Neural Beamformer Spatial Filtering for Reverberant Speech
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Solution Overview
Problem
Existing neural beamformer technologies struggle to accurately extract speech signals from arbitrary directions in reverberant environments, as they primarily focus on direct paths and lack explicit methods for spatial filtering considering early reflections.
Innovation Solution
A supervised learning method for spatial filtering of speech using a neural network-based beamformer model, which receives multi-channel speech signals and a beam condition representing the direction of interest. The model is trained to extract speech signals with specified azimuth and elevation angles, considering both direct paths and the directivity of early reflections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural beamformers are trained to improve recognition performance using neural network-based acoustic models, then recognition performance is improved, but speech signal quality deteriorates
Solution Approach 1:
The patent separates the optimization objectives into distinct components: spatial filtering for speech enhancement and recognition processing. The beamformer is designed to explicitly optimize speech signal quality through spatial filtering, while the recognition system processes the enhanced signal separately, allowing independent optimization of each component without compromising the other
Solution Approach 2:
Instead of optimizing beamformers for recognition performance as done in previous work, this patent inverts the approach by optimizing for speech signal quality through explicit spatial filtering. The beamformer prioritizes speech enhancement metrics, and recognition performance is then improved as a consequence of the enhanced input quality rather than being the direct optimization target
2Measurement precision
If direction-of-arrival (DOA) information is used to exploit directional features for time-frequency mask estimation, then speech separation performance is improved, but system complexity and accuracy requirements increase
Solution Approach 1:
The patent extracts and removes the dependency on DOA information from the beamforming process. By using data-driven spatial filtering that learns spatial characteristics directly from training data, the system eliminates the need for separate DOA estimation modules and the complexity associated with accurate directional information, while maintaining effective speech separation through learned spatial patterns
Solution Approach 2:
Instead of using explicit DOA information, the patent uses neural networks to learn and copy spatial filtering patterns directly from training data. The model learns to replicate effective spatial filtering behavior observed in training scenarios, achieving speech separation performance without requiring explicit directional information or complex DOA estimation mechanisms
3Adaptability or versatility
If time delay for azimuth steering is applied to steer toward any direction, then beamforming flexibility is improved, but sampling rate requirements and alignment accuracy increase
Solution Approach 1:
The patent replaces the mechanical time-delay-based beam steering mechanism with a data-driven neural network approach. Instead of applying precise time delays that require high sampling rates and accurate alignment, the system uses neural networks to learn and apply spatial filtering patterns directly in the time domain, achieving beamforming flexibility without the stringent sampling and alignment requirements
Solution Approach 2:
The patent changes the fundamental parameter approach from explicit time delay control to learned spatial filtering parameters. The neural network learns optimal filtering parameters directly from data, replacing the need for precise time delay calculations and high sampling rates, thereby achieving flexibility with relaxed hardware requirements
4Adaptability or versatility
If target signal is set as reverberant signal including early reflections, then spatial filtering in reverberant environments is addressed, but directivity determination becomes ambiguous
Solution Approach 1:
The patent applies preliminary spatial filtering to separate direct path signals from reverberant components before processing. By pre-processing the mixed signal to extract direct path components, the system establishes clear directivity references early in the processing chain, making subsequent directivity determination unambiguous even in highly reverberant environments
Solution Approach 2:
The patent introduces spatial filtering as an intermediary process between the reverberant mixture and the speech extraction stage. This intermediary step separates direct and reverberant components, providing clean direct path signals that serve as reliable mediators for determining true directivity, thereby resolving the ambiguity caused by mixing direct and reflected paths
Data Source
AI summary
A supervised learning method and system for explicit spatial filtering of speech are disclosed. According to an embodiment, the supervised learning method for spatial filtering of speech, performed by a beamformer learning system, includes: receiving, as input into a neural network-based beamformer model, a multi-channel speech signal incident on a microarray in a reverberant environment and a beam condition representing the direction of interest (DOI); and outputting a desired signal corresponding to the beam condition from the multi-channel speech signal by using the neural network-based beamformer model, wherein the neural network-based beamformer model is trained to extract a speech signal with azimuth and elevation angles that are set for the beam condition, by using training data.