Ramesh Krishna Mahimalur Reviewer
02 Dec 2025 11:06 AM
Approved
1. Relevance and Originality
This work explores a compelling and culturally significant topic, focusing on the preservation of historical sound environments through advanced computational methods. The combination of heritage studies, acoustic science, and artificial intelligence offers a distinctive interdisciplinary viewpoint. This blend of perspectives enhances the originality of the research and highlights its relevance to both technological and cultural preservation communities.
2. Methodology
The study mentions the use of three dimensional reconstruction, room acoustic simulation, and AI enabled sound analysis. While these approaches are well suited to the research goals, the methodology would be clearer if the authors explained how the input data were selected, what modelling assumptions guided the simulations, and how the predictive models were validated. Greater transparency in these areas would help readers fully appreciate the robustness of the workflow.
3. Validity and Reliability
The integration of computational acoustics with machine learning appears conceptually sound, but the reliability of the results depends on the accuracy of the historical measurements, geometrical reconstructions, and material properties. The work would benefit from a brief discussion of error sources, uncertainty considerations, or validation activities that help confirm the reliability of the simulated sound environments.
4. Clarity and Structure
The paper is written in a clear and engaging manner, with a consistent narrative that explains the cultural motivation behind reconstructing historic soundscapes. However, several sections introduce technical concepts rapidly, which may challenge readers unfamiliar with computational acoustics. Creating more defined sections to separate cultural context, technical modelling, and AI processes would make the structure more accessible and easier to follow.
5. Results and Analysis
The paper highlights the potential of technology driven sound reconstruction for historical interpretation and educational uses. To strengthen the analytical depth, the authors could include sample outputs from the simulations or examples of how the AI component improved predictive accuracy. Presenting a specific insight from the case studies would make the contribution more concrete and demonstrate the real value of the research.
Ramesh Krishna Mahimalur Reviewer