用深度学习从房间结构和材料预测声学衰减曲线，速度快精度高。
Deep Learning-Based Prediction of Energy Decay Curves from Room Geometry and Material Properties
- 输入房间几何与吸声参数，通过LSTM网络直接输出能量衰减曲线。
- 预测的早期衰减时间、混响时间等指标误差极小（如EDT MAE 0.017s）。
- 适用于建筑设计初期快速建模和实时声学评估，泛化能力强。
精确预测能量衰减曲线（EDCs）可实现鲁棒的室内声学分析与关键参数可靠估计。本文提出一种基于深度学习的框架，直接从房间几何结构和表面吸声特性预测EDCs。构建了包含6000个鞋盒型房间的数据集，涵盖真实尺寸、声源-接收器位置及频率相关墙面吸声特性。使用Pyroomacoustics对每种配置仿真房间脉冲响应（RIR），并计算目标EDCs。将归一化房间特征输入长短期记忆（LSTM）网络，映射至对应的EDC。通过平均绝对误差（MAE）和均方根误差（RMSE）在时间维度上评估性能。进一步从预测与目标EDCs中提取早期衰减时间（EDT）、混响时间（T20）和清晰度指数（C50），结果高度一致（如EDT MAE为0.017秒，T20 MAE为0.021秒）。该方法在多样房间场景中具有良好泛化能力，支持早期设计阶段的高效声学建模与实时应用。
原文摘要 · Abstract (English)
Accurate prediction of energy decay curves (EDCs) enables robust analysis of room acoustics and reliable estimation of key parameters. We present a deep learning framework that predicts EDCs directly from room geometry and surface absorption. A dataset of 6000 shoebox rooms with realistic dimensions, source-receiver placements, and frequency-dependent wall absorptions was synthesized. For each configuration we simulate room impulse responses (RIRs) using Pyroomacoustics and compute target EDCs. Normalized room features are provided to a long short-term memory (LSTM) network that maps configuration to EDC. Performance is evaluated with mean absolute error (MAE) and root mean square error (RMSE) over time. We further derive early decay time (EDT), reverberation time (T20), and clarity index (C50) from predicted and target EDCs; close agreement is observed (e.g., EDT MAE 0.017 s, T20 MAE 0.021 s). The approach generalizes across diverse rooms and supports efficient room-acoustics modeling for early-stage design and real-time applications.
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