AudioCLIP: Extending CLIP to Image, Text and Audio

24 Jun 2021  ·  Andrey Guzhov, Federico Raue, Jörn Hees, Andreas Dengel ·

In the past, the rapidly evolving field of sound classification greatly benefited from the application of methods from other domains. Today, we observe the trend to fuse domain-specific tasks and approaches together, which provides the community with new outstanding models. In this work, we present an extension of the CLIP model that handles audio in addition to text and images. Our proposed model incorporates the ESResNeXt audio-model into the CLIP framework using the AudioSet dataset. Such a combination enables the proposed model to perform bimodal and unimodal classification and querying, while keeping CLIP's ability to generalize to unseen datasets in a zero-shot inference fashion. AudioCLIP achieves new state-of-the-art results in the Environmental Sound Classification (ESC) task, out-performing other approaches by reaching accuracies of 90.07% on the UrbanSound8K and 97.15% on the ESC-50 datasets. Further it sets new baselines in the zero-shot ESC-task on the same datasets 68.78% and 69.40%, respectively). Finally, we also assess the cross-modal querying performance of the proposed model as well as the influence of full and partial training on the results. For the sake of reproducibility, our code is published.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Environmental Sound Classification ESC-50 AudioCLIP Accuracy 97.15 # 1
Zero-Shot Environment Sound Classification ESC-50 AudioCLIP (partial training) Accuracy 69.40 # 4
Environmental Sound Classification UrbanSound8K AudioCLIP Accuracy 90.07 # 5

Methods