Audio-Visual Source Separation With Cross-Modal Test-Time Adaptation
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Solution Overview
Problem
Existing audio visual sound source separation methods struggle with generalization in real-world scenarios due to distribution gaps between training and test data, requiring large amounts of training data and failing to adapt effectively to unknown musical instruments and other sound sources.
Innovation Solution
A cross-modal meta consistency learning approach that utilizes a model with a visual guidance network and audio source separation network, combined with a consistency network for self-supervised adaptation, enabling fast test-time adaptation to unseen video samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep learning based audio visual sound source separation methods are used, then separation accuracy for known sources is improved, but generalization ability to unknown sources deteriorates due to distribution gaps between training and test data
Solution Approach 1:
The method performs preliminary actions by pre-training the model on known sound sources and then conducting test-time adaptation on unseen sources. The visual guidance network is pre-trained to extract features from known instruments, and during inference, the system adapts to unknown sources by leveraging the pre-learned visual-audio associations without requiring retraining on the full dataset.
Solution Approach 2:
The system employs self-supervised learning where the model uses its own predictions and visual information to adapt to unknown sound sources during test time. The consistency regularization mechanism allows the model to self-correct and adapt to distribution shifts by comparing visual and audio modalities, enabling autonomous adaptation without external supervision or labeled data from unknown sources.
2Adaptability or versatility
If large amounts of training data are used to improve generalization, then adaptability to unknown sources is improved, but training time and computational resources increase
Solution Approach 1:
The method performs preliminary actions by pre-training the model on known sound sources and then conducting test-time adaptation on unseen sources. The visual guidance network is pre-trained to extract features from known instruments, and during inference, the system adapts to unknown sources by leveraging the pre-learned visual-audio associations without requiring retraining on the full dataset.
Solution Approach 2:
The system employs self-supervised learning where the model uses its own predictions and visual information to adapt to unknown sound sources during test time. The consistency regularization mechanism allows the model to self-correct and adapt to distribution shifts by comparing visual and audio modalities, enabling autonomous adaptation without external supervision or labeled data from unknown sources.
3Device complexity
If audio only separation methods are used, then device complexity is reduced, but separation performance deteriorates due to lack of visual guidance
Solution Approach 1:
The method merges audio and visual processing streams by integrating a visual guidance network with the audio separation model. The visual encoder processes video frames to extract features from sound-producing objects, and these visual features are combined with audio features through cross-modal attention mechanisms, enabling the system to leverage both modalities for improved separation performance.
Solution Approach 2:
The visual guidance network serves as an intermediary that bridges visual and audio modalities. It extracts visual features from video input and uses them to guide the audio separation process by providing spatial and temporal context about sound sources, thereby improving separation accuracy without requiring direct fusion of raw audio and video signals.
Data Source
AI summary
System, method and/or computer readable medium for audio visual sound source Separation that applies cross-modal meta consistency learning. Inputs include an audio spectrogram that represents first sounds second sounds, a first video of a first sound producing object, and a second video of a second sound producing object. Audio features and audio tokens are generated by applying audio encoders to the audio spectrogram. Visual tokens are generated by applying a visual encoder to the first video and the second video. Respective audio-visual features are obtained based on combining the audio token with the respective visual tokens. Based on the respective audio-visual features, first and second separated audio masks are generated by applying a decoder.