Audioclip: Extending Clip to Image, Text and Audio
Andrey Guzhov, Federico Raue, J.J. van Hees, Andreas R. Dengel · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · 2022
The rapidly evolving field of sound classification has greatly benefited from the methods of other domains. Today, the trend is to fuse domain-specific tasks and approaches together, which provides the community with new outstanding models.We present AudioCLIP – an extension of the CLIP model that handles audio in addition to text and images. Utilizing the AudioSet dataset, our proposed model incorporates the ESResNeXt audio-model into the CLIP framework, thus enabling it to perform multimodal classification and keeping CLIP’s zero-shot capabilities.AudioCLIP achieves new state-of-the-art results in the Environmental Sound Classification (ESC) task and out-performs others by reaching accuracies of 97.15 % on ESC-50 and 90.07 % on UrbanSound8K. Further, it sets new baselines in the zero-shot ESC-task on the same datasets (69.40 % and 68.78 %, respectively).We also asses the influence of different training setups on the final performance of the proposed model. For the sake of reproducibility, our code is published.