  Automatic Audio Captioning (AAC) refers to the task of translating audio into
a natural language that describes the audio events, source of the events and
their relationships. The limited samples in AAC datasets at present, has set up
a trend to incorporate transfer learning with Audio Event Detection (AED) as a
parent task. Towards this direction, in this paper, we propose an
encoder-decoder architecture with light-weight (i.e. with lesser learnable
parameters) Bi-LSTM recurrent layers for AAC and compare the performance of two
state-of-the-art pre-trained AED models as embedding extractors. Our results
show that an efficient AED based embedding extractor combined with temporal
attention and augmentation techniques is able to surpass existing literature
with computationally intensive architectures. Further, we provide evidence of
the ability of the non-uniform attention weighted encoding generated as a part
of our model to facilitate the decoder glance over specific sections of the
audio while generating each token.

Sound;Machine Learning;Audio and Speech Processing