Automated Audio Captioning and Language-Based Audio Retrieval

• URL: http://arxiv.org/abs/2207.04156v2
• Date: Mon, 15 May 2023 13:54:28 GMT
• Title: Automated Audio Captioning and Language-Based Audio Retrieval
• Authors: Clive Gomes, Hyejin Park, Patrick Kollman, Yi Song, Iffanice Houndayi, Ankit Shah
• Abstract summary: This project involved two subtasks: Automated Audio Captioning and Language-Based Audio Retrieval. For both subtasks, the Clotho dataset was used. 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.
• Score: 3.9065205774219334
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

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