Transparent Peer Review By Scholar9

Paper Title

ACOUSTIC SIMULATION AND AI IN PRESERVING ANCIENT MUSICAL SPACES

Description / Abstract

Palatial, amphitheatrically, and temple musical sites must be preserved to conserve intangible cultural treasures, notably historical soundscapes. Ancient architects and musicians understood sound and designed structures around specific acoustic principles to enhance ceremonial and musical experiences. Unfortunately, environmental changes, structural deterioration, and urbanisation threaten these acoustic environments. We investigate how acoustic modelling and AI may reproduce and preserve the sound of long-lost music performance venues. 3D modelling, room acoustic simulations, and AI-driven sound analysis recreate the original audio experiences. The research analyses case studies of heritage places and historical architectural data, material attributes, and spatial arrangements to anticipate acoustic responses. Artificial intelligence systems use machine learning and deep learning to analyse and improve models of sound propagation, reverberation, and resonance patterns for predictive modelling. Acoustic simulations augmented using artificial intelligence may help us comprehend building acoustics, how people listened to music in the past, and how to conserve cultural treasures for future generations. These methodologies may also guide restoration, VR recreations, and educational platforms, ensuring that historic performance spaces will inspire future generations. The project highlights the interdisciplinary intersection of heritage science, computational acoustics, and AI to preserve human civilisations' auditory history via technology-driven preservation.

Ramesh Krishna Mahimalur

Ramesh Krishna Mahimalur Reviewer

Review Request Accepted

Ramesh Krishna Mahimalur Reviewer

Approved Rating

Relevance and Originality

Methodology

Validity & Reliability

Clarity and Structure

Results and Analysis

Comment

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.

IJ Publication Publisher

Your review has been received, and we would like to thank you for the depth and clarity of your comments. The points you raised will support both the authors and the editorial board in refining the submission. Your contribution demonstrates a strong commitment to scholarly excellence, and we are grateful for your continued collaboration.

Publisher

IJ Publication

All Reviewers

Ramesh Krishna Mahimalur

PRONOY CHOPRA

Darshan Patel

Neelam Gupta

Nimeshkumar Patel