Energy Decay Curves Dataset

This Zenodo record provides the Energy Decay Curve (EDC) dataset used to train and evaluate the LSTM-based neural network for room impulse response (RIR) prediction. The dataset consists of 17,640 EDC files stored in .npy format. Each file corresponds to a unique simulated shoebox room configuration, covering a wide range of room geometries, source–receiver placements, and surface absorption properties. The EDCs were computed using standard acoustic simulation methods and are normalized to ensure consistent energy scaling. This dataset enables training and benchmarking of deep learning models for tasks such as: Room acoustic parameter estimation EDC prediction from room features RIR reconstruction using stochastic or neural post-processing methods The dataset pairs naturally with the trained LSTM model and inference pipeline available on GitHub:👉 https://github.com/TUIlmenauAMS/LSTM-Model-Energy-Decay-Curves Dataset Contents Files: 17,640 .npy files Sampling rate: 48 kHz EDC length: 3 seconds File naming: Each filename corresponds to a unique room ID, which maps to the room feature CSV in the repository. This dataset is intended for research and educational use. Please cite appropriately when used in publications.