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@article{180546,
        author = {Ashutosh pal and Dr. Pawan Singh},
        title = {Computational Musicology: Neural Network Architectures in Autonomous Composition Systems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {2310-2318},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180546},
        abstract = {Deep neural networks allow AI to use machine learning to create music. It introduces computational creativity, a new research field, with focus on algorithms for melody, harmony and rhythm generation. The field’s researchers often look into using transformers in sequence analysis, variational autoencoders and adversarial testing for improving art. Scholarly work with twelve different writing systems proves that most of these systems are better at writing text, but still struggle with truly understanding context and producing styles that are not copied. Using more than one way to solve a task improves results compared with using a single approach, as studies have demonstrated. Using ensemble techniques increases the coherence of harmonic parts by 73% and brings a 68% better link in melodic progression between segments. Still, professional musicians disagree with pop music because they say it lacks emotional depth and cultural understanding. We think that a significant approach for the future will join fast mathematics with creative human ideas, opening up the option for artists to collaborate.},
        keywords = {Neural Musicology, Algorithmic Composition, Computational Creativity, Deep Learning Applications, Sequence Generation, Creative Automation, Digital Composition, Machine Learning Arts, Automated Music Production, Human-Computer Collaboration.},
        month = {June},
        }