Audio-text Retrieval in Context

• URL: http://arxiv.org/abs/2203.13645v2
• Date: Tue, 29 Mar 2022 04:32:47 GMT
• Title: Audio-text Retrieval in Context
• Authors: Siyu Lou, Xuenan Xu, Mengyue Wu, Kai Yu
• Abstract summary: In this work, we investigate several audio features as well as sequence aggregation methods for better audio-text alignment. We build our contextual audio-text retrieval system using pre-trained audio features and a descriptor-based aggregation method. With our proposed system, a significant improvement has been achieved on bidirectional audio-text retrieval, on all metrics including recall, median and mean rank.
• Score: 24.38055340045366
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Audio-text retrieval based on natural language descriptions is a challenging task. It involves learning cross-modality alignments between long sequences under inadequate data conditions. In this work, we investigate several audio features as well as sequence aggregation methods for better audio-text alignment. Moreover, through a qualitative analysis we observe that semantic mapping is more important than temporal relations in contextual retrieval. Using pre-trained audio features and a descriptor-based aggregation method, we build our contextual audio-text retrieval system. Specifically, we utilize PANNs features pre-trained on a large sound event dataset and NetRVLAD pooling, which directly works with averaged descriptors. Experiments are conducted on the AudioCaps and CLOTHO datasets, and results are compared with the previous state-of-the-art system. With our proposed system, a significant improvement has been achieved on bidirectional audio-text retrieval, on all metrics including recall, median and mean rank.

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