Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation (2211.06687v4)

Abstract: Contrastive learning has shown remarkable success in the field of multimodal representation learning. In this paper, we propose a pipeline of contrastive language-audio pretraining to develop an audio representation by combining audio data with natural language descriptions. To accomplish this target, we first release LAION-Audio-630K, a large collection of 633,526 audio-text pairs from different data sources. Second, we construct a contrastive language-audio pretraining model by considering different audio encoders and text encoders. We incorporate the feature fusion mechanism and keyword-to-caption augmentation into the model design to further enable the model to process audio inputs of variable lengths and enhance the performance. Third, we perform comprehensive experiments to evaluate our model across three tasks: text-to-audio retrieval, zero-shot audio classification, and supervised audio classification. The results demonstrate that our model achieves superior performance in text-to-audio retrieval task. In audio classification tasks, the model achieves state-of-the-art performance in the zero-shot setting and is able to obtain performance 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

The paper presents a significant contribution to the domain of multimodal representation learning by proposing a novel framework for contrastive language-audio pretraining. This research primarily seeks to enhance audio representations by integrating audio data with natural language descriptions. The methodology involves developing a large-scale dataset, LAION-Audio-630K, comprising 633,526 audio-text pairs, and constructing a contrastive learning model that leverages feature fusion and keyword-to-caption augmentation.

Methodology Overview

The authors delineate a comprehensive approach to train a contrastive language-audio model:

1. Dataset Creation: LAION-Audio-630K is introduced as the largest public audio-caption dataset. The dataset incorporates diverse audio samples paired with text descriptions collected from numerous online sources. This large-scale dataset facilitates robust training and evaluation.
2. Model Architecture: The proposed model architecture includes separate audio and text encoders projecting inputs into a shared latent space. The audio encoders experimented include PANN and HTSAT, while the text encoders explored are CLIP transformer, BERT, and RoBERTa. Feature fusion mechanisms and keyword-to-caption augmentation techniques are integrated to support variable-length audio handling and augment textual representations.
3. Training Paradigms: Contrastive learning is employed to align audio and text representations by maximizing the agreement between corresponding audio-text pairs while minimizing it for non-corresponding pairs. This is achieved through a combination of innovative approaches such as feature fusion to handle variations in audio length and augmentation strategies to enrich text data.

Results and Implications

The paper extensively evaluates the proposed framework across tasks such as text-to-audio retrieval, zero-shot audio classification, and supervised audio classification. Notable findings include:

• Superior Retrieval Performance: The proposed model achieved impressive performance on text-to-audio retrieval tasks, surpassing existing models on key metrics. Particularly, the use of large datasets like LAION-Audio-630K and AudioSet, alongside keyword-to-caption augmentation, significantly enhanced retrieval capabilities.
• Zero-shot Classification: A salient outcome was the state-of-the-art performance in zero-shot audio classification, demonstrating the model’s generalization ability to classify audio events without explicit training for those categories.
• Supervised Classification: While maintaining competitive performance in supervised settings, the model highlights its potential in practical applications requiring robust audio classification.

Future Directions and Speculations

The implications of this research are multifaceted, catering to both theoretical advancements and practical applications. The creation of such a comprehensive audio-text dataset provides a benchmark for future studies. Additionally, the success of feature fusion and text augmentation suggests new avenues for handling variability in multi-modal data.

Future research directions may include extending this pretraining framework to more complex audio tasks such as audio synthesis, separation, and more nuanced retrieval challenges. Additionally, exploring alternative architectures and encoder configurations could further optimize performance across a broader range of audio applications.

In summary, the paper presents a well-substantiated paper that enhances our understanding of contrastive language-audio pretraining. Through meticulous dataset curation and innovative methodological approaches, it sets a new standard in multimodal representation learning and opens up extensive opportunities for subsequent research and application in artificial intelligence.