Joint Beamforming and Echo Cancellation via MVDR Whitening
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing speech processing technologies face challenges in distant talker scenarios due to low signal-to-noise ratio (SNR) and signal-to-echo ratio (SER), which complicate beamforming and echo cancellation, increasing computational complexity and latency.
Innovation Solution
A joint beamforming and echo cancellation system using recursive least squares (RLS) based inverse QR decomposition for efficient echo path estimation and weighted Minimum Variance Distortionless Response (MVDR) beamforming, which reduces noise and echo by estimating transfer functions and whitening signals, thereby improving SNR and SER.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If beamforming is performed followed by echo cancellation, then echo reduction is achieved, but beamformer design becomes greatly complicated and computationally expensive
Solution Approach 1:
The patent combines echo cancellation and beamforming into a single joint processing framework. The echo canceller and beamformer share common computational structures (QR decomposition, gain vectors), allowing simultaneous echo suppression and spatial filtering without the complexity of separate cascaded processing. This merging resolves the contradiction by achieving both echo reduction and noise reduction through a unified algorithmic approach.
2Object-affected harmful factors
If echo cancellation is performed followed by beamforming, then noise reduction is achieved, but multi-channel echo cancellation increases complexity
Solution Approach 1:
The joint processing framework integrates multi-channel echo cancellation with beamforming operations, sharing computational resources and algorithms. The RLS-based update mechanism and QR decomposition are applied jointly across all channels, eliminating the need for separate multi-channel echo cancellation processing and reducing overall system complexity while maintaining noise reduction performance.
3Measurement precision
If separate echo cancellation and beamforming processing is used, then processing accuracy is maintained, but latency increases
Solution Approach 1:
The joint processing framework performs echo cancellation and beamforming in a continuous, unified operation rather than as separate discrete steps. The RLS algorithm continuously updates gain vectors and QR decompositions for all channels simultaneously, maintaining processing accuracy while eliminating the temporal gaps and redundant computations inherent in sequential processing, thereby reducing latency.
Data Source
AI summary
Techniques are provided for reduction of noise and nonlinear-echo. A methodology implementing the techniques according to an embodiment includes estimating transfer functions (TFs) of echo paths of audio signals received through a microphone array. The audio signals include speech signal, additive noise, and echo, the TF estimation based on the reference signal. The method also includes cancellation of linear components of the echo, based on the echo path TFs. The method further includes estimating an inverse square root of a covariance matrix of the additive noise, whitening the echo cancelled signals, and estimating a speech path RTF associated with the speech signal, based on the whitened echo cancelled signals. The method further includes performing beamforming on the whitened signals (such as weighted MVDR beamforming), based on the echo path TFs, the speech path RTF, and the estimated inverse square root additive noise covariance matrix.