AudioCLIP: Extending CLIP to Image, Text and Audio (2106.13043v1)

Abstract: 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.

• The paper introduces a tri-modal architecture integrating audio into the CLIP framework, expanding its capabilities to process text, images, and audio.
• It employs the ESResNeXt audio model with the AudioSet dataset, achieving state-of-the-art accuracies of 90.07% on UrbanSound8K and 97.15% on ESC-50.
• Comprehensive model training enhances cross-modal querying and zero-shot performance, opening avenues for diverse real-world applications.

An Analysis of AudioCLIP: A Multimodal Extension to CLIP Incorporating Audio

The paper "AudioCLIP: Extending CLIP to Image, Text and Audio" explores an extension of the Contrastive Language-Image Pretraining (CLIP) model by incorporating audio as an additional modality. This endeavor brings into focus a tri-modal architecture capable of handling images, text, and audio simultaneously, thus advancing the model's versatility and potential applications.

Core Contributions

AudioCLIP integrates the ESResNeXt audio model into the existing text-image CLIP framework, utilizing the AudioSet dataset to facilitate training. This tri-modal configuration not only allows the model to classify across singular and combined modalities (unimodal and bimodal classification) but also maintains CLIP's robust generalization capabilities inherent in its zero-shot learning framework. By doing so, the authors address a notable gap in the field—the underrepresentation of audio in contrastive learning and classification tasks alongside text and image.

Performance and Results

The empirical results indicate that AudioCLIP achieves remarkable state-of-the-art accuracy in environmental sound classification tasks. It records accuracies of 90.07% on the UrbanSound8K dataset and 97.15% on the ESC-50 dataset, significantly outperforming contemporary methods. Furthermore, the model establishes new baselines in zero-shot environmental sound classification with scores of 68.78% and 69.40% on UrbanSound8K and ESC-50, respectively.

Evaluation of Training Strategies

The investigation involved both partial and full training regimens. Initially, the audio-head, using ESResNeXt, was trained independently before integrating with the text-image model for a holistic training phase. The findings suggest that complete model training yields superior results compared to partial training, thereby emphasizing the utility of integrated tri-modal training in achieving higher classification accuracy and enhanced cross-modal querying.

Theoretical and Practical Implications

The augmentation of CLIP with an audio component opens new avenues in multimodal learning, underscoring the efficacy of employing several modalities within a singular machine learning framework. The triangulation of text, image, and audio offers broader querying capabilities across modalities, a critical functionality in complex real-world applications where multi-sensory data is prevalent.

Practically, AudioCLIP can be envisioned in scenarios ranging from autonomous systems understanding and interacting with their environment to enhanced audio-visual data retrieval systems. The research also posits potential for exploring more ambitious datasets and tasks that could harness the power of such a multi-modal architecture.

Future Directions

Looking ahead, further enhancements can stem from incorporating more sophisticated model backbones for image and audio processing. Additionally, expanding the model's evaluation to include a wider spectrum of datasets could substantiate its robustness across diverse applications. Another promising direction includes optimizing the balance of performance across learning multiple modalities concurrently, which may lead to more efficient models suitable for deployment at scale.

AudioCLIP represents a significant step towards a comprehensive multimodal learning paradigm, providing a foundational architecture that elegantly bridges the gap between textual, visual, and auditory data domains. As the trajectory in AI continues leaning towards richer and more varied data input, such contributions are pivotal in framing future AI capabilities.