Abstract
This article proposes an efficient audio incremental learning method to reduce the computational complexity and catastrophic forgetting during the incremental addition of the audio data in Deep Neural Networks (DNN). The computational complexity is reduced by performing training of only fully-connected layers and catastrophic forgetting is reduced by sharing the knowledge from the old learned classes without using previously learned data. Our method has been evaluated extensively on UrbanSound8K, ESC-10, and TUT datasets where state-of-the-art accuracies have been achieved. Moreover, our method has been evaluated on Nvidia 1080-ti GPU, Nvidia TX-2, and Nvidia Xavier development boards to demonstrate the training time and energy consumption savings as compared to recent state-of-the-art methods.
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