developing multiple machine learning models to classify music genres has become a main area of research. In this research paper, a popular music dataset GTZAN which contains ten music genres is analysed to study various types of music features and audio signals. Multiple scalable machine learning algorithms supported by Apache Spark, including naïve Bayes, decision tree, logistic regression, and random forest, are investigated for the classification of music genres. The performance of these classifiers is compared, and the random forest performs as the best classifier for the classification of music genres. Apache Spark is used in this paper to reduce the computation time for machine learning predictions with no computational cost, as it focuses on parallel computation. The present work also demonstrates that the perfect combination of Apache Spark and machine learning algorithms reduces the scalability problem of the computation of machine learning predictions. Moreover, different hyperparameters of the random forest classifier are optimized to increase the performance efficiency of the classifier in the domain of music genre classification. The experimental outcome shows that the developed random forest classifier can establish a high level of performance accuracy, especially for the mislabelled, distorted GTZAN dataset. This classifier has outperformed other machine learning classifiers supported by Apache Spark in the present work. The random forest classifier manages to achieve 90% accuracy for music genre classification compared to other work in the same domain.

show abstract

Section: Literature Reviewmentioning

confidence: 99%