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Automatic music emotion classification using hashtag graph

  • S.I.: Emotion Recognition in Speech
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Abstract

Music is integrated automatically in routine life of human being. It reveals emotion and harmonizes the listener sensation. Human’s present state of mind is interconnected with music. Distinguishing the human emotions on behalf of pitch, rhythm, harmony, melody and interval is a tedious process. Identification of human emotions is done by the machine learning approaches. The classification models that are used for predicting emotions are not very much efficient. To address the above mentioned problem a novel approach using hash tag graph generation is proposed for automatic emotion detection. The proposed method consists of two steps training and testing process. In this paper the proposed method is compared with support vector machines, k-nearest neighbour approach and convolution neural network in terms of accuracy, precision, recall, specificity, f-measure, geometric mean, root mean square error and computational cost. The proposed technique achieves the best performance in terms of all the evaluation parameters.

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Authors and Affiliations

  1. Department of Electronics Engineering, National Institute of Technology, Kurukshetra, Kurukshetra, Haryana, 136119, India

    Deepti Chaudhary & Niraj Pratap Singh

  2. Department of Electrical and Electronics Engineering, National Institute of Technology, Delhi, 110040, India

    Sachin Singh

  3. Electronics and Communication Department, University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, Haryana, 136119, India

    Deepti Chaudhary

Authors
  1. Deepti Chaudhary
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  2. Niraj Pratap Singh
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  3. Sachin Singh
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Correspondence to Deepti Chaudhary.

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Chaudhary, D., Singh, N.P. & Singh, S. Automatic music emotion classification using hashtag graph. Int J Speech Technol 22, 551–561 (2019). https://doi.org/10.1007/s10772-019-09629-2

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  • DOI: https://doi.org/10.1007/s10772-019-09629-2

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