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@article{175611,
        author = {Dhruthi N Bharadwaj and Vaishnavi S and Vidyashree C and Dr. Laxmi V},
        title = {SURVEY OF DIFFERENT AI MODELS FOR MUSIC AND LYRICS GENERATION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {3559-3574},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=175611},
        abstract = {The fast development of artificial intelligence (AI) has greatly impacted creative industries, such as music and lyrical composition. This paper provides an extensive comparison of different state-of-the-art AI models developed for music and lyrics generation. The study explores the architecture, capabilities, input-output mechanisms, and creative potential of models such as OpenAI’s GPT-3.5, Meta’s MusicGen, OpenAI’s Jukebox, Google’s Magenta (MusicVAE and NSynth), MuseNet, and other industry-relevant tools like AIVA and Amper Music. By analyzing these models across several criteria including quality of generated output, genre adaptability, user control, and real-world applicability, the paper aims to highlight the strengths and limitations of each system. In addition, it speaks to pressing issues like poor emotional depth, biases in the data, issues of copyright infringement, and technical intricacy involved in fusing meaningful lyrics and harmonized sound. The conclusion finds potential research and innovation tracks in future research and development for AI-generated music, highlighting that such technologies are likely to improve human creativity but revolutionize the music industry as well.},
        keywords = {AI-generated Music, Lyrics Generation, Creative Automation, Music composition models},
        month = {April},
        }