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In this paper, we propose lightweight deep neural networks for Acoustic Scene Classification (ASC) and a visualization method for presenting a sound scene context. To this end, we first propose an inception-based and low-memory footprint ASC model as the ASC baseline. The ASC baseline is then compared with benchmark and high-complexity network architectures. Next, we improve the ASC baseline by proposing a novel deep neural network architecture which leverages a residual-inception architecture and multiple kernels. Given the novel residual-inception (NRI) based model, we apply multiple techniques of model compression to evaluate the trade off between the model complexity and the model accuracy performance. Finally, we evaluate whether sound events detected in a sound scene recording can help to improve ASC accuracy performance and to present the sound scene context more …