Audiovisual Signal Processing
Visual-Informed Speech Enhancement Using Attention-Based Beamforming
This paper introduces VI-NBFNet, a novel visual-informed neural beamforming network that integrates microphone array signal processing and deep neural networks with multimodal input features (lip movements) for robust speech enhancement. It leverages a pretrained visual speech recognition model for voice activity detection and target speaker identification, enabling effective handling of both static and moving speakers through a supervised end-to-end beamforming framework with an attention mechanism. Experimental results demonstrate superior speech enhancement performance and robustness compared to baselines.
Key Performance Indicators
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OVRL Score (Moving Speaker)
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Model Parameters
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WER Reduction
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Audiovisual Signal Processing
Improved Performance in Challenging Scenarios
2.177Overall Quality (OVRL) score for moving speakers with VI-NBFNet (Table II)
VI-NBFNet significantly outperforms all beamforming-based baselines in stationary and moving speaker scenarios, especially at low SIR levels, indicating satisfactory generalization and robustness in adverse acoustic conditions.
VI-NBFNet System Architecture
Audiovisual Encoder
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Mask Decoder
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Spatial-Aware Decoder
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SCM Computation
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MVDR Beamformer
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Enhanced Signal Output
The VI-NBFNet integrates MASP and DNNs, jointly learning audiovisual and spatial information for time-varying SCM estimation for time-varying SCM estimation in an end-to-end manner.
| Metric | VI-NBFNet | VI-SA-BF |
|---|---|---|
| Parameters (M) | 7.15 | 14.7 |
| MACs (G/s) | 0.4 | 0.6 |
Robustness Against Visual Degradation
8%Word Error Rate (WER) with Whisper-turbo under visually degraded conditions (Table VII)
VI-NBFNet achieves the lowest WER, showing strong resilience to partial occlusion, mosaic occlusion, and low-resolution inputs without significant performance loss.
Real-World Application and Performance
Scenario: Live-recorded speech enhancement in a conference room with an Apple® iPad Air 5.
Challenge: Uncontrollable environmental disturbances, dynamic speaker movement, and lower image resolution.
Solution: VI-NBFNet's end-to-end learning and attention mechanism.
Outcome: Achieved the lowest WER (8% with Whisper-turbo) and highest DNSMOS scores, confirming superior enhancement performance and robustness in realistic environments.
Key Takeaway: VI-NBFNet effectively suppresses non-target speech even with visual degradation, proving its robustness and generalizability for real-world applications.
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