Hearing Audio Noise Prediction for Low-Latency Cancellation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing hearing devices face challenges in providing satisfactory noise cancellation and speech enhancement, with a need for improved methods to effectively remove background noise and enhance speech.
Innovation Solution
A hearing device equipped with a processor that processes transducer inputs using a neural network to predict future noise, allowing for noise estimation and subtraction from the input signal, thereby enhancing sound quality and reducing latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If traditional noise cancellation methods are used, then noise removal is provided, but speech enhancement and sound quality are insufficient
Solution Approach 1:
The patent replaces traditional mechanical signal processing methods with a neural network-based system. The neural network processes transducer inputs to predict future noise, enabling more accurate noise cancellation while preserving speech quality. This substitution of processing methodology resolves the contradiction between noise removal effectiveness and speech enhancement quality.
Solution Approach 2:
The neural network predicts future noise before it fully impacts the audio signal. By anticipating noise patterns in advance and subtracting them proactively from the transducer input, the system achieves both effective noise cancellation and preserved speech quality, resolving the trade-off between these two features.
2Object-affected harmful factors
If complex noise cancellation processing is applied, then noise removal is improved, but processing latency increases
Solution Approach 1:
The system performs noise prediction in advance using the neural network, generating noise estimates before they need to be subtracted from the audio signal. This preliminary processing approach enables effective noise removal without introducing significant latency, as the computational work is done proactively rather than reactively.
Solution Approach 2:
The neural network creates a predictive copy of future noise based on current transducer inputs. Instead of waiting for noise to manifest and then processing it, the system generates a predictive model of the noise, allowing for low-latency subtraction while maintaining effective noise removal performance.
3Measurement precision
If neural network processing is applied, then noise prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent replaces complex traditional noise cancellation algorithms with a trained neural network model. While the neural network introduces computational complexity, it achieves superior noise prediction accuracy through pattern recognition capabilities that traditional methods lack. The system manages this complexity through efficient integration with the hearing device's existing processor.
Solution Approach 2:
The system transforms the noise cancellation problem from a direct signal processing task into a predictive modeling task using neural networks. By changing the approach parameters from traditional filter-based methods to machine learning-based prediction, the system achieves higher accuracy while managing computational requirements through the neural network's efficient architecture.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A hearing device, a hearing system, and related methods are disclosed, the hearing device comprising a set of input transducers providing a transducer input, the set of input transducers comprising a first input transducer for provision of a first transducer input signal, wherein the transducer input is based on the first transducer input signal; a processor configured to process the transducer input and provide an electrical output signal based on the transducer input; and a receiver for converting the electrical output signal to an audio output signal, wherein to process the transducer input comprises to process the transducer input for provision of a first input and a second input; apply a neural network to the second input for provision of a second output being a signal that is a prediction of future noise; provide a noise estimate based on the second output; and subtract the noise estimate from a first input magnitude of the first input for provision of a first output, wherein to provide the electrical output signal is based on the first output.