Multilingual Indian Musical Type Classification

Aswale, Swati Pravin; Shrivastava, Prabhat Chandra; Bhagat, R. B.; Joshi, Vikrant B.; Shende, Seema M.

doi:10.1007/978-981-99-0973-5_31

Lecture Notes in Electrical Engineering

2023

DOI: 10.1007/978-981-99-0973-5_31

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Multilingual Indian Musical Type Classification

Swati Pravin Aswale

Prabhat Chandra Shrivastava²,

R. B. Bhagat³

et al.

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2024

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Cited by 3 publications

References 38 publications

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Detecting Selected Instruments in the Sound Signal

Kostrzewa,

Szwajnoch,

Brzeski

et al. 2024

Applied Sciences

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Detecting instruments in a music signal is often used in database indexing, song annotation, and creating applications for musicians and music producers. Therefore, effective methods that automatically solve this issue need to be created. In this paper, the mentioned task is solved using mel-frequency cepstral coefficients (MFCC) and various architectures of artificial neural networks. The authors’ contribution to the development of automatic instrument detection covers the methods used, particularly the neural network architectures and the voting committees created. All these methods were evaluated, and the results are presented and discussed in the paper. The proposed automatic instrument detection methods show that the best classification quality was obtained for an extensive model, which is the so-called committee of voting classifiers.

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Detecting Selected Instruments in the Sound Signal

Kostrzewa,

Szwajnoch,

Brzeski

et al. 2024

Applied Sciences

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Indian Classical Music Recognition using Deep Convolution Neural Network

Aswale,

Shrivastava,

Ranjan

et al. 2024

IJEER

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A divine approach to communicate feelings about the world occurs through music. There is a huge variety in the language of music. One of the principal variables of Indian social legacy is classical music. Hindustani and Carnatic are the two primary subgenres of Indian classical music. Models have been trained and taught to distinguish between Carnatic and Hindustani songs. This paper presents Indian classical music recognition based on multiple acoustic features (MAF) consisting of various statistical, spectral, and time domain features. The MAF provides the changes in intonation, timbre, prosody and pitch of the musical speech due to different ragas. The lightweight DCNN is used to improve the representation of the raga sound and to provide higher order abstract level features. The overall performance of the raga type is estimated using various performance metrics, including accuracy, precision, recall and F1-score. The proposed DCNN achieves an accuracy, precision, recall, and F1-score of 89.38%, 0.89, 0.89, and 0.89, respectively, for eight raga classifications. The extensive experimentation on eight classical ragas has shown a noteworthy improvement over the traditional state of art.

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Indian Classical Music Recognition using Deep Convolution Neural Network

Aswale,

Shrivastava,

Ranjan

et al. 2024

IJEER

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Multilingual Indian Musical Type Classification

Cited by 3 publications

References 38 publications

Detecting Selected Instruments in the Sound Signal

Detecting Selected Instruments in the Sound Signal

Indian Classical Music Recognition using Deep Convolution Neural Network

Indian Classical Music Recognition using Deep Convolution Neural Network

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