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Data clustering analysis of early reflections in small room
Zhaoqi Zhang1, Ge Zhu1, Yong Shen1
1Institute of Acoustics, Nanjing University, Nanjing 210093, China zhangzhaoqi03@gmail.com, gzhu06@outlook.com, yshen@nju.edu.cn.
The Journal of the Acoustical Society of America
|November 9, 2018
Summary
Increasing measurement points improves room equalization, but is time-consuming. This study shows selecting key measurement positions can maintain robustness while reducing effort.
Area of Science:
- Acoustics
- Signal Processing
- Computational Auditory Scene Analysis
Background:
- Multipoint room equalization aims to enhance acoustic performance by increasing measurement points.
- Extensive measurements are time-consuming and labor-intensive, posing practical challenges.
Purpose of the Study:
- To investigate methods for reducing measurement points in multipoint room equalization.
- To analyze the spatial distribution of early reflections for optimized measurement strategies.
Main Methods:
- Utilized K-means clustering algorithm to analyze early reflections from numerous room impulse responses.
- Employed Monte Carlo simulations to evaluate the impact of measurement position selection.
Main Results:
- Early reflections exhibit regular and predictable spatial distributions within K-means clusters.
- Appropriate selection of measurement positions can significantly reduce the required number of points.
Conclusions:
- Optimized measurement point selection offers a viable strategy to reduce data acquisition time in room equalization.
- The findings suggest that robustness can be maintained with fewer, strategically chosen measurement locations.
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