Contrastive learning has shown remarkable success in the field of multimodal representation learning. In this paper, we propose a pipeline of contrastive language-audio pretraining to develop an audio representation by combining audio data with natural language descriptions. To accomplish this target, we first release LAION-Audio-630K, a large collection of 633,526 audio-text pairs from different data sources. Second, we construct a contrastive language-audio pretraining model by considering different audio encoders and text encoders. We incorporate the feature fusion mechanism and keyword-to-caption augmentation into the model design to further enable the model to process audio inputs of variable lengths and enhance the performance. Third, we perform comprehensive experiments to evaluate our model across three tasks: text-to-audio retrieval, zero-shot audio classification, and supervised audio classification. The results demonstrate that our model achieves superior performance in text-to-audio retrieval task. In audio classification tasks, the model achieves state-of-the-art performance in the zero-shot setting and is able to obtain performance comparable to models' results in the non-zero-shot setting. LAION-Audio-630K and the proposed model are both available to the public.

翻译：对比学习在多模态表示学习领域取得了显著成功。本文提出了一种对比语言-音频预训练流程，通过结合音频数据与自然语言描述来开发音频表示。为实现该目标，我们首先发布了LAION-Audio-630K数据集，这是一个包含来自不同数据源的633,526条音频-文本对的大型集合。其次，我们考虑不同的音频编码器和文本编码器，构建了对比语言-音频预训练模型。在模型设计中，我们融入了特征融合机制和关键词到标题增强技术，以进一步支持模型处理变长音频输入并提升性能。第三，我们开展了全面的实验，在三个任务上评估模型：文本到音频检索、零样本音频分类和有监督音频分类。结果表明，我们的模型在文本到音频检索任务中表现出色。在音频分类任务中，该模型在零样本设置下达到了最先进性能，并能获得与非零样本设置下模型结果相当的性能。LAION-Audio-630K数据集及所提出的模型均已公开提供。