  This paper introduces the Unbeatable Team's submission to the ICASSP 2023
Deep Noise Suppression (DNS) Challenge. We expand our previous work, TEA-PSE,
to its upgraded version -- TEA-PSE 3.0. Specifically, TEA-PSE 3.0 incorporates
a residual LSTM after squeezed temporal convolution network (S-TCN) to enhance
sequence modeling capabilities. Additionally, the local-global representation
(LGR) structure is introduced to boost speaker information extraction, and
multi-STFT resolution loss is used to effectively capture the time-frequency
characteristics of the speech signals. Moreover, retraining methods are
employed based on the freeze training strategy to fine-tune the system.
According to the official results, TEA-PSE 3.0 ranks 1st in both ICASSP 2023
DNS-Challenge track 1 and track 2.
