Effect of Feature Selection on The Accuracy of Music Genre Classification using SVM Classifier

Setiadi, De Rosal Ignatius Moses; Rahardwika, Dewangga Satriya; Rachmawanto, Eko Hari; Sari, Christy Atika; Susanto, Ajib; Mulyono, Ibnu Utomo Wahyu; Astuti, Erna Zuni; Fahmi, Amiq

doi:10.1109/isemantic50169.2020.9234222

Cited by 9 publications

(5 citation statements)

References 17 publications

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“…We have used the filter feature selection method when determining effective features over popularity. The success of algorithms created with both datasets was evaluated by using the F-score value [27]. The properties that influence popularity, through the methods that we use, are instrumentality, acousticness, mode, valence, danceability, energy, loudness.…”

Section: Literature Surveymentioning

confidence: 99%

Effect of Feature Selection on the Accuracy of Music Popularity Classification Using Machine Learning Algorithms

et al. 2022

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This research aims to analyze the effect of feature selection on the accuracy of music popularity classification using machine learning algorithms. The data of Spotify, the most used music listening platform today, was used in the research. In the feature selection stage, features with low correlation were removed from the dataset using the filter feature selection method. Machine learning algorithms using all features produced 95.15% accuracy, while machine learning algorithms using features selected by feature selection produced 95.14% accuracy. The features selected by feature selection were sufficient for classification of popularity in established algorithms. In addition, this dataset contains fewer features, so the computation time is shorter. The reason why Big O time complexity is lower than models constructed without feature selection is that the number of features, which is the most important parameter in time complexity, is low. The statistical analysis was performed on the pre-processed data and meaningful information was produced from the data using machine learning algorithms.

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Section: Literature Surveymentioning

confidence: 99%

Effect of Feature Selection on the Accuracy of Music Popularity Classification Using Machine Learning Algorithms

et al. 2022

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“…Furthermore, the comparative performance of SVM and kNN revealed SVM having superior classification accuracy, closely followed by kNN [23]. In addition, a unique ensemble model of SVM and radial basis function (SVM-RBF) was proposed for music clip classification within the Spotify dataset [25]. Meanwhile, the Daubechies Wavelet Coefficient Histogram (DWCH) that concurrently captures local and global music signal information was also utilized for MGC tasks [37].…”

Section: Related Workmentioning

confidence: 99%

A Short Survey and Comparison of CNN-Based Music Genre Classification Using Multiple Spectral Features

Seo,

Cho,

Teisseyre

et al. 2024

IEEE Access

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The goal of music genre classification is to identify the genre of given feature vectors representing certain characteristics of music clips. In addition, to improve the accuracy of music genre classification, considerable research has been conducted on extracting spectral features, which contain critical information for genre classification, from music clips and feeding these features into training models. In particular, recent studies argue that classification accuracy can be enhanced by employing multiple spectral features simultaneously. Consequently, fusing information from multiple spectral features is a critical consideration in designing music genre classification models. Hence, this paper provides a short survey of recent studies on music genre classification and compares the performance of the most recent CNN-based models with a newly devised model that employs a late fusion strategy for the multiple spectral features. Our empirical study of 12 public datasets, including Ballroom, ISMIR04, and GTZAN, showed that the late fusion CNN model outperforms other compared methods. Additionally, we performed an indepth analysis to validate the effectiveness of the late fusion strategy in music genre classification.

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“…A few pieces from the literature emphasize the comparative analysis of multiple machine learning algorithms on different audio datasets in the domain of music genre classification. Ignatius Moses Setiadi et al (2020) [33] implemented a support vector machine with a radial kernel base function (RBF), naïve Bayes, and K-nearest neighbor on the Spotify music dataset for the classification of music genres. The author applied the chi-square method to filter important features, as the dataset had 26 genres with 18 features each.…”

Section: Literature Reviewmentioning

confidence: 99%

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

2022

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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.

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Effect of Feature Selection on The Accuracy of Music Genre Classification using SVM Classifier

Cited by 9 publications

References 17 publications

Effect of Feature Selection on the Accuracy of Music Popularity Classification Using Machine Learning Algorithms

Effect of Feature Selection on the Accuracy of Music Popularity Classification Using Machine Learning Algorithms

A Short Survey and Comparison of CNN-Based Music Genre Classification Using Multiple Spectral Features

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

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