Genre Based Classification of Hindi Music

Chaudhary, Deepti; Singh, Niharika; Singh, Sukhwinder

doi:10.1007/978-3-030-16681-6_8

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…4. Finally, we have compared our result with previous works ( [9], [10], [11] & [12] and also with most recent works( [13] & [14])who have used deep learning techniques to classify other genres of Indian music. We found signi cant improvement (increase) in classi cation accuracies as 5.82%, 7.43%, 1.72% & 7.88% respectively (machine learning models) and 2.6% & 8.36%respectively(deep learning models), which are quite effective for our methodology proposed.…”

Section: Resultsmentioning

confidence: 95%

“…Highest classi cation accuracy of 96.96 was achieved for classi cation of Tamil Classical & Tamil Folk music using SVM & k-NN models [11]. Using Classical, Folk, Ghazal and Su songs of Indian music as the dataset, spectral features are extracted and a classi cation accuracy of 90.8% was obtained with SVM model; Naïve-based model and k-NN based models showed even lesser accuracy [12]. Most recent works such as [13] used deep neural network model 1-layer RNN-LSTM in order to classify Hindustani & Carnatic music with highest classi cation accuracy of 96.08% using MFCC features only.…”

Section: Related Workmentioning

confidence: 99%

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Automatic classification of Indian Music using EMD

Sarkar,

Solanki,

Chakraborty

2024

Preprint

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Indian music is very rich in emotional content and technicalities. It is also contrasting having multiple genres which may be broadly classified as Classical (Hindustani or Carnatic) music, Semi-classical music (including Ghazals) and Light (including folk) music. The so called “rasa” or emotional content in Indian music is of much significance, e.g. selection of the right kind of music for therapeutic intervention by the music therapist who might be an expert in western art music (WAM) but could be new to Indian music. This paper presents a novel classification technique based on empirical mode decomposition (EMD), applied for the first time in Indian music. In this work, two genres of Indian music, Classical and Semi-classical, are considered. Highest classification accuracy of 98.68% is reported in the paper using SVM binary classifier after extraction of relevant features such as time domain features, frequency domain features, MFCC and Chroma vector.

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Section: Resultsmentioning

confidence: 95%

Section: Related Workmentioning

confidence: 99%

Automatic classification of Indian Music using EMD

Sarkar,

Solanki,

Chakraborty

2024

Preprint

View full text Add to dashboard Cite

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“…Other machine learning models were used to train the dataset, for instance, SVM, logistic regression, linear regression, and kNN. Genres of music can often depend upon the mood and in the research made by Deepti et al [20] they have focused on classifying four genres of Hindi music -Classical, Folk, Ghazal, and Sufi based on positive arousal, negative arousal, positive valence, and negative valence by considering arousal and valence as parameters. The correlation coefficient is used to determine the linear dependence between the characteristics of two signals.…”

Section: Related Workmentioning

confidence: 99%

BMNet-5: A Novel Approach of Neural Network to Classify the Genre of Bengali Music Based on Audio Features

et al. 2022

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Music genre classification (MGC) is the process of putting genre labels on music by analyzing the sounds or words. With the rapid growth of music data repositories, MGC can be used in a lot of ways to organize and manage music recommendation systems, advertising, and streaming services. But there have been a lot of works on classifying English music using different statistical and machine learning methods, but there hasn't been much progress in classifying Bengali music. Also, Deep Learning (DL) methods have been used in a few important ways to classify different types of music. The content and uniqueness of Bengali music make it much more interesting. Also, there is still a lot to learn about how to use the DL approach in Bengali music. So, Bengali music genre classification is a pretty new area of research in the field of Deep Learning. In this paper, we developed a unique technique called BMNet-5 to perform a multiclass classification of Bangla music genres such as "Bangla Adhunik," "Bangla Hip-Hop," "Bangla Band Music," "Nazrulgeeti," "Palligeeti," and "Rabindra Sangeet." We show the effectiveness of the suggested technique by extracting features from a dataset of 1742 Bangla music pieces and evaluating the automated classification judgments. The proposed BMNet-5 is based on a neural network designed to predict music genre from audio inputs. Our suggested model outperformed the corresponding previous research with an accuracy of 90.32%. The BMNet-5 model is then tested for performance consistency using K-fold cross validation with varying k values. Finally, we use the suggested model to train the interpretable SHAP model for all the genre of the Bangla music dataset, and the development of an explainable outcome may have a significant advantage.

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