  In this paper, we presents a low-complexity deep learning frameworks for
acoustic scene classification (ASC). The proposed framework can be separated
into three main steps: Front-end spectrogram extraction, back-end
classification, and late fusion of predicted probabilities. First, we use Mel
filter, Gammatone filter and Constant Q Transfrom (CQT) to transform raw audio
signal into spectrograms, where both frequency and temporal features are
presented. Three spectrograms are then fed into three individual back-end
convolutional neural networks (CNNs), classifying into ten urban scenes.
Finally, a late fusion of three predicted probabilities obtained from three
CNNs is conducted to achieve the final classification result. To reduce the
complexity of our proposed CNN network, we apply two model compression
techniques: model restriction and decomposed convolution. Our extensive
experiments, which are conducted on DCASE 2021 (IEEE AASP Challenge on
Detection and Classification of Acoustic Scenes and Events) Task 1A development
dataset, achieve a low-complexity CNN based framework with 128 KB trainable
parameters and the best classification accuracy of 66.7%, improving DCASE
baseline by 19.0%
