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Contrastive learning has shown remarkable success in the field of multimodalrepresentation learning. In this paper, we propose a pipeline of contrastivelanguage-audio pretraining to develop an audio representation by combiningaudio data with natural language descriptions. To accomplish this target, wefirst release LAION-Audio-630K, a large collection of 633,526 audio-text pairsfrom different data sources. Second, we construct a contrastive language-audiopretraining model by considering different audio encoders and text encoders. Weincorporate the feature fusion mechanism and keyword-to-caption augmentationinto the model design to further enable the model to process audio inputs ofvariable lengths and enhance the performance. Third, we perform comprehensiveexperiments to evaluate our model across three tasks: text-to-audio retrieval,zero-shot audio classification, and supervised audio classification. Theresults demonstrate that our model achieves superior performance intext-to-audio retrieval task. In audio classification tasks, the model achievesstate-of-the-art performance in the zero-shot setting and is able to obtainperformance comparable to models' results in the non-zero-shot setting.LAION-Audio-630K and the proposed model are both available to the public.

Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation