  Dynamic parameterization of acoustic environments has drawn widespread
attention in the field of audio processing. Precise representation of local
room acoustic characteristics is crucial when designing audio filters for
various audio rendering applications. Key parameters in this context include
reverberation time (RT60) and geometric room volume. In recent years, neural
networks have been extensively applied in the task of blind room parameter
estimation. However, there remains a question of whether pure attention
mechanisms can achieve superior performance in this task. To address this
issue, this study employs blind room parameter estimation based on monaural
noisy speech signals. Various model architectures are investigated, including a
proposed attention-based model. This model is a convolution-free Audio
Spectrogram Transformer, utilizing patch splitting, attention mechanisms, and
cross-modality transfer learning from a pretrained Vision Transformer.
Experimental results suggest that the proposed attention mechanism-based model,
relying purely on attention mechanisms without using convolution, exhibits
significantly improved performance across various room parameter estimation
tasks, especially with the help of dedicated pretraining and data augmentation
schemes. Additionally, the model demonstrates more advantageous adaptability
and robustness when handling variable-length audio inputs compared to existing
methods.
