  We propose a novel neural model compression strategy combining data
augmentation, knowledge transfer, pruning, and quantization for device-robust
acoustic scene classification (ASC). Specifically, we tackle the ASC task in a
low-resource environment leveraging a recently proposed advanced neural network
pruning mechanism, namely Lottery Ticket Hypothesis (LTH), to find a
sub-network neural model associated with a small amount non-zero model
parameters. The effectiveness of LTH for low-complexity acoustic modeling is
assessed by investigating various data augmentation and compression schemes,
and we report an efficient joint framework for low-complexity multi-device ASC,
called \emph{Acoustic Lottery}. Acoustic Lottery could compress an ASC model up
to $1/10^{4}$ and attain a superior performance (validation accuracy of 79.4%
and Log loss of 0.64) compared to its not compressed seed model. All results
reported in this work are based on a joint effort of four groups, namely
GT-USTC-UKE-Tencent, aiming to address the "Low-Complexity Acoustic Scene
Classification (ASC) with Multiple Devices" in the DCASE 2021 Challenge Task
1a.
