Adaptive Beamformer Noise Reduction for Speech Recognition
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing multi-channel speech signal processing systems in noisy environments suffer from poor signal-to-noise ratio (SNR) due to reliance on fixed beamformers, which fail to consistently reduce noise effectively, especially when SNR varies among microphone signals, leading to reduced speech recognition accuracy and intelligibility.
Innovation Solution
A signal processing system that uses multiple microphone inputs, time delay compensation, adaptive beamforming with real-valued weights, and noise reference logic to generate noise estimates, which are then subtracted from the beamformed output, along with adaptive self-calibration and adaptation control logic to optimize noise reduction based on SNR and signal characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a fixed beamformer is used to synchronize microphone signals, then signal synchronization is achieved, but noise reduction performance deteriorates when SNR varies among microphone signals
Solution Approach 1:
The patent replaces the fixed beamformer with an adaptive beamformer that dynamically adjusts its weights based on the actual SNR conditions of each microphone signal. The adaptive algorithm continuously optimizes the beamforming parameters to maximize speech enhancement while minimizing noise, allowing the system to adapt to varying environmental conditions and different microphone signal qualities.
Solution Approach 2:
The system changes the beamformer weights from fixed values to adaptive values that are continuously adjusted based on signal characteristics. The adaptive beamformer calculates optimal weights by analyzing the correlation between microphone signals and the reference speech signal, thereby optimizing noise reduction performance for each specific operating condition.
2Productivity
If microphone signals with low SNR are included in beamforming, then more microphones contribute to the output, but excessive noise is introduced into the beamformed output signal
Solution Approach 1:
The adaptive beamformer applies different weights to different microphone signals based on their individual quality and SNR characteristics. Microphones with higher SNR receive greater weights, while those with lower SNR receive smaller weights, thereby optimizing the contribution of each microphone to the final output based on its local quality.
Solution Approach 2:
The system uses feedback from the actual signal and noise characteristics to continuously adjust the beamformer weights. The adaptive algorithm monitors the SNR of each microphone signal and modifies the weights accordingly, creating a closed-loop system that optimizes noise reduction while maintaining speech quality.
3Reliability
If adaptive beamforming with recalculated weights is implemented, then noise reduction performance improves, but system complexity increases
Solution Approach 1:
The adaptive beamformer is self-adjusting and does not require manual configuration or external control. The system automatically calculates and updates its own weights based on the input signals, making the complexity management self-contained while achieving superior noise reduction performance.
Data Source
AI summary
An adaptive signal processing system eliminates noise from input signals while retaining desired signal content, such as speech. The resulting low noise output signal delivers improved clarity and intelligibility. The low noise output signal also improves the performance of subsequent signal processing systems, including speech recognition systems. An adaptive beamformer in the signal processing system consistently updates beamforming signal weights in response to changing microphone signal conditions. The adaptive weights emphasize the contribution of high energy microphone signals to the beamformed output signal. In addition, adaptive noise cancellation logic removes residual noise from the beamformed output signal based on a noise estimate derived from the microphone input signals.