  This project involved participation in the DCASE 2022 Competition (Task 6)
which had two subtasks: (1) Automated Audio Captioning and (2) Language-Based
Audio Retrieval. The first subtask involved the generation of a textual
description for audio samples, while the goal of the second was to find audio
samples within a fixed dataset that match a given description. For both
subtasks, the Clotho dataset was used. The models were evaluated on BLEU1,
BLEU2, BLEU3, ROUGEL, METEOR, CIDEr, SPICE, and SPIDEr scores for audio
captioning and R1, R5, R10 and mARP10 scores for audio retrieval. We have
conducted a handful of experiments that modify the baseline models for these
tasks. Our final architecture for Automated Audio Captioning is close to the
baseline performance, while our model for Language-Based Audio Retrieval has
surpassed its counterpart.
