The AI revolution in multimedia authoring is not only visual but also profoundly auditory, particularly in music and sound design where traditional workflows have long relied on MIDI sequencing, subtractive or sample-based synthesis, painstaking waveform editing, and tacit craft knowledge embedded in digital audio workstations. Recent diffusion-based systems have opened a different paradigm in which high-level verbal, audio, or stylistic cues can guide sound creation directly. Levy et al. (2023) demonstrated that diffusion models can support musically relevant operations such as continuation, inpainting, transition generation, and style transfer; Schneider et al. (2024) introduced Mousai as an efficient text-to-music diffusion architecture capable of longer, high-quality stereo outputs; and Suckrow et al. (2024) moved closer to actual authoring practice by embedding diffusion-based sound synthesis into a playable digital instrument designed for music production workflows. This is a notable departure from earlier generative music systems, which often produced either symbolic sequences detached from production realities or black-box audio clips with little usable control. Nevertheless, the gap between generation and authorship remains large. Text prompts are effective for mood and texture, but professional composition still depends on structure, timing, harmony, mix discipline, and reproducible revision, all of which remain only partially controllable in current systems. There are also unresolved issues of style ownership, training-data provenance, and evaluation, because short-sample perceptual quality does not capture whether a generated piece supports iterative composition or downstream arrangement. The key research gap, then, is not audio fidelity alone but the lack of interfaces that connect language, stems, humming, notation, and DAW-native control into one coherent authoring loop. Future work should emphasize multimodal control, bar-level and song-level structure, and provenance-aware editing so that generative audio systems become serious creative instruments rather than merely efficient texture generators (Levy et al., 2023; Schneider et al., 2024; Suckrow et al., 2024).
References1. Levy, M., Di Giorgi, B., Weers, F., Katharopoulos, A., & Nickson, T. (2023). Controllable music production with diffusion models and guidance gradients. arXiv preprint arXiv:2311.00613.
2. Schneider, F., Kamal, O., Jin, Z., & Scholkopf, B. (2024). Mousai: Efficient text-to-music diffusion models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024).
3. Suckrow, P.-L. W. L., Weber, C. J., & Rothe, S. (2024). Diffusion-based sound synthesis in music production. In Proceedings of FARM 2024.
S. M. Monowar KayserLecturer, Department of Multimedia & Creative Technology (MCT)
Faculty of Science & Information Technology
Daffodil International University (DIU)
Daffodil Smart City, Savar, Dhaka, Bangladesh
Visit: https://monowarkayser.com/
