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Automatic identification of preferred music genres: an exploratory machine learning approach to support personalized music therapy

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Abstract

Music accompanies all phases of our lives, and when we reach old age, music becomes a direct symbol of nostalgia. Autobiographical memories are essential to an individual’s sense of identity, continuity, and meaning. But some pathologies, such as dementia, can interrupt the memory storage process. Music can help recall and evoke memories and can be used in alternative treatments for dementia. This work aims to propose an architecture for a music recommendation system capable of recommending music according to musical genre, with the aim of helping music therapists in therapies addressed to elderly people with dementia in initial states. Here we used data from the public music database Emotify, which is composed of 400 songs labeled by 1595 participants in 7975 sessions. Both channels of the songs were windowed using 10s windows with 5s overlap. The data from these windows were represented by 34 time and frequency features. Then, we assessed and compared the performance of classifiers based on support vector machines, decisions trees and Bayesian network. The most suitable architecture in this experimental study was the Random Forest with 250 trees, with an accuracy of 83.42% ± 1.72%, kappa statistic of 0.78 ± 0.02, AUC-ROC of 0.99 ± 0.00, sensitivity of 0.96 ± 0.02, and specificity of 0.94 ± 0.01. this exploratory study found promising results that indicates the possibility of building recommendation systems to support music therapy based on the automatic classification of songs according to the most appropriate musical genre for the patient.

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Data Availability

This work was based on the public dataset Emotify, composed by songs equally organized into the following genres: classical, rock, pop and electronic. There are 100 music tracks for each genre, each track lasting 60 seconds. A total of 1595 participants (651 females and 944 males) labeled the songs regarding emotions they felt, generating 15356 labels for the 400 songs during 7975 listening sessions [24]. The dataset is freely available. Additional data generated specifically for this work by the authors is available under demand.

References

  1. Levine B, Svoboda E, Hay JF, Winocur G, Moscovitch M (2002) Aging and autobiographical memory: dissociating episodic from semantic retrieval. Psychol Aging 17(4):677

    Article  Google Scholar 

  2. Gebka D, Kedziora-Kornatowska K, Podhorecka M, Sielski G, Sucharska-Szymkowiak M, Beck O (2015) Activation of the elderly with dementia syndrome

  3. Mather M, Carstensen LL (2005) Aging and motivated cognition: the positivity effect in attention and memory. Trend Cogn Sci 9(10):496–502

    Article  Google Scholar 

  4. de Melo Alcântara-Silva TR, Miotto EC, Moreira SV(2014) Musicoterapia, reabilitação cognitiva e doença de alzheimer: Revisão sistemática. Brazilian journal of music therapy

  5. Matthews S (2014) Music therapy and dementia: rethinking the debate over advance directives. Ethics education 20

  6. Koh WLE, Low F, Kam JW, Rahim S, Ng WF, Ng LL (2020) Person-centred creative dance intervention for persons with dementia living in the community in singapore. Dement 19(7):2430–2443

    Article  Google Scholar 

  7. Li D-m, Li X-x (2017) The effect of folk recreation program in improving symptoms: a study of chinese elder dementia patients. Int J Geriatr Psychiatr 32(8):901–908

    Article  Google Scholar 

  8. Barcellos LRM (2015) Musicoterapia em medicina: uma tecnologia leve na promoção da saúde – a dança nas poltronas! revista música hodie, 15(2)

  9. Miao D, Lu X, Dong Q, Hong D (2020) Humming-query and reinforcement-learning based modeling approach for personalized music recommendation. Proc Comput Sci 176:2154–2163

    Article  Google Scholar 

  10. Aleixo MAR, Santos RL, Dourado MCdN (2017) Efficacy of music therapy in the neuropsychiatric symptoms of dementia: systematic review. Jornal Brasileiro de Psiquiatria 66:52–61

    Article  Google Scholar 

  11. Van der Steen JT, Smaling HJ, Van der Wouden JC, Bruinsma MS, Scholten RJ, Vink AC (2018) Music-based therapeutic interventions for people with dementia. Cochrane database of systematic reviews, 7

  12. Moreno-Morales C, Calero R, Moreno-Morales P, Pintado C (2020) Music therapy in the treatment of dementia: a systematic review and meta-analysis. Front Med 7:160

    Article  Google Scholar 

  13. Fang R, Ye S, Huangfu J, Calimag DP (2017) Music therapy is a potential intervention for cognition of alzheimer’s disease: a mini-review. Transl Neurodegener 6(1):2

    Article  Google Scholar 

  14. Lam HL, Li WTV, Laher I, Wong RY (2020) Effects of music therapy on patients with dementia-a systematic review. Geriatr 5(4):62

    Article  Google Scholar 

  15. Vink A, Hanser S (2018) Music-based therapeutic interventions for people with dementia: a mini-review. Med 5(4):109

    Google Scholar 

  16. Leontjevas R (2021) Soundscape in nursing homes as a treatment strategy for challenging behavior in dementia? Int Psychogeriatr 33(6):553–556

    Article  Google Scholar 

  17. Wang D, Belden A, Hanser SB, Geddes MR, Loui P (2020) Resting-state connectivity of auditory and reward systems in alzheimer’s disease and mild cognitive impairment. Front Hum Neurosci 14:280

    Article  Google Scholar 

  18. Han E, Park J, Kim H, Jo G, Do H-K, Lee BI (2020) Cognitive intervention with musical stimuli using digital devices on mild cognitive impairment: a pilot study. Healthcare, MDPI, 8(1)

  19. Moss H, Lee S, Clifford AM, Ní Bhriain O, O’Neill D (2022) Together in song: designing a singing for health group intervention for older people living in the community. Nord J Music Ther 31(5):413–430

    Article  Google Scholar 

  20. Shalini SK, Jaichandran R, Leelavathy S, Raviraghul R, Ranjitha J, Saravanakumar N (2021) Facial emotion based music recommendation system using computer vision and machine learning techiniques. Turk J Comput Math Educ 12(2):912–917

    Google Scholar 

  21. Sharma VP, Gaded AS, Chaudhary D, Kumar S, Sharma S (2021) Emotion-based music recommendation system. In: 2021 9th international conference on reliability, Infocom technologies and optimization (trends and future directions)(ICRITO), IEEE, pp 1–5

  22. Angkasa HS, Maulidevi NU (2022) Emotion classification of user face image in music recommendation system. In: 2022 9th international conference on advanced informatics: concepts, theory and applications (ICAICTA), IEEE, pp 1–6

  23. Gupta S (2023) Deep audio embeddings and attention based music emotion recognition. In: 2023 15th international conference on developments in eSystems engineering (DeSE), IEEE, pp 357–362

  24. Aljanaki A, Wiering F, Veltkamp R (2014) Collecting annotations for induced musical emotion via online game with a purpose emotify. UU BETA ICS departement informatica

  25. Espinola CW, Gomes JC, Pereira JMS, dos Santos WP (2021) Detection of major depressive disorder using vocal acoustic analysis and machine learning-an exploratory study. Res Biomed Eng 37(1):53–64

    Article  Google Scholar 

  26. Espinola CW, Gomes JC, Pereira JMS, dos Santos WP (2021) Vocal acoustic analysis and machine learning for the identification of schizophrenia. Res Biomed Eng 37(1):33–46

    Article  Google Scholar 

  27. Jackins V, Vimal S, Kaliappan M, Lee MY (2021) Ai-based smart prediction of clinical disease using random forest classifier and naive bayes. J Supercomput 77(5):5198–5219

    Article  Google Scholar 

  28. Barbosa VAdF, Gomes JC, de Santana MA, de Lima CL, Calado RB, Bertoldo Júnior CR, Albuquerque JEdA, de Souza RG, de Araújo RJE, Mattos Júnior LAR et al (2021) Covid-19 rapid test by combining a random forest-based web system and blood tests. Journal of Biomolecular Structure and Dynamics, 1–20

  29. Barbosa VAdF, Gomes JC, de Santana MA, Jeniffer EdA, de Souza RG, de Souza RE, dos Santos WP (2021) Heg.ia: an intelligent system to support diagnosis of covid-19 based on blood tests. Res Biomed Eng 2021:1–18

    Google Scholar 

  30. da Silva CC, de Lima CL, da Silva ACG, Silva EL, Marques GS, de Araújo LJB, Júnior LAA, de Souza SBJ, de Santana, MA, Gomes JC, et al (2021) Covid-19 dynamic monitoring and real-time spatio-temporal forecasting. Frontiers in public health, 9

  31. De Lima CL, Da Silva CC, Da Silva ACG, Luiz Silva E, Marques GS, De Araújo LJB, Albuquerque Júnior LA, de Souza SBJ, de Santana MA, Gomes JC et al (2020) Covid-sgis: a smart tool for dynamic monitoring and temporal forecasting of covid-19. Front Public Health 8:580815

  32. Gomes JC, Barbosa VAdF, de Santana MA, de Lima CL, Calado RB, Junior CRB, de Almeida Albuquerque JE, de Souza RG, de Araujo RJE, Moreno GMM, et al (2021) Rapid protocols to support covid-19 clinical diagnosis based on hematological parameters.

  33. Pahwa P, Papreja M, Miglani R (2014) Performance analysis of classification algorithms. Int J Comput Sci Mob Comput 3(4):50–58

    Google Scholar 

  34. Wang X, Wang S, Huang Z, Du Y (2021) Condensing the solution of support vector machines via radius-margin bound. Appl Soft Comput 101:107071

    Article  Google Scholar 

  35. Ahmad I, Basheri M, Iqbal MJ, Rahim A (2018) Performance comparison of support vector machine, random forest, and extreme learning machine for intrusion detection. IEEE Access 6:33789–33795

    Article  Google Scholar 

  36. Tiwari A, Chaturvedi A (2022) A hybrid feature selection approach based on information theory and dynamic butterfly optimization algorithm for data classification. Expert Syst Appl 196:116621

    Article  Google Scholar 

  37. Tiwari A, Chaturvedi A (2019) A multiclass eeg signal classification model using spatial feature extraction and xgboost algorithm. In: 2019 IEEE/RSJ international conference on intelligent robots and systems (IROS), IEEE, pp 4169–4175

  38. Tiwari A, Chaturvedi A (2021) A novel channel selection method for bci classification using dynamic channel relevance. IEEE Access 9:126698–126716

    Article  Google Scholar 

  39. Tiwari A, Chaturvedi A (2023) Automatic eeg channel selection for multiclass brain-computer interface classification using multiobjective improved firefly algorithm. Multimed Tools Appl 82(4):5405–5433

    Article  Google Scholar 

  40. Tiwari A, Chaturvedi A (2022) Automatic channel selection using multiobjective x-shaped binary butterfly algorithm for motor imagery classification. Expert Syst Appl 206:117757

    Article  Google Scholar 

  41. Tiwari A, Mishra S (2022) Higher-order dynamic mode decomposition and multichannel singular spectrum decomposition hybridization for bci feature extraction. In: 2022 international conference for advancement in technology (ICONAT), IEEE, pp 1–6

  42. Haykin S (2007) Redes Neurais: princípios e prática, 2nd edn. Bookman, São paulo

    Google Scholar 

  43. Xavier, J (2021) As máquinas podem pensar? BIOINFO – revista brasileira de bioinformática e biologia computacional, 221

  44. Galera Monico JF, Dal Poz AP, Galo M, dos Santos MC, De Oliveira LC (2009) Accuracy and precision: reviewing the concepts by means of an accurate procedure. Boletim de Ciências Geodésicas 15(3):469–483

    Google Scholar 

  45. Sim J, Wright CC (2005) The kappa statistic in reliability studies: use, interpretation, and sample size requirements. Phys Ther 85(3):257–268

    Article  Google Scholar 

  46. Powers DM (2020) Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation. arXiv:2010.16061

  47. Lever J, Krzywinski M, Altman N (2016) Points of significance: logistic regression. Nature methods 13(7):541–542

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Acknowledgements

The authors are grateful to the Brazilian research agencies CAPES, FACEPE and CNPq for the partial finantial support of this work.

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Author notes

  1. Ingrid Bruno Nunes, Maíra Araújo de Santana, Nicole Charron, Hyngrid Souza e Silva, Caylane Mayssa de Lima Simões, Camila Lins, Ana Beatriz de Souza Sampaio, Arthur Moreira Nogueira de Melo, Thailson Caetano Valdeci da Silva, Camila Tiodista, Nathália Córdula de Brito, Arianne Sarmento Torcate, Juliana Carneiro Gomes, Giselle Machado Magalhães Moreno, Cristine Martins Gomes de Gusmão, and Wellington Pinheiro dos Santos are contributed equally to this work.

Authors and Affiliations

  1. Escola Politécnica de Pernambuco, Universidade de Pernambuco, Rua Benfica, 455, Madalena, Recife, 50720-001, Pernambuco, Brazil

    Ingrid Bruno Nunes, Maíra Araújo de Santana, Arianne Sarmento Torcate & Wellington Pinheiro dos Santos

  2. Departamento de Engenharia Biomédica, Universidade Federal de Pernambuco, Av. da Arquitetura, sn, Cidade Universitária, Recife, 50740-550, Pernambuco, Brazil

    Nicole Charron, Hyngrid Souza e Silva, Ana Beatriz de Souza Sampaio, Arthur Moreira Nogueira de Melo, Thailson Caetano Valdeci da Silva, Nathália Córdula de Brito, Juliana Carneiro Gomes, Giselle Machado Magalhães Moreno, Cristine Martins Gomes de Gusmão & Wellington Pinheiro dos Santos

  3. Departamento de Psicologia, Universidade Federal de Pernambuco, Av. da Arquitetura, sn, Cidade Universitária, Recife, 50740-550, Pernambuco, Brazil

    Caylane Mayssa de Lima Simões

  4. Instituto Federal de Pernambuco, Recife, Pernambuco, Brazil

    Camila Lins & Camila Tiodista

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  1. Ingrid Bruno Nunes
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Correspondence to Wellington Pinheiro dos Santos.

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Nunes, I.B., de Santana, M.A., Charron, N. et al. Automatic identification of preferred music genres: an exploratory machine learning approach to support personalized music therapy. Multimed Tools Appl 83, 82515–82531 (2024). https://doi.org/10.1007/s11042-024-18826-4

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