Raga recognition using fibonacci series based pitch distribution in Indian Classical Music

Sinith, M. S.; Tripathi, Shikha; Murthy, Kiran

doi:10.1016/j.apacoust.2020.107381

Cited by 14 publications

(7 citation statements)

References 10 publications

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“…Sinith [2] performed Raga recognition using Fibonacci series based pitch distribution in ICM. The developed model sets up an interesting relation between Fibonacci series and Just Intonation in ICM has been found.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The comparative analysis is performed for existing MLP and MCC models against the proposed Hybrid Spectral Feature Extraction methods that evaluated the results with CompMusic dataset for mood classification of ragas. Sinith [2] developed the Discrete Pitch Contour (DPC) based Hidden Markov Model (HMM) on a table derived using Fibonacci series failed to consider when the same notes in different ragas performed DPC that showed deviations and lowered the performance. Similarly, Siji John [15] utilized CNN for automatic raga classification that faced problem during raga recognition.…”

Section: Comparative Analysismentioning

confidence: 99%

“…The ragas are composed based on the moods, area associated with the pitches and the relations among other pitches. The ragas are identified based on the levels of pitches and based on the pitches of tones, their relations among them convey which particular kind of emotion the raga belongs to [1,2]. The set of pitch arrangements give rise to a set of ragas which establish a mood of the raga or the flavor of raga [3,4].…”

Section: Introductionmentioning

confidence: 99%

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Automatic Music Mood Classification using Multi-class Support Vector Machine based on Hybrid Spectral Features

Krishnaiah¹,

Parameshachari²

2021

IJIES

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Automatic Raga identification is an important step in computational musicology as it is helpful for indexing Indian music, classifying, recommending tunes, etc. However, automatic raga recognition is a difficult task as it requires guidance from the expertise training and each of the ragas is identified based on the characteristic phrase and pitch collection. However, the identification or extraction of appropriate musical sample features was difficult to enhance the success rate of the classifier. The feature identification and extraction were considered as the major issue in the existing models. To overcome such an issue, the proposed hybrid spectral feature extraction technique extracted and combined the spectral features of audios such as Spread, centroid, skewness, Mel Frequency Cepstrum Coefficients (MFCCs) and Linear Predictor Coefficients (LPCs) reduced the dimensionality complexity enhanced the success rate of the Multi-Support Vector Machine (MSVM) classifier for mood classification. The CompMusic dataset is utilized for this research work, where 4 types of ragas namely Sindhu Bhairavi, Darbari, Saveri, and Sri Raga, are considered for the mood classification of ragas. From these ragas, tone based features are extracted based on the amplitude of each raga samples and are fed to the MSVM classifier for mood classification of 4 different types are Sympathy, Serious, Peace, and Sad. The classification results for the proposed hybrid spectral feature extraction obtained better accuracy of 97.53% when compared to the existing Hidden Markov Model (HMM) obtained accuracy as 95.3%, Convolution Neural Network (CNN) model as 94%, and Gaussian Mixture Model (GMM) as 95 %.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Music Mood Classification using Multi-class Support Vector Machine based on Hybrid Spectral Features

Krishnaiah¹,

Parameshachari²

2021

IJIES

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“…Sinith et al [14] presented the Fibonacci arrangement-based pitch dispersion Hidden Markov Model. The pitch shapes were dependent on a table which was inferred by utilizing the Fibonacci arrangement for ICM.…”

Section: Resolution Of Pitch-classesmentioning

confidence: 99%

Automated Transcription for Raga Recognition and Classification in Indian Classical Music Using Machine Learning

Gowrishankar¹,

Nagappa²

2021

Atlantis Highlights in Computer Sciences

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Raga recognition is only possible by trained musician to understand the notes based on the lead voice but a beginner is unable to decode the notes. This is significant for current scenarios in developing an automated note transcription system in Indian Classical Music (ICM). In the present research, various properties of raga and the machine learning techniques that are used for identifying the raga by a machine rather than a human or music expert are surveyed. The previously developed automatic raga recognition techniques using Carnatic and Hindustani Music, the main drawbacks and the improvements required are discussed. The present research work discusses about the future proposed models for automatic raga recognition using pitch detection algorithm, finding Tuning Offset, and Note Segmentation process. The proposed model will obtain better accuracy more than 96 % when compared to the existing CNN, GMM that obtained accuracy of 94 % and 95 %.

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“…Although the machine learning method is the first innovation in the research of music short score recognition, the method adds a lot of work cost to the method due to the lack of a clear and explicit engineering framework and the tedious manual labeling work, plus the accuracy and real-time performance of the machine learning method are not good enough. Therefore, there is still a lot of research space in the field of music score recognition [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Network for Musical Notation Recognition during Music Teaching

2022

Computational Intelligence and Neuroscience

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In order to improve the quality and efficiency of music teaching, we try to automate the teaching of music notation. With the addition of computer vision technology and note recognition algorithms, we improve the generative adversarial network to enhance the recognition accuracy and efficiency of music short scores. We adopt an embedded matching structure based on adversarial neural networks, starting from generators and discriminators, respectively, to unify generators and discriminators from the note input side. Each network layer is then laid out according to a cascade structure to preserve the different layers of note features in each convolutional layer. Residual blocks are then inserted in some network layers to break the symmetry of the network structure and enhance the ability of the adversarial network to acquire note features. To verify the efficiency of our method, we select monophonic spectrum, polyphonic spectrum, and miscellaneous spectrum datasets for validation. The experimental results demonstrate that our method has the best recognition accuracy in the monophonic spectrum and the miscellaneous spectrum, which is better than the machine learning method. In the recognition efficiency of note detail information, our method is more efficient in recognition and outperforms other deep learning methods.

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Raga recognition using fibonacci series based pitch distribution in Indian Classical Music

Cited by 14 publications

References 10 publications

Automatic Music Mood Classification using Multi-class Support Vector Machine based on Hybrid Spectral Features

Automatic Music Mood Classification using Multi-class Support Vector Machine based on Hybrid Spectral Features

Automated Transcription for Raga Recognition and Classification in Indian Classical Music Using Machine Learning

Generative Adversarial Network for Musical Notation Recognition during Music Teaching

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