Automatic music mood recognition using Russell’s twodimensional valence-arousal space from audio and lyrical data as classified using SVM and Naïve Bayes

Tan, Kaiwen; Villarino, M. L.; Maderazo, Christian V.

doi:10.1088/1757-899x/482/1/012019

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…They find that the SVM classifier has the best classification performance with 80% of accuracy, followed by KNN and NB. SVM and NB-based classifier is also used on audio features and lyrical features to automatically classify the mood of a song (7) . Based on MFCC features and Chroma Reduced Pitch (CRP) features of songs, Sangeetha & Nalini (8) propose an approach for the identification of singers with the help of SVM.…”

Section: Related Workmentioning

confidence: 99%

Classification of Assamese Folk Songs’ Melody using Supervised Learning Techniques

Bora¹,

Barman²,

Patowary³

et al. 2023

IJST

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Objectives: A melody is made up of several musical notes or pitches that are joined together to form one whole. This experiment aims to develop four models based on the Mel-frequency Cepstral Coefficients (MFCC) to classify the melodies played on harmonium corresponding to five different class of Assamese folk Music. Methods: The melodies of five different categories of Assamese folk songs are selected for classification. With the help of expert musicians, these melodies are played in harmonium and audio samples are recorded in the same acoustic environment. 20 MFCC's are extracted from each of the samples and classification of the melodies is done using four supervised learning techniques-Decision Tree Classifier, Linear Discriminant Analysis (LDA), Random Forest Classifier, and Support Vector Machine (SVM). Findings: The performance of the fitted models are evaluated using different evaluation techniques and presented. A maximum of 94.17% average accuracy score is achieved under Support Vector Machine. The average accuracy scores of Decision Tree Classifier, Linear Discriminant Analysis (LDA), and Random Forest Classifier are 73.58%, 85.58%, and 86.11% respectively. The models are developed based on 250 samples (50 from each type). However, increasing the training sample size, there is a possibility to improve the performances of the other three models also. Novelty: The developed approach for identifying the melodies is based on computational techniques. This work will certainly provide a basis for conducting further computational studies in folk music for any community.

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Section: Related Workmentioning

confidence: 99%

Classification of Assamese Folk Songs’ Melody using Supervised Learning Techniques

Bora¹,

Barman²,

Patowary³

et al. 2023

IJST

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“…Most machine learning (ML) methods consider features such as: pitch, beat, tempo, rhythm, melody or harmony. These were successfully utilized as inputs for Support Vector Machines and Naive Bayes models [15]. However, traditional machine learning techniques under perform when compared to deep learning methods.…”

Section: Introductionmentioning

confidence: 99%

Music Recommendations Based on User's Mood Using Convolutional Neural Networks

Petrescu¹

2022

Studia UBB Informatica

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"This paper proposes a method for music recommendations using emotions, using deep learning techniques. The method is composed of two modules. The emotion detection module, which utilizes a hybrid architecture involving a Convolutional Neural Network (CNN) and a Reccurent Neural Network using Long-Short Term Memory (LSTM) Cells. We compared individual architectures of CNNs and LSTMs against our hybrid approach, outperforming them during experiments. We evaluated the modules on our own data set, created using Spotify’s API and containing 2028 songs from different genres and linguistic families, labeled with valence and arousal values. The model also outperforms other related approaches, however we did not evaluate them on the same data set. The predictions are used by the second module, for which we proposed a simple method of ordering the results based on the similarity to user’s input. Keywords and phrases: mood, emotion, valence, energy, convolutional neural network, recurrent neural networks, long-short term memory, hybrid, regression, classification. "

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