Transparent Peer Review By Scholar9
ACOUSTIC SIMULATION AND AI IN PRESERVING ANCIENT MUSICAL SPACES
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.
Darshan Patel Reviewer
02 Dec 2025 10:59 AM
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.
IJ Publication Publisher
Thank you for submitting your evaluation. Your review reflects careful consideration and a strong understanding of the subject matter. The insights you provided will greatly assist us in guiding the authors toward meaningful improvements. We appreciate the clarity and fairness of your assessment, as well as the time you dedicated to completing it.
Darshan Patel Reviewer