  Recent high-performance transformer-based speech enhancement models
demonstrate that time domain methods could achieve similar performance as
time-frequency domain methods. However, time-domain speech enhancement systems
typically receive input audio sequences consisting of a large number of time
steps, making it challenging to model extremely long sequences and train models
to perform adequately. In this paper, we utilize smaller audio chunks as input
to achieve efficient utilization of audio information to address the above
challenges. We propose a dual-phase audio transformer for denoising (DPATD), a
novel model to organize transformer layers in a deep structure to learn clean
audio sequences for denoising. DPATD splits the audio input into smaller
chunks, where the input length can be proportional to the square root of the
original sequence length. Our memory-compressed explainable attention is
efficient and converges faster compared to the frequently used self-attention
module. Extensive experiments demonstrate that our model outperforms
state-of-the-art methods.
