BLAT: Bootstrapping Language-Audio Pre-training with AudioSet Tag-guided Synthetic Data

Core Concepts

The author proposes a novel approach, BLAT, for audio-text pre-training using AudioSet tag-guided synthetic data to eliminate noise from the visual modality. The model achieves state-of-the-art performance on various downstream tasks.

Abstract

BLAT introduces a method to generate high-quality audio-text data without relying on the visual modality, leading to improved performance in downstream tasks. The approach involves utilizing AudioSet tags for caption generation and contrastive learning for pre-training. Experimental results demonstrate the effectiveness of BLAT in achieving zero-shot classification performance and fine-tuning on real data.
The content discusses the challenges of audio-text pre-training due to limited parallel data and proposes a solution through synthetic data generation guided by AudioSet tags. The model's performance is evaluated across different tasks, showcasing its effectiveness in improving audio-related tasks.
Key points include:

Proposal of BLAT for audio-text pre-training using synthetic data generated from AudioSet tags.
Utilization of contrastive learning and tag-guided captioning for high-quality data curation.
Demonstration of BLAT's performance on various downstream tasks, highlighting its effectiveness in zero-shot classification and fine-tuning scenarios.

Stats

Compared with human annotations, synthetic captions show significant improvement in quality metrics like ROUGEL.
BLAT outperforms template-based text generation methods in zero-shot audio-text retrieval tasks.
BLAT feature exhibits superior performance compared to PANNs and COLA in audio captioning evaluations.
BLAT achieves state-of-the-art zero-shot classification results on various datasets, showcasing its effectiveness.
Fine-tuning BLAT leads to competitive results close to SOTA across different single-modality audio classification tasks.

Quotes

"Compared with previous methods, the data generation approach does not incorporate video to eliminate noise induced by the visual modality."
"Our model trained on synthetic data significantly outperforms VIP∼ANT except for text-to-audio retrieval on Clotho."
"BLAT serves as a powerful feature extractor even under linear probing settings."
"BLAT exhibits SOTA zero-shot performance with a moderate model size."

Key Insights Distilled From

BLAT

by Xuenan Xu,Zh... at arxiv.org 03-06-2024

https://arxiv.org/pdf/2303.07902.pdf

Deeper Inquiries

How can the proposed method be extended or adapted for other modalities beyond audio

The proposed method can be extended or adapted for other modalities beyond audio by incorporating additional data sources and modalities into the pre-training process. For example, to include visual information, one could explore using video clips with corresponding captions to create a multi-modal dataset for training. This would involve developing models that can effectively learn representations from both audio and visual inputs simultaneously. By integrating multiple modalities in the pre-training phase, the model can capture richer and more comprehensive features that encompass a wider range of sensory inputs.

What are potential limitations or drawbacks of eliminating noise from the visual modality in audio-text pre-training

Eliminating noise from the visual modality in audio-text pre-training may have some potential limitations or drawbacks. One drawback is that certain types of information present in visuals but not captured in audio may be lost during training. For instance, contextual cues provided by images or videos could enhance the understanding of certain concepts mentioned in text but not explicitly stated in audio clips alone. Additionally, relying solely on auditory input might limit the diversity and richness of features available for learning compared to leveraging multiple modalities simultaneously.

How might incorporating additional contextual information improve the quality of generated captions beyond AudioSet tags

Incorporating additional contextual information beyond AudioSet tags could further improve the quality of generated captions by providing more nuanced descriptions and enhancing semantic understanding. One way to achieve this is by including metadata such as location data, timestamps, or user-generated annotations associated with audio clips. These additional context cues can offer valuable insights into the content of an audio clip and help generate more accurate and detailed captions that capture specific details relevant to different contexts or scenarios depicted in the sound recordings.