GAN Audio Interference Reduction and Frequency Band Compensation
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Solution Overview
Problem
Videoconferencing systems face challenges in delivering high-quality audio due to interference from noise and reverberation, as well as distortions caused by varying audio recording devices, which existing methods struggle to address effectively across different devices.
Innovation Solution
The implementation of an interference reduction and frequency band compensation (IRFBC) model, specifically a generative adversarial network (GAN) model, that simultaneously removes noise and distortions from captured audio signals, allowing for enhanced audio quality transmission across various audio recording devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional noise reduction and reverberation removal methods are used separately, then each can achieve its specific function, but the overall audio quality improvement is limited and the processing complexity increases
Solution Approach 1:
The patent combines noise reduction and reverberation removal into a single integrated deep learning model that processes audio signals simultaneously for both functions. This unified approach eliminates the need for separate processing stages, reducing overall system complexity while maintaining or improving audio quality through joint optimization of both noise and reverberation removal.
Solution Approach 2:
The deep learning model is designed to perform multiple audio processing functions (noise reduction and reverberation removal) within a single universal framework. This multi-functional model can handle various audio degradation types without requiring separate specialized processors, thereby reducing device complexity while maintaining high reliability across different audio scenarios.
2Reliability
If device-specific audio processing is implemented, then audio quality for that specific device can be optimized, but the solution cannot be effectively applied to other audio recording devices
Solution Approach 1:
The deep learning model is trained on diverse audio data from multiple devices and scenarios, creating a universal processor that adapts to various recording characteristics. This universal model maintains high audio quality across different device types without requiring device-specific customization, thereby achieving both reliability and adaptability simultaneously.
Solution Approach 2:
The model uses learnable parameters that automatically adapt to different audio characteristics from various devices during processing. By dynamically adjusting internal parameters based on the input audio properties rather than being fixed to device-specific settings, the system maintains high audio quality across diverse recording devices without requiring device-specific optimization.
3Reliability
If complex deep learning models are used for interference reduction, then audio quality improves, but computational complexity and memory requirements increase
Solution Approach 1:
The patent extracts and focuses on the most critical features for noise and reverberation removal within the deep learning model, eliminating unnecessary processing components. This feature extraction approach maintains high audio quality by concentrating computational resources on the most impactful processing operations, thereby reducing overall computational complexity and memory requirements.
Solution Approach 2:
The model applies deep learning processing selectively to the most problematic frequency ranges and time segments where noise and reverberation are most prominent, rather than uniformly processing the entire audio signal. This partial action approach maintains audio quality in critical regions while reducing computational burden in less problematic areas, optimizing the trade-off between quality and resource usage.
Data Source
AI summary
One disclosed example method includes a device receiving an audio signal recorded in a physical environment and applying a machine learning model onto the audio signal to generate an enhanced audio signal. The machine learning model is configured to simultaneously remove interference and distortion from the audio signal and is trained via a training process. The training process includes generating a training dataset by generating a clean audio signal and generating a noisy distorted audio signal based on the clean audio signal that includes both an interference and a distortion. The training further includes constructing the machine learning model as a generative adversarial network (GAN) model that includes a generator model and multiple discriminator models, and training the machine learning model using the training dataset to minimize a loss function defined based on the clean audio signal and the noisy distorted audio signal.