  This work is an improved system that we submitted to task 1 of DCASE2023
challenge. We propose a method of low-complexity acoustic scene classification
by a parallel attention-convolution network which consists of four modules,
including pre-processing, fusion, global and local contextual information
extraction. The proposed network is computationally efficient to capture global
and local contextual information from each audio clip. In addition, we
integrate other techniques into our method, such as knowledge distillation,
data augmentation, and adaptive residual normalization. When evaluated on the
official dataset of DCASE2023 challenge, our method obtains the highest
accuracy of 56.10% with parameter number of 5.21 kilo and multiply-accumulate
operations of 1.44 million. It exceeds the top two systems of DCASE2023
challenge in accuracy and complexity, and obtains state-of-the-art result. Code
is at: https://github.com/Jessytan/Low-complexity-ASC.
