Data analysis on music classification system and creating a sentiment word dictionary for Kokborok language

Das, S. R.; Satpathy, Sambit; Debbarma, Swapan; Bhattacharyya, Budhaditya

doi:10.1007/s12652-019-01565-y

Cited by 6 publications

(3 citation statements)

References 38 publications

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“…The model has the silence region showed the least negligible energy. The total energy for the short time energy (SE) is expressed as shown in (4). In (4), 𝑆 is known as the frame's length of audio.…”

Section: Mel Frequency Cepstrum Coefficients and Short Time Energymentioning

confidence: 99%

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Raga classification using enhanced spatial bound whale optimization algorithm

Gowrishankar

Bhajantri

2023

IJEECS

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A raga is a unique set of notes with certain rules that carefully followed, retain and protect its purity and produce amazing musical effects. An automated raga transcription and identification is important for computational musicology, which is an important step for musicology for indexing, classifying, and recommending tunes. In the present research, the audio features such as mel frequency cepstrum coefficients (MFCCs), spectral flux, short time energy, audio feature extractor, and spectral centroid features are used for the prediction of a raga. The model showed more complexity which means it required lots of training data. The proposed enhanced spatial bound whale optimization algorithm (ESBWOA) is used that overcome the feature selection problem of high dimensional features. In addition to this, a weighted salp swarm algorithm (SSA) is used for selecting the tone-based features from the ragas based on amplitude or each raga sample. The features were fed for bidirectional long short-term memory (Bi-LSTM) network, which enhanced the success rate for raga identification and classification. The present research uses CompMusic dataset in the research work where 9 classes for Carnatic music and 7 classes in Hindustani music are considered for the classification of ragas.

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Section: Mel Frequency Cepstrum Coefficients and Short Time Energymentioning

confidence: 99%

“…Significant work has been performed on the multimedia sources like text or video also audio processing in the developing phase. The ICM is categorized broadly into Hindustani and Carnatic music [3], [4]. It is having a wide range of following in the way Carnatic music has higher complexity which means the notes were arranged and rendered.…”

Section: Introductionmentioning

confidence: 99%

Raga classification using enhanced spatial bound whale optimization algorithm

Gowrishankar

Bhajantri

2023

IJEECS

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“…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]. The emotion is classified based on the 2 step process that includes feature extraction and classification.…”

Section: Introductionmentioning

confidence: 99%

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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