A Hybrid Cluster-Classifier model for Carnatic Raga Classification

Vasudevan, Anusooya; Ramasangu, Hariharan

doi:10.1109/conecct52877.2021.9622669

Cited by 5 publications

(2 citation statements)

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“…To linearize the frequency range and capture pertinent acoustic characteristics, Mel-filter banks are employed. A Mel-filter bank comprises an array of bandpass filters, operating based on the Mel scale as depicted in (3).…”

Section: B Feature Extractionmentioning

confidence: 99%

“…Diverse methodologies have been explored by researchers in this endeavor, ranging from conventional approaches like support vector machines (SVMs) and hidden Markov models (HMMs) to cutting-edge deep learning techniques that employ convolutional neural networks (CNNs) [2]. Various feature extraction methods, including the utilization of mel-frequency cepstral coefficients (MFCCs), chroma features, and spectral roll-off features, have been harnessed to capture the unique characteristics of ragas within the audio data [3]. By accurately identifying ragas, automatic music recommendation systems can be empowered, music education can be enhanced, and emotional analysis of musical compositions can be facilitated [4].…”

Section: Introductionmentioning

confidence: 99%

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Thaat Classification Using Recurrent Neural Networks with Long Short-Term Memory and Support Vector Machine

Swati Shilaskar

2023

IJRITCC

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This research paper introduces a groundbreaking method for music classification, emphasizing thaats rather than the conventional raga-centric approach. A comprehensive range of audio features, including amplitude envelope, RMSE, STFT, spectral centroid, MFCC, spectral bandwidth, and zero-crossing rate, is meticulously used to capture thaats' distinct characteristics in Indian classical music. Importantly, the study predicts emotional responses linked with the identified thaats. The dataset encompasses a diverse collection of musical compositions, each representing unique thaats. Three classifier models - RNN-LSTM, SVM, and HMM - undergo thorough training and testing to evaluate their classification performance. Initial findings showcase promising accuracies, with the RNN-LSTM model achieving 85% and SVM performing at 78%. These results highlight the effectiveness of this innovative approach in accurately categorizing music based on thaats and predicting associated emotional responses, providing a fresh perspective on music analysis in Indian classical music.

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Section: B Feature Extractionmentioning

confidence: 99%