A Deep Learning Approach for Automatic Music Genre Classification
DOI:
https://doi.org/10.36676/jrps.v14.i5.1562Keywords:
Deep Learning, Music Classification, Recurrent Neural NetworksAbstract
This study uses a deep learning framework, employing recurrent neural networks (RNN), to classify music tracks into different genres based on audio signals.
References
Vasa, Y. (2023). Ethical implications and bias in Generative AI. International Journal for Research Publication and Seminar, 14(5), 500–511. https://doi.org/10.36676/jrps.v14.i5.1541 DOI: https://doi.org/10.36676/jrps.v14.i5.1541
Mallreddy, S. R., & Vasa, Y. (2023). Predictive Maintenance In Cloud Computing And Devops: Ml Models For Anticipating And Preventing System Failures. NVEO-NATURAL VOLATILES & ESSENTIAL OILS Journal| NVEO, 10(1), 213-219. DOI: https://doi.org/10.53555/nveo.v10i1.5751
Vasa, Y., Kilaru, N. B., & Gunnam, V. (2023). Automated Threat Hunting In Finance Next Gen Strategies For Unrivaled Cyber Defense. International Journal of Advances in Engineering and Management, 5(11). https://doi.org/10.35629/5252-0511461470
Vasa, Y., Mallreddy, S. R., & Jaini, S. (2023). AI And Deep Learning Synergy: Enhancing Real-Time Observability And Fraud Detection In Cloud Environments, 6(4), 36–42. https://doi.org/ 10.13140/RG.2.2.12176.83206
Vasa, Y., Singirikonda, P., & Mallreddy, S. R. (2023). AI Advancements in Finance: How Machine Learning is Revolutionizing Cyber Defense. International Journal of Innovative Research in Science, Engineering and Technology, 12(6), 9051–9060.
Mallreddy, S. R., & Vasa, Y. (2023). Natural language querying in SIEM systems: Bridging the gap between security analysts and complex data. NATURAL LANGUAGE QUERYING IN SIEM SYSTEMS: BRIDGING THE GAP BETWEEN SECURITY ANALYSTS AND COMPLEX DATA, 10(1), 205–212. https://doi.org/10.53555/nveo.v10i1.5750 DOI: https://doi.org/10.53555/nveo.v10i1.5750
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 International Journal for Research Publication and Seminar
This work is licensed under a Creative Commons Attribution 4.0 International License.
Re-users must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. This license allows for redistribution, commercial and non-commercial, as long as the original work is properly credited.
