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This report presents a low-complexity CNN-based deep learning framework for acoustic scene classification task (ASC). In particular, the framework approaches spectrogram representation referred to as front-end feature extraction. The spectrograms extracted are fed into a CNN-based architecture for classification, referred to as the baseline. Next, quantization and pruning techniques are applied on the pre-trained baseline to fine-tune and further compress the network size, eventually achieve low-complexity models with competitive performance.