Audio Noise Suppression via Spatial Gain Combination
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing noise suppression methods in audio communication devices are limited in effectively distinguishing and reducing both stationary and non-stationary background noise, especially in multi-microphone scenarios, as they lack spatial sensitivity and often fail to account for varying sound field characteristics.
Innovation Solution
A method that combines multiple spatial sound field features such as sound source proximity, coherence, and directionality to compute intermediate noise suppression gains, which are then combined to achieve a total noise suppression gain, applied to audio signals to enhance noise reduction, utilizing techniques like FFT for frequency domain processing and beamforming to improve noise separation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If beamforming is applied to process audio signals from multiple microphones, then spatial noise suppression capability is improved, but device complexity increases due to the need for multiple microphones and complex spatial processing algorithms
Solution Approach 1:
The noise suppression process is segmented into distinct stages: beamforming stage for spatial filtering, spectral subtraction stage for stationary noise removal, and transient noise suppression stage for non-stationary noise handling. Each stage processes specific aspects of the audio signal independently, allowing complex noise suppression to be achieved through manageable modular components rather than a single monolithic system
Solution Approach 2:
The system dynamically adapts its processing strategy based on the characteristics of the noise and speech signals. The transient noise suppression gain is computed adaptively using spectral subtraction techniques that respond to changing noise conditions, allowing the system to adjust between aggressive noise suppression and speech preservation based on real-time signal analysis
2Object-affected harmful factors
If aggressive noise suppression is applied to eliminate background noise, then noise reduction effectiveness is improved, but speech quality deteriorates due to potential distortion of the desired speech signal
Solution Approach 1:
Different noise suppression strategies are applied to different portions of the audio signal based on local characteristics. Stationary noise components are suppressed using spectral subtraction, while transient noise components are handled with adaptive gain control. The beamforming stage applies spatial filtering selectively to signals from specific directions, preserving speech from the target direction while suppressing noise from other directions
Solution Approach 2:
The noise suppression gain is dynamically adjusted based on the estimated noise level and speech activity. The system computes transient noise suppression gains adaptively, increasing suppression during high-noise periods and reducing suppression during speech-dominated periods, thereby maintaining speech quality while maximizing noise reduction effectiveness
3Measurement precision
If multiple spatial sound field features are extracted and combined for noise suppression, then noise suppression accuracy is improved, but computational complexity increases due to additional processing steps
Solution Approach 1:
Multiple spatial features (inter-microphone time delay, inter-microphone level difference, signal coherence) are merged into a unified noise suppression decision. The beamforming stage combines spatial information from multiple microphones to create directionally selective filtering, while the spectral subtraction stage integrates these spatial features with spectral analysis to compute noise suppression gains that leverage all available information
Solution Approach 2:
The feature extraction and processing is segmented into specialized modules: spatial feature extraction module for computing inter-microphone characteristics, spectral analysis module for frequency-domain processing, and gain computation module for determining suppression levels. This segmentation allows each module to focus on specific computational tasks, improving efficiency despite the overall complexity
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A method and a system of noise suppressing an audio signal comprising a combination of at least two audio system input signals each having a sound source signal portion and a background noise portion, the method and system comprising steps and means of: Extracting at least two different types of spatial sound field features from the input signals such as discriminative speech and/or background noise features, computing a first intermediate spatial noise suppression gain on the basis of the extracted spatial sound field features, computing a second intermediate stationary noise suppression gain, combining the two intermediate noise suppression gains to form a total noise suppression gain,wherein the two intermediate noise suppression gains are combined by comparing their values and dependent on their ratio or relative difference, determining the total noise suppression gain, applying the total noise suppression gain to the audio signal to generate a noise suppressed audio system output signal.