Music Genre Classification Using Neural Network

Shaikh, Tarannum; Jadhav, Ashish

doi:10.1051/itmconf/20224403016

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Exploring other aspects could significantly enhance its capabilities. In 2022, Tarannum Shaikh and Ashish Jadhav [2] presented research work using spectrogram images as input to the neural networks using two distinct databases-the "GTZAN" database and the "MusicNet" database. Even though an accuracy of 92.65% was achieved, the validation loss was as high as 57%.…”

Section: Literature Reviewmentioning

confidence: 99%

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Music Genre Classification using Artificial Neural Networks

Sood,

Rathee,

Bansal

et al. 2024

Preprint

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Music genres play a pivotal role in structuring and interpreting music through shared attributes and traditions. Accurate classification of music genres enhances the overall music experience and enables more precise evaluation of artistic innovation and quality. In this paper, a music genre classification system has been proposed that uses an Artificial Neural Network (ANN) Model, moving beyond the traditional Convolutional Neural Network (CNN) approaches that rely heavily on spectrogram images and require extensive computational resources. Our model focuses on analyzing only the crucial characteristic features of audio files, sidestepping the need for complete spectrogram data. Mel-frequency Cepstral Coefficient (MFCC) has been utilized as the main feature extraction method and is combined with Chroma features and Spectral Contrast. The widely used GTZAN dataset has been utilized. After the extraction of the feature vector, the proposed model classifies the music into different genres. The model exhibited impressive performance with a training accuracy of 98.41% and a validation accuracy of 97.05%, highlighting its effectiveness in accurately classifying music genres.

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Section: Literature Reviewmentioning

confidence: 99%

“…Model Accuracy (in %) [1] CNN 80.50 [2] CNN 92.65 [3] CRNN 77.89 [4] SVM 76.40 [5] ANN 91.76 [6] KNN 90.00 [7] KNN 92.69 [8] SNN 94.12 [9] KNN 80.00 Proposed Model ANN 97.05…”

Section: Referencesmentioning

confidence: 99%

Music Genre Classification using Artificial Neural Networks

Sood,

Rathee,

Bansal

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…In 2022, Tarannum Shaikh and Ashish Jadhav studied the performance of the CNN model for MGC [2]. Spectrogram images are given as input to the CNN model.…”

Section: Literature Surveymentioning

confidence: 99%

Automated Music Genre Classification through Deep Learning Techniques

Kiran Kumar,

Sujanasri,

Neha

et al. 2023

E3S Web Conf.

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Music Genre Classification (MGC) automatically categorizes music into different genres based on various musical attributes and features in a small number of music files. This is a crucial problem in the field of music information retrieval as it provides a way to organize and analyse large amounts of music files. MGC can be performed using conventional machine learning algorithms such as SVM, k-nearest neighbours, Decision trees, and neural networks. These algorithms learn to recognize different musical features and attributes to categorize the music files into different genres. The literature shows that the performance of conventional machine learning algorithms is inferior to deep learning algorithms such as CNN, RNN, etc., in various applications. Hence, the CNN algorithm is adapted to implement the classification of music files. This aims to classify music genres using CNN deep learning techniques. The performance of the algorithms for MGC can be evaluated using metrics such as accuracy, precision, recall, and F1-score. Additionally, the impact of different features and algorithms on the performance of MGC can be studied and compared. It has applications in areas such as automated music recommendation systems, music education, and music production. An accuracy of 83% is achieved by using CNN to accomplish the task of MGC.

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State-of-the-Art in 1D Convolutional Neural Networks: A Survey

Ige,

Sibiya

2024

IEEE Access

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Music Genre Classification Using Neural Network

Cited by 6 publications

References 11 publications

Music Genre Classification using Artificial Neural Networks

Music Genre Classification using Artificial Neural Networks

Automated Music Genre Classification through Deep Learning Techniques

State-of-the-Art in 1D Convolutional Neural Networks: A Survey

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