Darshan Patel Reviewer
Approved
1. Relevance and Originality
The paper explores a compelling and timely topic that sits at the crossroads of heritage preservation, computational acoustics, and artificial intelligence. The focus on reconstructing ancient sound environments demonstrates strong innovative value, since historical acoustics is often overlooked in cultural conservation research. The work presents an original perspective by combining traditional heritage science with modern AI based reconstruction methods, which enhances its academic relevance.
2. Methodology
The paper describes several technical approaches including 3D modelling, room acoustic simulation, and AI based analysis to reconstruct historic soundscapes. While the methods are promising, the description would benefit from clearer details on how specific datasets were processed, the criteria used for simulation calibration, and the choice of modelling tools. A brief outline of the workflow or validation procedures would help readers fully understand the methodological structure.
3. Validity and Reliability
The use of machine learning and deep learning to evaluate propagation, resonance, and reverberation is theoretically sound, but the reliability of the findings depends heavily on the quality of historical architectural data. The study would be strengthened by clarifying the accuracy of the heritage sources, the uncertainty ranges in the simulations, and whether any benchmark tests were conducted. Such information would help readers assess the trustworthiness of the results.
4. Clarity and Structure
The paper presents its ideas in a coherent flow, beginning with the importance of historic acoustics and moving toward the technological approaches used for reconstruction. The writing is smooth, although some sections contain dense descriptions that may benefit from clearer segmentation between heritage discussions, technical modelling, and AI analysis. Improving the transitions between these sections would enhance readability.
5. Results and Analysis
The work convincingly argues that AI assisted acoustic reconstruction can offer insights into past ceremonial and musical experiences. However, the discussion could be enriched by including sample outputs, comparative simulations, or specific findings from the case studies. Demonstrating one or two results would make the analytical contribution more concrete and illustrate the practical success of the modelling efforts.
Darshan Patel Reviewer