Download Neural Networks for Physical Parameter Estimation of Plate Reverberation from Impulse Responses ★ This paper presents our Task A submission to the 1st DAFx Parameter Estimation Challenge. We use the official ModalPlate dataset generator to synthesize 1000 one-second plate impulse responses with randomly sampled parameters inside the public ranges. A time-domain CNN-GRU regressor then estimates the six official Task A parameters from each unnormalised waveform. The model combines three one-dimensional convolutional blocks with a bidirectional gated recurrent unit and is trained with mean squared error on min-max normalised targets. The generated data are split into 700/150/150 train/validation/test examples, and the test split is never used during training or model selection. The implementation follows the official Task A format and exports evaluation-compatible prediction files for both development evaluation and blind-set submission.