###### Abstract
A mechanism analysis revealed that the SDEwas highly associated with standing waves inside the seat gaps and with the \"diffusion\" effect on the grazing incident waves by the energy flow vortexes around the top surfaces of the seats. **Miscellaneous (Sound Absorbers, Listeners Perception, Machine Learning)**  
_15. A Parametric Study of the Acoustic Performance of Resonant Absorbers Made of Micro-perforated Membranes and Perforated Panels_  
The paper by [PERSON] and [PERSON] [15] first investigated the reliability of prediction models for perforated and micro-perforated panels by a comparison with measured data. Subsequently, whilst taking advantage of a parametric optimization algorithm, it was shown how to design an absorber covering three octave bands from 500 Hz to 2 kHz with an average sound absorption coefficient of approximately 0.8. Such a solution might be conveniently realized whilst using optically transparent panels, which might offer extra value as they could ensure visual contact whilst remaining neutral in terms of the design. _16. Listeners Sensitivity to Different Locations of Diffusive Surfaces in Performance Spaces: The Case of a Shoebox Concert Hall_  
The effects of diffusive surfaces on the acoustic design parameters in a real shoebox concert hall with variable acoustics (Espace de Projection, IRCAM, Paris, France) were investigated by [PERSON] et al. [16]. Acoustic measurements were performed in six hall configurations by varying the location of the diffusive surfaces over the front, mid and rear part of the lateral walls whilst the other surfaces were maintained absorptive or reflective. Conventional ISO 3382 objective acoustic parameters were evaluated along with a subjective investigation performed by using the ABX method with auralization at two listening positions. _17. A Machine Learning Based Prediction Model for the Sound Absorption Coefficient of a Micro-Expanded Metal Mesh (MEMM)_  
The objective of this study by [PERSON] and [PERSON] [17] was to develop a prediction model for a MEMM via a machine learning approach. An experiment including 14 types of MEMM was first performed in a reverberation room measured according to ISO 354. To predict the sound absorption coefficient of the MEMM, the capability of three conventional models and three machine learning (ML) models of the supervised learning method were studied for the development of the prediction model. **Conclusions**  
The diversity and richness of the presented papers demonstrates the liveliness of the research in the field of architectural acoustics. In this exciting context, it is easy to guess that more novel ideas are yet to appear along with further research and new applications. We hope that this collection will serve as an inspiration for our fellow acousticians, especially the young ones, to explore new ways in architectural acoustics in the future. **Acknowledgments:** We are grateful to all contributors who made this special issue a success. First of all, to the authors who trusted their work to us. Thank you all, one by one. We express our gratitude to the _Applied Sciences_ editorial team for their effective communication and tireless work. We would like to personally thank our assistant editor, [PERSON], for unlimited help, kindness and understanding. [PERSON], thank you so much for your help and positive spirit, even after numerous exchanges of emails to resolve small or big issues. We also thank our initial assistant editor, [PERSON]. Finally, we extend our thanks to all reviewers, who made it possible to evaluate and select the excellent works presented in this special issue. **Conflicts of Interest:** The authors declare no conflict of interest.