自动音频字幕生成与基于语言的音频检索
Automated Audio Captioning and Language-Based Audio Retrieval
AI总结：
本项目参与DCASE 2022任务6，针对自动音频字幕生成与基于语言的音频检索两个子任务，基于Clotho数据集改进基线模型，最终音频检索模型超越基线，字幕生成接近基线。
摘要：
本项目参与了DCASE 2022竞赛（任务6），该竞赛包含两个子任务：（1）自动音频字幕生成和（2）基于语言的音频检索。第一个子任务涉及为音频样本生成文本描述，而第二个子任务的目标是在固定数据集中找到与给定描述匹配的音频样本。对于这两个子任务，均使用了Clotho数据集。模型在音频字幕生成方面通过BLEU1、BLEU2、BLEU3、ROUGEL、METEOR、CIDEr、SPICE和SPIDEr分数进行评估，在音频检索方面通过R1、R5、R10和mARP10分数进行评估。我们进行了一系列实验来修改这些任务的基线模型。我们的自动音频字幕生成最终架构接近基线性能，而我们的基于语言的音频检索模型则超越了其对应基线。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
