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In this paper, we examine the use of Transfer Learning using Pretrained AudioNeural Networks (PANNs), and propose an architecture that is able to betterleverage the acoustic features provided by PANNs for the Automated AudioCaptioning Task. We also introduce a novel self-supervised objective,Reconstruction Latent Space Similarity Regularization (RLSSR). The RLSSR modulesupplements the training of the model by minimizing the similarity between theencoder and decoder embedding. The combination of both methods allows us tosurpass state of the art results by a significant margin on the Clotho datasetacross several metrics and benchmarks.

Automated Audio Captioning using Transfer Learning and Reconstruction Latent Space Similarity Regularization