  We present a method to develop low-complexity convolutional neural networks
(CNNs) for acoustic scene classification (ASC). The large size and high
computational complexity of typical CNNs is a bottleneck for their deployment
on resource-constrained devices. We propose a passive filter pruning framework,
where a few convolutional filters from the CNNs are eliminated to yield
compressed CNNs. Our hypothesis is that similar filters produce similar
responses and give redundant information allowing such filters to be eliminated
from the network. To identify similar filters, a cosine distance based greedy
algorithm is proposed. A fine-tuning process is then performed to regain much
of the performance lost due to filter elimination. To perform efficient
fine-tuning, we analyze how the performance varies as the number of fine-tuning
training examples changes. An experimental evaluation of the proposed framework
is performed on the publicly available DCASE 2021 Task 1A baseline network
trained for ASC. The proposed method is simple, reduces computations per
inference by 27%, with 25% fewer parameters, with less than 1% drop in
accuracy.
