Natural Language Supervision for General-Purpose Audio Representations (2309.05767v2)

Abstract: Audio-LLMs jointly learn multimodal text and audio representations that enable Zero-Shot inference. Models rely on the encoders to create powerful representations of the input and generalize to multiple tasks ranging from sounds, music, and speech. Although models have achieved remarkable performance, there is still a performance gap with task-specific models. In this paper, we propose a Contrastive Language-Audio Pretraining model that is pretrained with a diverse collection of 4.6M audio-text pairs employing two innovative encoders for Zero-Shot inference. To learn audio representations, we trained an audio encoder on 22 audio tasks, instead of the standard training of sound event classification. To learn language representations, we trained an autoregressive decoder-only model instead of the standard encoder-only models. Then, the audio and language representations are brought into a joint multimodal space using Contrastive Learning. We used our encoders to improve the downstream performance by a margin. We extensively evaluated the generalization of our representations on 26 downstream tasks, the largest in the literature. Our model achieves state of the art results in several tasks leading the way towards general-purpose audio representations.

Analysis of "Natural Language Supervision for General-Purpose Audio Representations"

The paper presents a paper focused on bridging the gap between task-specific and general-purpose audio models using Contrastive Language-Audio Pretraining (CLAP). By training with an extensive dataset of 4.6 million diverse audio-text pairs, this research advances the field of audio representations through innovative pretraining strategies, achieving new state-of-the-art results in a diverse set of tasks.

Methodology

The authors introduce CLAP, a model leveraging contrastive learning to jointly encode audio and text data. The training process involves two main components: an advanced audio encoder termed HTSAT-22, and a modified autoregressive text encoder based on GPT2.

• Audio Encoder: HTSAT-22 is pretrained on 22 audio tasks which enhances its ability to generalize across tasks compared to traditional focus on sound event classification alone.
• Text Encoder: A modification to GPT2 is detailed, wherein the model is adapted to produce sentence-level representations by introducing a special end-of-text token, better aligning its sequential processing capacity to CLAP's needs.

The audio and text representations are merged into a joint multimodal space using a projection layer, nurturing the model's zero-shot capabilities.

Results

The model was tested on 26 downstream tasks, representing the most extensive evaluation of this kind in the literature. The authors achieved state-of-the-art (SoTA) results across various domains, including music, speech emotion, and surveillance sound classification, outperforming existing models in the process. Notable results include:

• Music Genres: A significant improvement of 58.4% accuracy over prior benchmarks.
• Vocal Sound Classification: An 80% accuracy, substantially higher than former results.

On specific tasks like Audio Text Retrieval, the model displayed promising results, although some challenges remain, particularly in AudioCaps retrieval tasks, indicative of the sensitivity to distribution shifts in training datasets.

Implications and Future Directions

This paper emphasizes the utility of scaling up audio-text pair diversity for zero-shot models and demonstrates the potential of general-purpose audio representations. The performance improvements highlight the importance of leveraging multiple training sources and tasks to gain generalized models capable of excelling over a wide task array.

Future research might explore further advancements in encoder architectures or test the impact of even more extensive and varied datasets. Additionally, performance optimization strategies for specific retrieval tasks could help mitigate the marginal decline observed in certain evaluations.

Conclusion

Overall, this paper provides compelling evidence for the efficacy of comprehensive multimodal pretraining in audio models. By setting new benchmarks across numerous tasks, the authors illustrate a clear path forward for general-purpose audio representation learning in both theoretical exploration and practical applications.