Environmental　sound　classification　(ESC)　is　an　important　but　challenging　issue.　In　this　paper,　we　propose　a　new　deep　convolutional　neural　network,　which　uses　concatenated　spectrogram　as　input　features,　for　ESC　task.　This　concatenated　spectrogram　feature　we　adopt　can　increase　the　richness　of　features　compared　with　single　spectrogram.　It　is　generated　by　concatenating　two　regular　spectrograms,　the　Log-Mel　spectrogram　and　the　Log-Gammatone　spectrogram.　The　network　we　propose　uses　convolutional　blocks　to　extract　and　derive　high-level　feature　images　from　concatenated　spectrogram,　and　each　block　is　composed　of　three　convolutional　layers　and　a　pooling　layer.　In　order　to　keep　depth　of　the　network　and　reduce　numbers　of　parameters,　we　use　filter　with　a　small　receptive　field　in　each　convolutional　layer.　Besides,　we　use　the　average　pooling　to　keep　more　information.　Our　method　was　tested　on　ESC-50　and　UrbanSound8K　and　achieved　classification　accuracy　of　83.8%　and　80.3%,　respectively.　The　experimental　results　show　that　the　proposed　method　is　suitable　for　ESC　task.　©　2019　IEEE.