Dynamic Neural Audio Separation for Adaptive Hearing Devices
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Solution Overview
Problem
Existing audio signal processing technologies struggle to efficiently and adaptively separate and process multiple audio signals in complex environments while minimizing computational complexity and power consumption.
Innovation Solution
A signal processing device utilizing a neural network that can dynamically adjust the number of audio signals separated, allowing for adaptive customization based on the environment, with a sequential arrangement of neural networks for efficient and precise separation and modulation of audio signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed number of audio signals are separated using neural networks, then the processing structure is simple and stable, but the system cannot adapt to different environmental complexities and loses relevant information in complex situations
Solution Approach 1:
The patent implements dynamic adaptability by allowing the neural network to vary the number of separated audio signals based on environmental complexity. In simple environments, fewer signals are separated to reduce computational load, while in complex environments with multiple sound sources, the network separates more signals to capture all relevant information. This dynamic adjustment resolves the contradiction between adaptability and structural complexity.
Solution Approach 2:
The system changes the parameter of the number of separated audio signals dynamically. The neural network adjusts this parameter based on the complexity of the acoustic environment, enabling the system to optimize performance for different scenarios without requiring a completely different processing structure for each case.
2Adaptability or versatility
If a variable number of audio signals are separated to handle complex environments, then adaptability improves, but computational complexity and power consumption increase
Solution Approach 1:
The system dynamically adjusts the number of separated audio signals based on environmental complexity. In simple environments, fewer signals are separated to minimize power consumption, while in complex environments, the network separates more signals only when necessary. This dynamic approach ensures adaptability while optimizing energy usage by avoiding unnecessary processing in simple scenarios.
Solution Approach 2:
The parameter of the number of separated signals is changed dynamically according to environmental conditions. This allows the system to consume less power in simple environments by separating fewer signals, while maintaining the capability to handle complex environments by increasing the number of separated signals only when needed.
3Measurement precision
If multiple neural networks are used to separate audio signals, then separation accuracy improves, but device complexity and power consumption increase
Solution Approach 1:
The system dynamically determines the number of neural networks to deploy based on environmental complexity and the number of audio sources detected. In simple environments, fewer neural networks are used to reduce complexity, while in complex environments with multiple overlapping sound sources, more neural networks are activated to achieve accurate separation. This dynamic approach balances accuracy requirements with system complexity.
Data Source
AI summary
A hearing device for processing audio signals is described. The hearing device has an input interface for receiving an input signal, a neural network for separating one or more audio signals from the input signal, and an output interface for outputting an output signal, wherein the output signal comprises the one or more audio signals separated from the input signal. The hearing device is configured to determine a priority parameter for each of the one or more audio signals, modulate each audio signal in the one or more audio signals based on the re-spective priority parameter, combine the one or more audio signals to produce an output signal, and output the output signal via the output interface.