Audio Neural Network Activation Tuning Without Retraining
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Solution Overview
Problem
Neural networks for audio processing face challenges in balancing computational cost with adaptability to various sound environments and user-specific needs, and adapting these networks for audio devices is often difficult or impossible without re-training.
Innovation Solution
A method that adapts the activation functions of a neural network's nodes based on user input, device characteristics, and audio data, while maintaining the network's topology, allowing efficient adaptation without re-training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the neural network is expanded to handle a large variety of sound environments and devices, then the neural network can handle all possible combinations, but the computational cost increases
Solution Approach 1:
The patent segments the neural network into a fixed topology portion and an adaptable activation function portion. The topology remains constant while activation functions can be selectively modified for different sound environments and devices, avoiding the need to expand the entire network structure.
Solution Approach 2:
The patent introduces dynamic adaptability through configurable activation functions that can be adjusted based on input characteristics, sound environment, and device properties. This allows the network to adapt its behavior without changing its structural complexity.
2Adaptability or versatility
If the neural network is designed to solve complex problems, then it can handle multiple parameters and non-linear dependencies, but adapting or tuning the audio device becomes difficult or impossible
Solution Approach 1:
The patent enables adaptation by allowing changes to activation function parameters and configurations without retraining the entire network. This provides a mechanism to tune the audio device for specific user needs while maintaining the complex problem-solving capabilities of the original network.
Solution Approach 2:
The patent establishes a fixed topology that is pre-configured to handle complex audio processing tasks. This preliminary structure allows for easier subsequent adaptation through activation function modification rather than requiring complete retraining or redesign.
3Reliability
If the neural network topology is changed to improve performance, then it can better handle specific audio processing tasks, but the computational cost increases
Solution Approach 1:
The patent applies local quality by allowing different activation functions to be applied to different nodes or layers of the neural network based on specific processing needs. This enables performance optimization in specific areas without increasing the overall computational cost of the entire network.
Data Source
AI summary
Disclosed is a computer-implemented method for processing audio data in an audio device by using a neural network, the neural network is defined by its topology including its number of layers and its number of nodes, where each node has an activation function. The method comprises obtaining first audio data. The method comprises obtaining an input, wherein the input comprises one or more of the following: input from an audio engineer tuning the audio device; input from a user of the audio device defining a preference; an audiogram for a user of the audio device; and device characteristics of the audio device. The method comprises, based on the input, adapting the activation function(s) of the one or more nodes of the neural network, while maintaining the topology of the neural network, thereby allowing the neural network to adapt in a computationally efficient way. The method comprises processing the first audio data, into processed audio data, by using the neural network with the adapted activation function(s). The method comprises outputting the processed audio data.