Neural-Kalman Acoustic Howling Suppression for Nonlinear Audio Feedback
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Solution Overview
Problem
Existing acoustic howling suppression (AHS) methods, including adaptive feedback control and deep-learning-based approaches, face challenges in effectively managing nonlinearity introduced by amplifiers and loudspeakers, and suffer from sensitivity to control parameters and discrepancies between offline training and real-time streaming inference.
Innovation Solution
The proposed solution involves an augmented Kalman filter that refines its parameters using neural networks to improve acoustic howling suppression. This method incorporates neural networks to estimate reference signals and covariance matrices within the Kalman filter framework, enhancing its performance in real-time acoustic howling suppression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If adaptive feedback control methods are used for acoustic howling suppression, then real-time adaptation breaks the positive feedback loop, but the methods show disadvantageous sensitivity to control parameters and cannot effectively manage nonlinearity introduced by amplifiers and loudspeakers
Solution Approach 1:
The patent introduces neural networks as intermediary components that bridge the linear Kalman filter and the nonlinear acoustic system. The neural networks process the audio signal and generate nonlinear transformations that are fed into the Kalman filter, enabling the system to handle nonlinearities introduced by amplifiers and loudspeakers while maintaining the stability benefits of Kalman filtering.
Solution Approach 2:
The patent creates a composite AHS system that combines two different approaches: traditional Kalman filtering (which provides stability and real-time adaptation) and neural networks (which provide nonlinear processing capability). This hybrid architecture leverages the strengths of both methods to achieve robust howling suppression that handles both linear and nonlinear characteristics of acoustic systems.
2Adaptability or versatility
If deep-learning-based approaches are used for acoustic howling suppression, then the model can capture nonlinear patterns, but the discrepancy between offline training data generation and real-time streaming inference with continuous AHS processing integration affects performance
Solution Approach 1:
The patent segments the AHS system into distinct functional components: neural network modules for nonlinear processing and a Kalman filter for stable state estimation. This segmentation allows each component to be optimized independently and reduces the coupling between training and inference processes, thereby minimizing the discrepancy between offline training and real-time streaming inference.
Solution Approach 2:
The patent implements feedback mechanisms where the Kalman filter continuously monitors the system state and adjusts its estimates based on the difference between predicted and actual observations. This feedback loop ensures that the system adapts to changing acoustic conditions in real-time while maintaining consistency between training and inference by using the same processing pipeline for both.
3Productivity
If traditional Kalman filter is used for acoustic howling suppression, then real-time processing is efficient, but it cannot effectively handle the nonlinearity introduced by amplifiers and loudspeakers
Solution Approach 1:
The patent replaces the traditional purely mathematical Kalman filter with a neural network-augmented version. The neural networks substitute for the linear assumptions of the traditional Kalman filter by learning and modeling the nonlinear characteristics of amplifiers and loudspeakers, while the core Kalman filtering mechanism maintains real-time processing efficiency.
Data Source
AI summary
A method performed by at least one processor of an acoustic howling suppression (AHS) system includes receiving, from an input source device, an audio signal. The method includes refining one or more parameters of a Kalman filter based on one or more neural networks. The method includes filtering the audio signal using the Kalman filter with the one or more refined parameters of the Kalman filter to reduce acoustic howling included in the audio signal.