{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T11:06:26Z","timestamp":1776078386070,"version":"3.50.1"},"reference-count":60,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T00:00:00Z","timestamp":1695859200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Princess Nourah bint Abdulrahman University","award":["PNURSP2023R138"],"award-info":[{"award-number":["PNURSP2023R138"]}]},{"name":"Princess Nourah bint Abdulrahman University","award":["R.G.P.2\/513\/44"],"award-info":[{"award-number":["R.G.P.2\/513\/44"]}]},{"name":"King Khalid University (KKU)","award":["PNURSP2023R138"],"award-info":[{"award-number":["PNURSP2023R138"]}]},{"name":"King Khalid University (KKU)","award":["R.G.P.2\/513\/44"],"award-info":[{"award-number":["R.G.P.2\/513\/44"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The musical key serves as a crucial element in a piece, offering vital insights into the tonal center, harmonic structure, and chord progressions while enabling tasks such as transposition and arrangement. Moreover, accurate key estimation finds practical applications in music recommendation systems and automatic music transcription, making it relevant across academic and industrial domains. This paper presents a comprehensive comparison between standard deep learning architectures and emerging vision transformers, leveraging their success in various domains. We evaluate their performance on a specific subset of the GTZAN dataset, analyzing six different deep learning models. Our results demonstrate that DenseNet, a conventional deep learning architecture, achieves remarkable accuracy of 91.64%, outperforming vision transformers. However, we delve deeper into the analysis to shed light on the temporal characteristics of each deep learning model. Notably, the vision transformer and SWIN transformer exhibit a slight decrease in overall performance (1.82% and 2.29%, respectively), yet they demonstrate superior performance in temporal metrics compared to the DenseNet architecture. The significance of our findings lies in their contribution to the field of musical key estimation, where accurate and efficient algorithms play a pivotal role. By examining the strengths and weaknesses of deep learning architectures and vision transformers, we can gain valuable insights for practical implementations, particularly in music recommendation systems and automatic music transcription. Our research provides a foundation for future advancements and encourages further exploration in this area.<\/jats:p>","DOI":"10.3390\/info14100527","type":"journal-article","created":{"date-parts":[[2023,9,28]],"date-time":"2023-09-28T01:51:14Z","timestamp":1695865874000},"page":"527","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Comparative Analysis of Deep Learning Architectures and Vision Transformers for Musical Key Estimation"],"prefix":"10.3390","volume":"14","author":[{"given":"Manav","family":"Garg","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1730-9640","authenticated-orcid":false,"given":"Pranshav","family":"Gajjar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pooja","family":"Shah","sequence":"additional","affiliation":[{"name":"School of Technology, Pandit Deendayal Energy University, Gandhinagar 382426, Gujarat, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Madhu","family":"Shukla","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering\u2014AI and BDA, Marwadi University, Rajkot 360003, Gujarat, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6506-9207","authenticated-orcid":false,"given":"Biswaranjan","family":"Acharya","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering\u2014AI and BDA, Marwadi University, Rajkot 360003, Gujarat, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9895-7606","authenticated-orcid":false,"given":"Vassilis C.","family":"Gerogiannis","sequence":"additional","affiliation":[{"name":"Department of Digital Systems, University of Thessaly, 41500 Larissa, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9964-4134","authenticated-orcid":false,"given":"Andreas","family":"Kanavos","sequence":"additional","affiliation":[{"name":"Department of Informatics, Ionian University, 49100 Corfu, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Humphrey, E.J., and Bello, J.P. (2012, January 12\u201315). Rethinking Automatic Chord Recognition with Convolutional Neural Networks. Proceedings of the 11th IEEE International Conference on Machine Learning and Applications (ICMLA), Boca Raton, FL, USA.","DOI":"10.1109\/ICMLA.2012.220"},{"key":"ref_2","unstructured":"Mauch, M., and Dixon, S. (2010, January 9\u201313). Approximate Note Transcription for the Improved Identification of Difficult Chords. Proceedings of the 11th International Society for Music Information Retrieval Conference (ISMIR), Utrecht, The Netherlands."},{"key":"ref_3","unstructured":"Temperley, D. (2004). The Cognition of Basic Musical Structures, MIT Press."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1037\/0033-295X.89.4.334","article-title":"Tracing the Dynamic Changes in Perceived Tonal Organization in a Spatial Representation of Musical Keys","volume":"89","author":"Krumhansl","year":"1982","journal-title":"Psychol. Rev."},{"key":"ref_5","first-page":"335","article-title":"Key Estimation in Electronic Dance Music","volume":"Volume 9626","author":"Faraldo","year":"2016","journal-title":"Advances in Information Retrieval, Proceedings of the 38th European Conference on IR Research (ECIR), Padua, Italy, 20\u201323 March 2016"},{"key":"ref_6","unstructured":"Noland, K., and Sandler, M. (2007, January 5\u20138). Signal Processing Parameters for Tonality Estimation. Proceedings of the Audio Engineering Society Convention 122, Vienna, Austria."},{"key":"ref_7","unstructured":"Pauws, S. (2004, January 10\u201314). Musical Key Extraction from Audio. Proceedings of the 5th International Conference on Music Information Retrieval (ISMIR), Barcelona, Spain."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"65","DOI":"10.2307\/40285812","article-title":"WWhat\u2019s Key for Key? The Krumhansl-Schmuckler Key-Finding Algorithm Reconsidered","volume":"17","author":"Temperley","year":"1999","journal-title":"Music Percept."},{"key":"ref_9","unstructured":"Giorgi, B.D., Zanoni, M., Sarti, A., and Tubaro, S. (2013, January 9\u201311). Automatic Chord Recognition based on the Probabilistic Modeling of Diatonic Modal Harmony. Proceedings of the 8th International Workshop on Multidimensional Systems, Erlangen, Germany."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1280","DOI":"10.1109\/TASL.2009.2032947","article-title":"Simultaneous Estimation of Chords and Musical Context From Audio","volume":"18","author":"Mauch","year":"2010","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1771","DOI":"10.1109\/TASL.2012.2188516","article-title":"An End-to-End Machine Learning System for Harmonic Analysis of Music","volume":"20","author":"Ni","year":"2012","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1080\/09298215.2014.917684","article-title":"Combining Musicological Knowledge About Chords and Keys in a Simultaneous Chord and Local Key Estimation System","volume":"43","author":"Pauwels","year":"2014","journal-title":"J. New Music Res."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Krumhansl, C.L. (2001). Cognitive Foundations of Musical Pitch, Oxford University Press.","DOI":"10.1093\/acprof:oso\/9780195148367.001.0001"},{"key":"ref_14","unstructured":"Harte, C. (2010). Towards Automatic Extraction of Harmony Information from Music Signals. [Ph.D. Thesis, Queen Mary University of London]."},{"key":"ref_15","unstructured":"Fujishima, T. (1999, January 22\u201328). Realtime Chord Recognition of Musical Sound: A System using Common Lisp Music. Proceedings of the International Computer Music Conference, Beijing, China."},{"key":"ref_16","unstructured":"Juslin, P.N., and Sloboda, J. (2011). Handbook of Music and Emotion: Theory, Research, Applications, Oxford University Press."},{"key":"ref_17","unstructured":"Dowling, W.J., and Harwood, D.L. (1986). Music Cognition, Academic Press."},{"key":"ref_18","unstructured":"Hatten, R.S. (2004). Musical Meaning in Beethoven: Markedness, Correlation, and Interpretation, Indiana University Press."},{"key":"ref_19","unstructured":"G\u00f3mez, E. (2006). Tonal Description of Music Audio Signals. [Ph.D. Thesis, Universitat Pompeu Fabra]."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1109\/TSA.2002.800560","article-title":"Musical Genre Classification of Audio Signals","volume":"10","author":"Tzanetakis","year":"2002","journal-title":"IEEE Trans. Speech Audio Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1038\/s41580-021-00407-0","article-title":"A Guide to Machine Learning for Biologists","volume":"23","author":"Greener","year":"2022","journal-title":"Nat. Rev. Mol. Cell Biol."},{"key":"ref_22","unstructured":"Mehta, N., Shah, P., Gajjar, P., and Ukani, V. (2022). Communication and Intelligent Systems, Springer."},{"key":"ref_23","unstructured":"Senjaliya, H., Gajjar, P., Vaghasiya, B., Shah, P., and Gujarati, P. (2022). Optimization of Rocker-Bogie Mechanism using Heuristic Approaches. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1038\/s41576-021-00434-9","article-title":"Navigating the Pitfalls of Applying Machine Learning in Genomics","volume":"23","author":"Whalen","year":"2022","journal-title":"Nat. Rev. Genet."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Gajjar, P., Dodia, V., Mandaliya, S., Shah, P., Ukani, V., and Shukla, M. (2022, January 24\u201326). Path Planning and Static Obstacle Avoidance for Unmanned Aerial Systems. Proceedings of the International Conference on Advancements in Smart Computing and Information Security, Rajkot, India.","DOI":"10.1007\/978-3-031-23095-0_19"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"428","DOI":"10.1038\/s41570-022-00391-9","article-title":"Evaluation Guidelines for Machine Learning Tools in the Chemical Sciences","volume":"6","author":"Bender","year":"2022","journal-title":"Nat. Rev. Chem."},{"key":"ref_27","first-page":"421","article-title":"Findings on Teaching Machine Learning in High School: A Ten-Year Systematic Literature Review","volume":"22","author":"Martins","year":"2022","journal-title":"Inform. Educ."},{"key":"ref_28","first-page":"89","article-title":"Quadruplet Loss and SqueezeNets for Covid-19 Detection from Chest-X Rays","volume":"30","author":"Gajjar","year":"2022","journal-title":"Comput. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, X. (2022, January 15\u201316). Information Retrieval Method of Professional Music Teaching Based on Hidden Markov Model. Proceedings of the 14th IEEE International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), Changsha, China.","DOI":"10.1109\/ICMTMA54903.2022.00216"},{"key":"ref_30","unstructured":"Murthy, Y.V. (2019). Content-based Music Information Retrieval (CB-MIR) and its Applications Towards Music Recommender System. [Ph.D. Thesis, National Institute of Technology Karnataka]."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s13636-023-00278-7","article-title":"AAM: A Dataset of Artificial Audio Multitracks for Diverse Music Information Retrieval Tasks","volume":"2023","author":"Ostermann","year":"2023","journal-title":"EURASIP J. Audio Speech Music Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3505244","article-title":"Transformers in Vision: A Survey","volume":"54","author":"Khan","year":"2022","journal-title":"ACM Comput. Surv."},{"key":"ref_33","first-page":"28092","article-title":"Post-Training Quantization for Vision Transformer","volume":"34","author":"Liu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Mao, X., Qi, G., Chen, Y., Li, X., Duan, R., Ye, S., He, Y., and Xue, H. (2022, January 18\u201324). Towards Robust Vision Transformer. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.01173"},{"key":"ref_36","unstructured":"Gajjar, P., Shah, P., and Sanghvi, H. (2021). International Conference on Ubiquitous Computing and Intelligent Information Systems, Springer."},{"key":"ref_37","unstructured":"Raphael, C. (2010, January 21\u201324). Music Plus One and Machine Learning. Proceedings of the 27th International Conference on Machine Learning (ICML), Haifa, Israel."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1109\/JSTSP.2019.2908700","article-title":"Deep Learning for Audio Signal Processing","volume":"13","author":"Purwins","year":"2019","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1002\/pra2.620","article-title":"Uncovering Black Fantastic: Piloting A Word Feature Analysis and Machine Learning Approach for Genre Classification","volume":"59","author":"Parulian","year":"2022","journal-title":"Proc. Assoc. Inf. Sci. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"10183","DOI":"10.1016\/j.aej.2022.03.060","article-title":"A Hybrid Deep Learning Approach for Musical Difficulty Estimation of Piano Symbolic Music","volume":"61","author":"Ghatas","year":"2022","journal-title":"Alex. Eng. J."},{"key":"ref_41","unstructured":"Nagarajan, S.K., Narasimhan, G., Mishra, A., and Kumar, R. (2023). Deep Learning Research Applications for Natural Language Processing, IGI Global."},{"key":"ref_42","unstructured":"Huang, H., Zhou, X., and He, R. (December, January 28). Orthogonal Transformer: An Efficient Vision Transformer Backbone with Token Orthogonalization. Proceedings of the NeurIPS, New Orleans, LA, USA."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Arnab, A., Dehghani, M., Heigold, G., Sun, C., Lucic, M., and Schmid, C. (2021, January 11\u201317). ViViT: A Video Vision Transformer. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Virtual.","DOI":"10.1109\/ICCV48922.2021.00676"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Miranda, E.R., and Shaji, H. (2023). Generative Music with Partitioned Quantum Cellular Automata. Appl. Sci., 13.","DOI":"10.3390\/app13042401"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Kaliakatsos-Papakostas, M., Velenis, K., Pasias, L., Alexandraki, C., and Cambouropoulos, E. (2023). An HMM-Based Approach for Cross-Harmonization of Jazz Standards. Appl. Sci., 13.","DOI":"10.3390\/app13031338"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1007\/s10844-019-00582-9","article-title":"Machine Learning for Music Genre: Multifaceted Review and Experimentation with Audioset","volume":"55","author":"Flores","year":"2020","journal-title":"J. Intell. Inf. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1007\/s00521-018-3813-6","article-title":"Deep Learning for Music Generation: Challenges and Directions","volume":"32","author":"Briot","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_48","unstructured":"Mao, H.H., Shin, T., and Cottrell, G.W. (February, January 31). DeepJ: Style-Specific Music Generation. Proceedings of the 12th IEEE International Conference on Semantic Computing (ICSC), Laguna Hills, CA, USA."},{"key":"ref_49","first-page":"111","article-title":"Music Tempo Estimation: Are We Done Yet?","volume":"3","author":"Schreiber","year":"2020","journal-title":"Trans. Int. Soc. Music Inf. Retr."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"19945","DOI":"10.1007\/s11042-022-12432-y","article-title":"Development of an Intelligent Model for Musical Key Estimation using Machine Learning Techniques","volume":"81","author":"George","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"118636","DOI":"10.1016\/j.eswa.2022.118636","article-title":"Holistic Approaches to Music Genre Classification using Efficient Transfer and Deep Learning Techniques","volume":"211","author":"Prabhakar","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_52","unstructured":"(2023, July 09). GTZAN Key Dataset. Available online: https:\/\/github.com\/alexanderlerch\/gtzan_key."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2023, September 20). Deep Residual Learning for Image Recognition. CoRR. abs\/1512.03385. Available online: https:\/\/openaccess.thecvf.com\/content_cvpr_2016\/html\/He_Deep_Residual_Learning_CVPR_2016_paper.html.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_55","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An Image is Worth 16 \u00d7 16 Words: Transformers for Image Recognition at Scale. arXiv."},{"key":"ref_56","unstructured":"Mehta, S., and Rastegari, M. (2021). MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"16329","DOI":"10.1007\/s00521-021-06232-y","article-title":"Deep learning models for forecasting aviation demand time series","volume":"33","author":"Kanavos","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Lyras, A., Vernikou, S., Kanavos, A., Sioutas, S., and Mylonas, P. (2021, January 26\u201328). Modeling Credibility in Social Big Data using LSTM Neural Networks. Proceedings of the 17th International Conference on Web Information Systems and Technologies (WEBIST), Online.","DOI":"10.5220\/0010726600003058"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Savvopoulos, A., Kanavos, A., Mylonas, P., and Sioutas, S. (2018). LSTM Accelerator for Convolutional Object Identification. Algorithms, 11.","DOI":"10.3390\/a11100157"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"19615","DOI":"10.1007\/s00521-022-07650-2","article-title":"Multiclass sentiment analysis on COVID-19-related tweets using deep learning models","volume":"34","author":"Vernikou","year":"2022","journal-title":"Neural Comput. Appl."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/10\/527\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:00:15Z","timestamp":1760130015000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/14\/10\/527"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,28]]},"references-count":60,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2023,10]]}},"alternative-id":["info14100527"],"URL":"https:\/\/doi.org\/10.3390\/info14100527","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,28]]}}}