Music Genre Classification Using African Buffalo Optimization

Jaishankar, B.; Anitha, R.; Shadrach, Finney Daniel; Sivarathinabala, M.; Balamurugan, V.

doi:10.32604/csse.2023.022938

Cited by 11 publications

(3 citation statements)

References 18 publications

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“…To enhance computational efficiency and address issues related to premature convergence, innovative algorithms have been incorporated into the domain of music genre classification. For instance, the African Buffalo Optimization (ABO) method, inspired by the behavior of African buffalos, can remember previous solutions, encourage collaboration among optimization agents, and minimize the reliance on numerous parameters [2]. The ABO algorithm enhances optimization efficiency by leveraging these characteristics.…”

Section: Machine Learning Methods Applied In Mirmentioning

confidence: 99%

Evolution and Emerging Trends in Musical Information Retrieval: A Comprehensive Review and Future Prospects

Ding

2024

HSET

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The rapid development of the digital music industry has brought challenges for music lovers and researchers. In response to these challenges, the field of Music Information Retrieval (MIR) emerged in the mid-1960s to capture the complicated and multi-layered nature of music. Among the various approaches explored, machine learning methods have shown promise in overcoming the complexity of this interdisciplinary field. This paper is primarily centered on conducting an in-depth review of the current state of research regarding the utilization of machine learning within the field of Music Information Retrieval (MIR). Additionally, it aims to forecast the potential future directions within the MIR industry. Upon extensive literature review, it becomes evident that various machine learning techniques, including Neural Networks (NN), Support Vector Machines (SVM), and K-nearest neighbors (KNN), have found common applicability in this field. Furthermore, this review highlights Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) as potential algorithms poised to shape the future landscape of MIR. The findings of this paper serve to elucidate the direction in which MIR is progressing, offering valuable guidance for forthcoming research and development endeavors. In doing so, it contributes to the continued progress and maturation of the field MIR.

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Section: Machine Learning Methods Applied In Mirmentioning

confidence: 99%

Evolution and Emerging Trends in Musical Information Retrieval: A Comprehensive Review and Future Prospects

Ding

2024

HSET

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“…Another research [27] uses several ML models and one DL model which will be used on three datasets, including GTZAN, ISMIR2004, and Latin Music. The DL learning model gets the best results among the ML models, and the model is a Neural Network (NN) with a training algorithm, namely trainingdx.…”

Section: Related Workmentioning

confidence: 99%

Music-Genre Classification using Bidirectional Long Short-Term Memory and Mel-Frequency Cepstral Coefficients

Wijaya,

Setiadi,

Muslikh

2024

J. Comput. Theor. Appl.

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Music genre classification is one part of the music recommendation process, which is a challenging job. This research proposes the classification of music genres using Bidirectional Long Short-Term Memory (BiLSTM) and Mel-Frequency Cepstral Coefficients (MFCC) extraction features. This method was tested on the GTZAN and ISMIR2004 datasets, specifically on the IS-MIR2004 dataset, a duration cutting operation was carried out, which was only taken from seconds 31 to 60 so that it had the same duration as GTZAN, namely 30 seconds. Preprocessing operations by removing silent parts and stretching are also performed at the preprocessing stage to obtain normalized input. Based on the test results, the performance of the proposed method is able to produce accuracy on testing data of 93.10% for GTZAN and 93.69% for the ISMIR2004 dataset.

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“…There are many theories related to the study of diversified models of choir teaching. For example, some experts believe that diverse teaching models can provide more comprehensive methods for vocal music teaching in universities and promote a dynamic classroom atmosphere [1][2]. Some experts also believe that in middle school music classes, teachers should pay attention to the development of different cultures [3][4].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study on Choir Teaching Based on Diversified Models in Chinese and Foreign Universities under the Background of Information Technology

2023

FER

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With the continuous development of information technology, the traditional choir teaching mode can no longer meet the needs of students, and diversified teaching modes need to be adopted. This article aims to explore a comparative study of choir teaching based on diversified models in Chinese and foreign universities under the background of informatization. By comparing the different modes of choir teaching in Chinese and foreign universities and analyzing their characteristics, advantages and disadvantages, this paper explored the impact of diversified modes on choir teaching and proposed corresponding improvement suggestions. In addition, a questionnaire survey was conducted to analyze the diversified mode of choir teaching. The research results indicated that 86% of people agree with diversified teaching platforms. Diversified models can effectively improve the effectiveness and quality of choir teaching, but it is also necessary to pay attention to adapting to different teaching environments and student needs.

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Music Genre Classification Using African Buffalo Optimization

Cited by 11 publications

References 18 publications

Evolution and Emerging Trends in Musical Information Retrieval: A Comprehensive Review and Future Prospects

Evolution and Emerging Trends in Musical Information Retrieval: A Comprehensive Review and Future Prospects

Music-Genre Classification using Bidirectional Long Short-Term Memory and Mel-Frequency Cepstral Coefficients

A Comparative Study on Choir Teaching Based on Diversified Models in Chinese and Foreign Universities under the Background of Information Technology

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