Cacophony: An Improved Contrastive Audio-Text Model
<|reference_start|>Cacophony: An Improved Contrastive Audio-Text Model: Despite recent advancements, audio-text models still lag behind their image-text counterparts in scale and performance. In this paper, we propose to improve both the data scale and the training procedure of audio-text contrastive models. Specifically, we craft a large-scale audio-text dataset containing 13,000 hours of text-labeled audio, using pretrained language models to process noisy text descriptions and automatic captioning to obtain text descriptions for unlabeled audio samples. We first train on audio-only data with a masked autoencoder (MAE) objective, which allows us to benefit from the scalability of unlabeled audio datasets. We then train a contrastive model with an auxiliary captioning objective with the audio encoder initialized from the MAE model. Our final model, which we name Cacophony, achieves state-of-the-art performance on audio-text retrieval tasks, and exhibits competitive results on the HEAR benchmark and other downstream tasks such as zero-shot classification.<|reference_end|>
@article{zhu2024cacophony:,
    title={Cacophony: An Improved Contrastive Audio-Text Model},
    author={Ge Zhu, Jordan Darefsky, Zhiyao Duan},
    journal={arXiv preprint arXiv:2402.06986},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06986},
    primaryClass={cs.SD eess.AS}
}