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This technical report presents a low-complexity CNN-based deep learning framework for acoustic scene classification. Particularly, the proposed architecture constitute of two main steps front-end feature extraction and back-end network. Firstly, spectrogram representation is approached as front-end feature extraction in this framework. Next, the spectrograms extracted are fed into a CNN-based architecture for classification. Obtained experimental results conducted over the DCASE 2020 Task 1B dataset improve DCASE baseline by 7.2%.