The Contribution of Music Information Retrieval System Optimization to Music Analysis in the Context of Big Data

[1] Simonetta, F., Ntalampiras, S., & Avanzini, F. (2019, January). Multimodal music information processing and retrieval: Survey and future challenges. In 2019 international workshop on multilayer music representation and processing (MMRP) (pp. 10-18). IEEE. Search in Google Scholar

[2] Pesek, M., Strle, G., Kavčič, A., & Marolt, M. (2017). The Moodo dataset: Integrating user context with emotional and color perception of music for affective music information retrieval. Journal of New Music Research, 46(3), 246-260. Search in Google Scholar

[3] Furner, M., Islam, M. Z., & Li, C. T. (2021). Knowledge discovery and visualisation framework using machine learning for music information retrieval from broadcast radio data. Expert Systems with Applications, 182, 115236. Search in Google Scholar

[4] Bayle, Y., Robine, M., & Hanna, P. (2019). SATIN: a persistent musical database for music information retrieval and a supporting deep learning experiment on song instrumental classification. Multimedia Tools and Applications, 78(3), 2703-2718. Search in Google Scholar

[5] Urbano, J., & Flexer, A. (2018). Statistical analysis of results in music information retrieval: why and how. In Proceedings of the International Society for Music Information Retrieval Conference, Paris, France, pp. xli–xlii. Search in Google Scholar

[6] Stober, S. (2017). Toward studying music cognition with information retrieval techniques: Lessons learned from the OpenMIIR initiative. Frontiers in psychology, 8, 238580. Search in Google Scholar

[7] Cheng, Y. (2020, October). Music information retrieval technology: Fusion of music, artificial intelligence and blockchain. In 2020 3rd international conference on smart blockchain (SmartBlock) (pp. 143-146). IEEE. Search in Google Scholar

[8] Vasu, K., & Choudhary, S. (2022). Music Information Retrieval Using Similarity Based Relevance Ranking Techniques. Scalable Computing: Practice and Experience, 23(3), 103-114. Search in Google Scholar

[9] Schäfer, T., & Mehlhorn, C. (2017). Can personality traits predict musical style preferences? A meta-analysis. Personality and Individual Differences, 116, 265-273. Search in Google Scholar

[10] Oore, S., Simon, I., Dieleman, S., Eck, D., & Simonyan, K. (2020). This time with feeling: Learning expressive musical performance. Neural Computing and Applications, 32, 955-967. Search in Google Scholar

[11] Li, B., Liu, X., Dinesh, K., Duan, Z., & Sharma, G. (2018). Creating a multitrack classical music performance dataset for multimodal music analysis: Challenges, insights, and applications. IEEE Transactions on Multimedia, 21(2), 522-535. Search in Google Scholar

[12] Chang, A., Kragness, H. E., Livingstone, S. R., Bosnyak, D. J., & Trainor, L. J. (2019). Body sway reflects joint emotional expression in music ensemble performance. Scientific reports, 9(1), 205. Search in Google Scholar

[13] Murthy, Y. S., & Koolagudi, S. G. (2018). Content-based music information retrieval (cb-mir) and its applications toward the music industry: A review. ACM Computing Surveys (CSUR), 51(3), 1-46. Search in Google Scholar

[14] Holzapfel, A., Sturm, B. L., & Coeckelbergh, M. (2018). Ethical Dimensions of Music Information Retrieval Technology. Transactions of the International Society for Music Information Retrieval, 1(1), 44-56. Search in Google Scholar

[15] Stefani, D., & Turchet, L. (2022). On the challenges of embedded real-time music information retrieval. In Proceedings of the International Conference on Digital Audio Effects (DAFx) (Vol. 3, pp. 177-184). MDPI (Multidisciplinary Digital Publishing Institute). Search in Google Scholar

[16] Clercq, T. D. (2017). Embracing ambiguity in the analysis of form in pop/rock music, 1982–1991. Music Theory Online, 23(3). Search in Google Scholar

[17] Nieto, O., Mysore, G. J., Wang, C. I., Smith, J. B., Schlüter, J., Grill, T., & McFee, B. (2020). Audio-Based Music Structure Analysis: Current Trends, Open Challenges, and Applications. Transactions of the International Society for Music Information Retrieval, 3(1), 246-264. Search in Google Scholar

[18] Sofer, D. (2020). Specters of Sex: Tracing the Tools and Techniques of Contemporary Music Analysis. Zeitschrift der Gesellschaft für Musiktheorie [Journal of the German-Speaking Society of Music Theory], 17(1), 31-63. Search in Google Scholar

[19] Zhu Xiaojun & Balakrishnan Narayanaswamy.(2024).Exact likelihood inference for Laplace distribution based on generalized hybrid censored samples.Communications in Statistics - Simulation and Computation(1),259-272. Search in Google Scholar

[20] Suzuki Hiroki & Kouchi Toshinori.(2022).Development of a single-scale initial flow field into steady homogeneous turbulence with validating a constructed Fourier spectral analysis.Journal of Physics: Conference Series(1). Search in Google Scholar

[21] A S Bychkov,P V Kubasov,V G Kamenev & A E Dormidonov.(2024).Fourier transform with matched non-linear frequency modulation in femtosecond laser ranging with imbalanced dispersion..The Review of scientific instruments(5). Search in Google Scholar

[22] Frainer Guilherme,Dufourq Emmanuel,Fearey Jack,Dines Sasha,Probert Rachel,Elwen Simon & Gridley Tess.(2023).Automatic detection and taxonomic identification of dolphin vocalisations using convolutional neural networks for passive acoustic monitoring.Ecological Informatics102291-. Search in Google Scholar