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This work proposes a deep learning framework applied for Acoustic Scene Classification (ASC), targeting DCASE2019 task 1A. In general, the front-end process shows a combination of three types of spectrograms: Gammatone (GAM), log-Mel and Constant Q Transform (CQT). The back-end classification presents a joined learning model between CDNN and CRNN. Our experiments over the development dataset of DCASE2019 challenge task 1A show a significant improvement, increasing 11.2% compared to DCASE2019 baseline of 62.5%. The Kaggle reports the classification accuracy of 74.6% when we train all development dataset.