AudioTime: A Temporally-aligned Audio-text Benchmark Dataset
<|reference_start|>AudioTime: A Temporally-aligned Audio-text Benchmark Dataset: Recent advancements in audio generation have enabled the creation of high-fidelity audio clips from free-form textual descriptions. However, temporal relationships, a critical feature for audio content, are currently underrepresented in mainstream models, resulting in an imprecise temporal controllability. Specifically, users cannot accurately control the timestamps of sound events using free-form text. We acknowledge that a significant factor is the absence of high-quality, temporally-aligned audio-text datasets, which are essential for training models with temporal control. The more temporally-aligned the annotations, the better the models can understand the precise relationship between audio outputs and temporal textual prompts. Therefore, we present a strongly aligned audio-text dataset, AudioTime. It provides text annotations rich in temporal information such as timestamps, duration, frequency, and ordering, covering almost all aspects of temporal control. Additionally, we offer a comprehensive test set and evaluation metric to assess the temporal control performance of various models. Examples are available on the https://zeyuxie29.github.io/AudioTime/<|reference_end|>
@article{xie2024audiotime:,
    title={AudioTime: A Temporally-aligned Audio-text Benchmark Dataset},
    author={Zeyu Xie, Xuenan Xu, Zhizheng Wu, and Mengyue Wu},
    journal={arXiv preprint arXiv:2407.02857},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02857},
    primaryClass={cs.SD eess.AS}
}