{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T02:14:33Z","timestamp":1768270473210,"version":"3.49.0"},"reference-count":61,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T00:00:00Z","timestamp":1642636800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Taiwan University of Science and Technology\u2014Taipei Medical University Joint Research Program","award":["NTUST-TMU-110-02"],"award-info":[{"award-number":["NTUST-TMU-110-02"]}]},{"name":"National Taiwan University of Science and Technology\u2014Taipei Medical University Joint Research Program","award":["TMU-NTUST-103-04"],"award-info":[{"award-number":["TMU-NTUST-103-04"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Music can generate a positive effect in runners\u2019 performance and motivation. However, the practical implementation of music intervention during exercise is mostly absent from the literature. Therefore, this paper designs a playback sequence system for joggers by considering music emotion and physiological signals. This playback sequence is implemented by a music selection module that combines artificial intelligence techniques with physiological data and emotional music. In order to make the system operate for a long time, this paper improves the model and selection music module to achieve lower energy consumption. The proposed model obtains fewer FLOPs and parameters by using logarithm scaled Mel-spectrogram as input features. The accuracy, computational complexity, trainable parameters, and inference time are evaluated on the Bi-modal, 4Q emotion, and Soundtrack datasets. The experimental results show that the proposed model is better than that of Sarkar et al. and achieves competitive performance on Bi-modal (84.91%), 4Q emotion (92.04%), and Soundtrack (87.24%) datasets. More specifically, the proposed model reduces the computational complexity and inference time while maintaining the classification accuracy, compared to other models. Moreover, the size of the proposed model for network training is small, which can be applied to mobiles and other devices with limited computing resources. This study designed the overall playback sequence system by considering the relationship between music emotion and physiological situation during exercise. The playback sequence system can be adopted directly during exercise to improve users\u2019 exercise efficiency.<\/jats:p>","DOI":"10.3390\/s22030777","type":"journal-article","created":{"date-parts":[[2022,1,20]],"date-time":"2022-01-20T22:51:06Z","timestamp":1642719066000},"page":"777","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Music Playback Algorithm Based on Residual-Inception Blocks for Music Emotion Classification and Physiological Information"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5333-8783","authenticated-orcid":false,"given":"Yi-Jr","family":"Liao","sequence":"first","affiliation":[{"name":"Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei-Chun","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Humanities and Social Sciences, National Taiwan University of Science and Technology, Taipei 106, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1075-8512","authenticated-orcid":false,"given":"Shanq-Jang","family":"Ruan","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 106, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu-Hao","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Physical Medicine and Rehabilitation, Shuang Ho Hospital, Taipei Medical University, Taipei 106, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8630-1271","authenticated-orcid":false,"given":"Shih-Ching","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, Taipei 106, Taiwan"},{"name":"School of Medicine, College of Medicine, Taipei Medical University, Taipei 106, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,20]]},"reference":[{"key":"ref_1","first-page":"539","article-title":"The effects of exercise on mood changes: The moderating effect of depressed mood","volume":"41","author":"Lane","year":"2001","journal-title":"J. Sports Med. Phys. Fit."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1503\/cmaj.051351","article-title":"Health benefits of physical activity: The evidence","volume":"174","author":"Warburton","year":"2006","journal-title":"CMAJ Can. Med. Assoc. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.tips.2007.02.002","article-title":"Beneficial health effects of exercise\u2014The role of IL-6 as a myokine","volume":"28","author":"Pedersen","year":"2007","journal-title":"Trends Pharmacol. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1007\/s12603-011-0076-7","article-title":"Physical function predicts improvement in quality of life in elderly Icelanders after 12 weeks of resistance exercise","volume":"16","author":"Geirsdottir","year":"2012","journal-title":"J. Nutr. Health Aging"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1136\/hrt.2005.064600","article-title":"Cardiovascular, cerebrovascular, and respiratory changes induced by different types of music in musicians and non-musicians: The importance of silence","volume":"92","author":"Bernardi","year":"2006","journal-title":"Heart"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1868","DOI":"10.1136\/hrt.2010.209858","article-title":"The effects of music on the cardiovascular system and cardiovascular health","volume":"96","author":"Trappe","year":"2010","journal-title":"Heart"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1038\/238279a0","article-title":"Control of the heart rate by external stimuli","volume":"238","author":"Bason","year":"1972","journal-title":"Nature"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"953","DOI":"10.1080\/02640414.2012.676665","article-title":"The BASES Expert Statement on use of music in exercise","volume":"30","author":"Karageorghis","year":"2012","journal-title":"J. Sports Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1207\/S15327655JCHN2001_03","article-title":"The use of music to promote sleep in older women","volume":"20","author":"Johnson","year":"2003","journal-title":"J. Community Health Nurs."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1111\/j.1365-2648.2005.03563.x","article-title":"The effect of music on preoperative anxiety in day surgery","volume":"52","author":"Cooke","year":"2005","journal-title":"J. Adv. Nurs."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"912","DOI":"10.1177\/0031512520928244","article-title":"Effects of preferred and nonpreferred warm-up music on exercise performance","volume":"127","author":"Karow","year":"2020","journal-title":"Percept. Mot. Skills"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1109\/MPRV.2005.47","article-title":"A personalized music system for motivation in sport performance","volume":"4","author":"Wijnalda","year":"2005","journal-title":"IEEE Pervasive Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1186\/s40798-015-0025-9","article-title":"Spontaneous entrainment of running cadence to music tempo","volume":"1","author":"Moens","year":"2015","journal-title":"Sports Med.-Open"},{"key":"ref_14","first-page":"300","article-title":"Music therapy and Alzheimer\u2019s disease: Cognitive, psychological, and behavioural effects","volume":"32","author":"Gallego","year":"2017","journal-title":"Neurolog\u00eda"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Cheng, J.C., Chiu, C.Y., and Su, T.J. (2019). Training and evaluation of human cardiorespiratory endurance based on a fuzzy algorithm. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16132390"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Pao, T.L., Chen, Y.T., Yeh, J.H., Cheng, Y.M., and Lin, Y.Y. (2007). A comparative study of different weighting schemes on KNN-based emotion recognition in Mandarin speech. International Conference on Intelligent Computing, Springer.","DOI":"10.1007\/978-3-540-74171-8_101"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yadav, A., and Vishwakarma, D.K. (2020, January 1\u20133). A multilingual framework of CNN and bi-LSTM for emotion classification. Proceedings of the 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), Kharagpur, India.","DOI":"10.1109\/ICCCNT49239.2020.9225614"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1055\/s-2007-971876","article-title":"Effect of music on perceived exertion, plasma lactate, norepinephrine and cardiovascular hemodynamics during treadmill running","volume":"19","author":"Szmedra","year":"1998","journal-title":"Int. J. Sports Med."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"35","DOI":"10.5604\/20831862.1029819","article-title":"Effect of music on anaerobic exercise performance","volume":"30","author":"Atan","year":"2013","journal-title":"Biol. Sport"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1080\/1750984X.2011.631026","article-title":"Music in the exercise domain: A review and synthesis (Part I)","volume":"5","author":"Karageorghis","year":"2012","journal-title":"Int. Rev. Sport Exerc. Psychol."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Karageorghis, C.I. (2016). Applying Music in Exercise and Sport, Human Kinetics.","DOI":"10.4324\/9781315621364-32"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1055\/s-2004-815715","article-title":"Effects of music on work-rate distribution during a cycling time trial","volume":"25","author":"Atkinson","year":"2004","journal-title":"Int. J. Sports Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1597","DOI":"10.1080\/00140130600899104","article-title":"The effects of music tempo and loudness level on treadmill exercise","volume":"49","author":"Edworthy","year":"2006","journal-title":"Ergonomics"},{"key":"ref_24","first-page":"425","article-title":"Effects of music during exercise on RPE, heart rate and the autonomic nervous system","volume":"46","author":"Yamashita","year":"2006","journal-title":"J. Sports Med. Phys. Fit."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1080\/02640410903150467","article-title":"The effect of attentional focus on running economy","volume":"27","author":"Hagemann","year":"2009","journal-title":"J. Sports Sci."},{"key":"ref_26","first-page":"91","article-title":"Effects of music on arousal, affect, and mood following moderate-intensity cycling","volume":"2","author":"Carmichael","year":"2018","journal-title":"Int. J. Exerc. Sci. Conf. Proc."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1114","DOI":"10.3389\/fpsyg.2018.01114","article-title":"The heat is on: Effects of synchronous music on psychophysiological parameters and running performance in hot and humid conditions","volume":"9","author":"Nikol","year":"2018","journal-title":"Front. Psychol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1111\/j.1600-0838.2009.00948.x","article-title":"Effects of music tempo upon submaximal cycling performance","volume":"20","author":"Waterhouse","year":"2010","journal-title":"Scand. J. Med. Sci. Sports"},{"key":"ref_29","unstructured":"Terry, P.C., and Karageorghis, C.I. (2020, April 13). Music in Sport and Exercise. Available online: https:\/\/eprints.usq.edu.au\/19163\/."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.psychsport.2018.05.002","article-title":"The influence of music genre on explosive power, repetitions to failure and mood responses during resistance exercise","volume":"37","author":"Moss","year":"2018","journal-title":"Psychol. Sport Exerc."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1249\/00005768-198205000-00012","article-title":"Psychophysical bases of perceived exertion","volume":"14","author":"Borg","year":"1982","journal-title":"Med. Sci. Sports Exerc."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e6164","DOI":"10.7717\/peerj.6164","article-title":"High tempo music prolongs high intensity exercise","volume":"6","author":"Maddigan","year":"2019","journal-title":"PeerJ"},{"key":"ref_33","unstructured":"Liu, X., Chen, Q., Wu, X., Liu, Y., and Liu, Y. (2017). CNN based music emotion classification. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1622","DOI":"10.2991\/ijcis.d.191216.001","article-title":"Music emotion recognition by using chroma spectrogram and deep visual features","volume":"12","author":"Er","year":"2019","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_35","first-page":"760","article-title":"Music emotion recognition using convolutional long short term memory deep neural networks","volume":"24","author":"Hizlisoy","year":"2021","journal-title":"Eng. Sci. Technol. Int. J."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1161","DOI":"10.1037\/h0077714","article-title":"A circumplex model of affect","volume":"39","author":"Russell","year":"1980","journal-title":"J. Personal. Soc. Psychol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1111\/jopy.12258","article-title":"The relation between valence and arousal in subjective experience varies with personality and culture","volume":"85","author":"Kuppens","year":"2017","journal-title":"J. Personal."},{"key":"ref_38","first-page":"3511","article-title":"Non-speech environmental sound classification using SVMs with a new set of features","volume":"8","author":"Uzkent","year":"2012","journal-title":"Int. J. Innov. Comput. Inf. Control"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1733","DOI":"10.1109\/TMM.2015.2428998","article-title":"Detection and classification of acoustic scenes and events","volume":"17","author":"Stowell","year":"2015","journal-title":"IEEE Trans. Multimed."},{"key":"ref_40","first-page":"45","article-title":"Investigating the Putative Mechanisms Mediating the Beneficial Effects of Exercise on the Brain and Cognitive Function","volume":"8","author":"Mashhadi","year":"2021","journal-title":"Int. J. Med. Rev."},{"key":"ref_41","first-page":"1096","article-title":"Unsupervised feature learning for audio classification using convolutional deep belief networks","volume":"22","author":"Lee","year":"2009","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_42","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A.A. (2017, January 4\u20139). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_44","unstructured":"Panda, R., Malheiro, R., and Paiva, R.P. (2018, January 23\u201327). Musical texture and expressivity features for music emotion recognition. Proceedings of the 19th International Society for Music Information Retrieval Conference (ISMIR 2018), Paris, France."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1177\/0305735610362821","article-title":"A comparison of the discrete and dimensional models of emotion in music","volume":"39","author":"Eerola","year":"2011","journal-title":"Psychol. Music"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Elliott, G.T., and Tomlinson, B. (2006, January 22\u201327). PersonalSoundtrack: Context-aware playlists that adapt to user pace. Proceedings of the CHI\u201906 Extended Abstracts on Human Factors in Computing Systems, Montr\u00e9al, QC, Canada.","DOI":"10.1145\/1125451.1125599"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"De Oliveira, R., and Oliver, N. (2008, January 2\u20135). TripleBeat: Enhancing exercise performance with persuasion. Proceedings of the 10th International Conference on Human Computer Interaction with Mobile Devices and Services, Amsterdam, The Netherlands.","DOI":"10.1145\/1409240.1409268"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"E206","DOI":"10.1055\/s-0043-120195","article-title":"Validity and reliability of the Apple Watch for measuring heart rate during exercise","volume":"1","author":"Khushhal","year":"2017","journal-title":"Sports Med. Int. Open"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"15607","DOI":"10.1007\/s11042-016-3860-x","article-title":"Develop a personalized intelligent music selection system based on heart rate variability and machine learning","volume":"76","author":"Chiu","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_50","unstructured":"Malik, M. (2008). Standard measurement of heart rate variability. Dynamic Electrocardiography, Wiley."},{"key":"ref_51","unstructured":"Medicore (2020, July 17). SA-3000P Clinical Manual Version 3.0. Retrieved: 8 June 2015. Available online: https:\/\/therisingsea.org\/notes\/FoundationsForCategoryTheory.pdf."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_53","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 7\u20139). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"765","DOI":"10.1007\/s11042-019-08192-x","article-title":"Recognition of emotion in music based on deep convolutional neural network","volume":"79","author":"Sarkar","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_55","unstructured":"Lin, M., Chen, Q., and Yan, S. (2013). Network in network. arXiv."},{"key":"ref_56","unstructured":"Han, Y., and Lee, K. (2016, January 3). Convolutional neural network with multiple-width frequency-delta data augmentation for acoustic scene classification. Proceedings of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events, Budapest, Hungary."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"McFee, B., Raffel, C., Liang, D., Ellis, D.P., McVicar, M., Battenberg, E., and Nieto, O. (2015, January 6\u201312). librosa: Audio and music signal analysis in python. Proceedings of the 14th Python in Science Conference, Austin, TX, USA.","DOI":"10.25080\/Majora-7b98e3ed-003"},{"key":"ref_58","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1007\/s10772-020-09781-0","article-title":"Development of music emotion classification system using convolution neural network","volume":"24","author":"Chaudhary","year":"2021","journal-title":"Int. J. Speech Technol."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"1802","DOI":"10.1109\/TASL.2010.2101596","article-title":"Generalizability and simplicity as criteria in feature selection: Application to mood classification in music","volume":"19","author":"Saari","year":"2010","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_61","unstructured":"Chen, N., and Wang, S. (2017, January 23\u201327). High-Level Music Descriptor Extraction Algorithm Based on Combination of Multi-Channel CNNs and LSTM. Proceedings of the ISMIR, Suzhou, China."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/777\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:04:39Z","timestamp":1760133879000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/3\/777"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,20]]},"references-count":61,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["s22030777"],"URL":"https:\/\/doi.org\/10.3390\/s22030777","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,20]]}}}