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Multi-label emotion recognition from Indian classical music using gradient descent SNN model

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Abstract

Music enthusiasts are growing exponentially and based on this, many songs are being introduced to the market and stored in signal music libraries. Due to this development emotion recognition model from music contents has received increasing attention in today’s world. Of these technologies, a novel Music Emotion Recognition (MER) system is introduced to meet the ever-increasing demand for easy and efficient access to music information. Even though this system was well-developed it lacks in maintaining accuracy of the system and finds difficulty in predicting multi-label emotion type. To address these shortcomings, in this research article, a novel MER system is developed by inter-linking the pre-processing, feature extraction and classification steps. Initially, pre-processing step is employed to convert larger audio files into smaller audio frames. Afterwards, music related temporal, spectral and energy features are extracted for those pre-processed frames which are subjected to the proposed gradient descent based Spiking Neural Network (SNN) classifier. While learning SNN, it is important to determine the optimal weight values for reducing the training error so that gradient descent optimization approach is adopted. To prove the effectiveness of proposed research, proposed model is compared with conventional classification algorithms. The proposed methodology was experimentally tested using various evaluation metrics and it achieves 94.55% accuracy. Hence the proposed methodology attains a good accuracy measure and outperforms well than other algorithms.

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References

  1. Antelis JM, Falcón LE (2020) Spiking neural networks applied to the classification of motor tasks in EEG signals. Neural Netw 122:130–143

    Article  Google Scholar 

  2. Bajaj V, Taran S, Sengur A (2018) Emotion classification using flexible analytic wavelet transform for electroencephalogram signals. Health Inf Sci Syst 6(1):12

    Article  Google Scholar 

  3. Baniya BK, Lee J (2017) Rough set-based approach for automatic emotion classification of music. J Inf Process Syst 13(2):400–416. https://doi.org/10.3745/JIPS.04.0032

    Article  Google Scholar 

  4. Barthet M, Fazekas G, Sandler M (2012) Music emotion recognition: From content- to context-based models. Computer music modelling and retrieval, pp 228–252

  5. Bashwiner DM, Wertz CJ, Flores RA, Jung RE (2016) Musical creativity “revealed” in brain structure: interplay between motor, default mode, and limbic networks. Sci Rep 6:204–282

    Article  Google Scholar 

  6. Baume C (2013) Evaluation of acoustic features for music emotion recognition. In Audio Engineering Society Convention. Audio Engineering Society, 134

  7. Bhatti AM, Majid M, Anwar SM, Khan B (2016) Human emotion recognition and analysis in response to audio music using brain signals. Comput Hum Behav 65:267–275

    Article  Google Scholar 

  8. Buscicchio CA, Górecki P, Caponetti L (2006) Speech emotion recognition using spiking neural networks. In International Symposium on Methodologies for Intelligent Systems. Springer, Berlin, Heidelberg, pp 38-46

  9. Capizzi G, Sciuto GL, Napoli C, Woźniak M, Gianluca Susi (2020) A spiking neural network-based long-term prediction system for biogas production. Neural Netw 129:271–279

    Article  Google Scholar 

  10. Charles J, Lekamge LS (2019) The applicability of miremotion in emotion classification of Sri Lankan Folk Melodies. Available at SSRN 3496519

  11. Chen A, Dai X (2010) Internal combustion engine vibration analysis with short-term Fourier-transform. In 2010 3rd International Congress on Image and Signal Processing, 9. IEEE, 4088-4091

  12. Costa YMG, Oliveira LS, Koericb AL, Gouyon F (2011) Music genre recognition using spectrograms. IEEE International Conference on Systems, Signals and Image Processing(IWSSIP), pp 1– 4

  13. Du SS, Zhai X, Poczos, Singh A (2018) Gradient descent provably optimizes over-parameterized neural networks. arXiv preprintarXiv:1810.02054

  14. Eyben F, Salomao GL, Sundberg J, Scherer KR, Schuller B (2015) Emotion in the singing voice—a deeper look at acoustic features in the light of automatic classification. EURASIP J Audio Speech Music Process 2015(1):1–9

  15. Furnkranz J, Hullermeier E, Mencia, Brinker K (2008) Multilabel classification viacalibrated label ranking. Mach Learn 73(2):133–153

    Article  Google Scholar 

  16. Gyöngyössy NM, Domonkos M, Botzheim J, Korondi P (2019) Supervised learning with small training set for gesture recognition by spiking neural networks. In 2019 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE, pp 2201-2206

  17. Juslin PN, Laukka P (2010) Expression, perception, and induction of musical emotions: A review and aquestionnaire study of everyday listening. J New Music Res 33(3):216–237

    Google Scholar 

  18. Lin C, Liu M, Hsiung W, Jhang J (2016) Music emotion recognition based on two-level support vector classification. In 2016 International Conference on Machine Learning and Cybernetics (ICMLC) (1: 375-389). IEEE

  19. Liu Y, Liu Y, Zhao Y, Hua KA (2015) What strikes the strings of your heart?—feature mining for music emotion analysis. IEEE Trans Eff Comput 6(3):247–260

    Google Scholar 

  20. Lokhande PS, Tiple B, Systems C (2017) Aframework for emotion identification in music: Deep learning approach. In: 2017International Conference on Intelligent Computing and Control Systems (ICICCS). IEEE, pp 262-266

  21. Malheiro R, Panda R, Gomes P, Paiva RP (2016) Emotionally-relevant features for classification and regression of music lyrics. IEEE Trans Affect Comput 9(2):240–254

    Article  Google Scholar 

  22. Meftah B, Lézoray O, Chaturvedi S, Khurshid AA, Abdelkader Benyettou (2013) Image processing with spiking neuron networks. Artificial Intelligence, Evolutionary Computing and Metaheuristics. Springer, Berlin, Heidelberg, pp 525–544

    Chapter  Google Scholar 

  23. Misron MM, Rosli NB, Manaf NA, Halim HA (2014) Music motion classification (mec): Exploiting vocal and instrumental sound features. Recent Advances on Soft Computing and Data Mining, pp 539–549

  24. Mo S, Niu J (2017) A novel method based on OMPGW method for feature extraction in automatic music mood classification. IEEE Trans Affect Comput 10(3):313–324. https://doi.org/10.1109/TAFFC.2017.2724515

    Article  Google Scholar 

  25. Panda R, Malheiro RM, Paiva RP (2018) Novel audio features for music emotion recognition. IEEE Trans Affect Comput 11(4):614–626. https://doi.org/10.1109/TAFFC.2018.2820691

    Article  Google Scholar 

  26. Pouyanfar S, Sameti H (2014) Music emotion recognition using two level classification. In 2014 Iranian Conference on Intelligent Systems (ICIS), IEEE, pp 1-6

  27. Querlioz D, Bichler O, Dollfus P, Gamrat C (2013) Immunity to device variations in a spiking neural network with memristive nanodevices. IEEE Trans Nanotechnol 12(3):288–295

    Article  Google Scholar 

  28. Rachman FH, Sarno R, Fatichah C (2018) Music emotion classification based on lyrics-audio using corpus based emotion. Int J Electr Comput Eng 8(3):2088–8708

    Google Scholar 

  29. Sanden C, Zhang JZ (2011) An empirical study of multi-label classifiers for music tag annotation. In: Proc. of the 12th International Society for Music Information Retrieval (ISMIR) Conference, pp 717–722

  30. Schmidt EM, Turnbull D, Kim YE (2010) Feature selection for content-based, time-varying musical emotion regression in Proc. of the 11th ACMSIGMM International Conference on Multimedia Information Retrieval (MIR), 267–274

  31. Tsoumakas G, Katakis I, Vla-havas I (2011) Random k-label sets for multilabel classifica- tion. IEEE Trans Knowl Data Eng 23(7):1079–1089

    Article  Google Scholar 

  32. Wieczorkowska AA, Synak P, WRas Z (2006) Multi-label classification of emotions in music. In: Proc of Intelligent Information Processing and Web Mining, 35:307–315

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Authors and Affiliations

  1. School Of SCET, Dr. Vishwanath Karad MIT World Peace University, Pune, India

    Bhavana Tiple

  2. TCS Innovation Labs, Pune, India

    Manasi Patwardhan

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  1. Bhavana Tiple
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  2. Manasi Patwardhan
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Correspondence to Bhavana Tiple.

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Tiple, B., Patwardhan, M. Multi-label emotion recognition from Indian classical music using gradient descent SNN model. Multimed Tools Appl 81, 8853–8870 (2022). https://doi.org/10.1007/s11042-022-11975-4

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