Multimodal Continual Pretraining for Generalizable Audio Encoders
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Solution Overview
Problem
Existing audio encoders are typically trained with single pretext tasks, limiting their ability to generalize to various downstream tasks due to a lack of data from different modalities with human annotations, and current continual learning methods are resource-intensive and result in poor generalization.
Innovation Solution
A multi-modal continual pre-training method for audio encoders using a sequence of pretext tasks, including supervised learning, self-supervised learning on image-audio and text-audio pairs, with knowledge distillation as regularization, to improve generalization across diverse downstream tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If audio encoders are trained with single pretext tasks, then training simplicity is maintained, but generalization ability to downstream tasks deteriorates
Solution Approach 1:
The patent combines multiple pretext tasks (supervised learning on audio labels, self-supervised learning on image-audio pairs, and self-supervised learning on text-audio pairs) into a unified training framework. This merging of diverse tasks allows the audio encoder to learn from multiple modalities and task types simultaneously, improving generalization ability while maintaining a structured training process through knowledge distillation.
2Adaptability or versatility
If multi-modal data with human annotations is used for pre-training, then generalization ability improves, but data availability and annotation cost worsen
Solution Approach 1:
The patent employs self-supervised learning mechanisms where the model generates its own training signals from unannotated multi-modal data. Specifically, it uses contrastive learning on image-audio pairs and text-audio pairs without requiring manual annotations, allowing the system to leverage large amounts of readily available unannotated data from the internet while maintaining generalization ability.
3Adaptability or versatility
If continual learning methods are applied to audio encoders, then adaptability to multiple tasks improves, but computational resources and training time worsen
Solution Approach 1:
The patent performs preliminary pre-training of the audio encoder using self-supervised learning on large-scale unannotated data before fine-tuning on specific downstream tasks. This preliminary action allows the model to acquire general audio representations efficiently, reducing the computational resources needed during subsequent continual learning phases while maintaining adaptability to multiple tasks.
4Adaptability or versatility
If audio encoders are trained with diverse pretext tasks, then generalization improves, but training complexity worsens
Solution Approach 1:
The patent introduces knowledge distillation as an intermediary mechanism to simplify the training of diverse pretext tasks. A teacher model pre-trained on multiple tasks provides softened labels and guidance to the student model, making the training process of complex multi-task learning more manageable while preserving generalization ability across different downstream applications.
Data Source
AI summary
A method for training an audio encoder includes receiving first training data comprising first audio data, performing a first training task on an audio encoder using the first training data, receiving second training data comprising first image data and second audio data, and performing a second training task on the audio encoder using the second training data. The method also includes receiving third training data comprising first text data and third audio data, performing a third training task on the audio encoder using the third training data, and performing at least one downstream task using the audio encoder.