Noise-Aware Audio Visual Speech Denoising
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Solution Overview
Problem
Existing speech denoising methods fail to effectively utilize video modalities, particularly the lip region, to extract clean speech signals from noisy recordings, leading to suboptimal performance in real-world scenarios with environmental noise.
Innovation Solution
A noise-aware audio visual speech denoising system that models noise using both audio and visual data, estimates noise strength, and fuses refined audio and visual features to generate a de-noised signal, employing a softmax operation and decoder to improve speech clarity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a naive combination of audio and visual modalities is used by concatenating features in the bottleneck layer, then the system structure is simple, but the speech denoising performance is suboptimal
Solution Approach 1:
The system segments the feature fusion process into distinct stages: early fusion of audio and visual features, followed by separate processing of audio features through denoising modules. This segmentation allows each component to optimize its function while maintaining overall system manageability, resolving the contradiction between performance and complexity.
Solution Approach 2:
The system introduces an intermediary noise strength estimation module that mediates between audio and visual features. This intermediary component dynamically adjusts the fusion based on noise conditions, enabling the system to achieve high denoising performance without requiring overly complex structural modifications.
2Reliability
If only audio modality is used for speech denoising, then the system is simpler to implement, but the ability to handle environmental noise is insufficient
Solution Approach 1:
The system merges audio and visual modalities by concatenating their feature representations in the bottleneck layer, creating a unified feature space that leverages complementary information from both sources. This merging enables the system to achieve superior noise handling capability while maintaining a relatively clean and manageable architecture.
Solution Approach 2:
The system designs a universal feature fusion mechanism that can handle multiple noise types and conditions through a single integrated architecture. The noise strength estimation module provides multi-functionality by adapting the fusion strategy based on varying noise environments, eliminating the need for separate specialized modules for different noise conditions.
Data Source
AI summary
The present subject matter provides a method for de-noising an audio visual speech. The method includes modeling a noise in the audio visual speech using a noisy speech from audio data associated with the audio visual speech to generate a reconstructed noise signal. The method includes estimating the reconstructed noise signal in the audio visual speech using an audio signal and a plurality of visual frames. The method includes partitioning the reconstructed noise signal into a plurality of windows and calculate an energy associated with each window. The method includes estimating a noise strength in each window by performing a soft max operation to obtain one or more refined audio features. The method includes fusing the one or more refined audio features and one or more visual features using the noise strength to generate an output that is passed through a decoder to obtain a de-noised audio visual speech.