Neural Network Audio System Modeling with Closed-Loop Training
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Solution Overview
Problem
Existing audio processing methods struggle to accurately model non-linear and time-varying audio systems, such as guitar amplifiers, due to the difficulty in combining expert knowledge with neural networks, leading to inefficiencies in training, composition, and user control integration.
Innovation Solution
A closed-loop modeling process using a neural network with trainable parameters that simulates audio systems, incorporating both white-box and black-box methods, allowing for real-time modeling of audio systems by adjusting weights to minimize differences between simulation and reference signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional white-box methods are used to model audio systems, then expert knowledge can be incorporated, but the process requires time-consuming and expensive measurements and is difficult to implement
Solution Approach 1:
The patent segments the modeling process into distinct phases: data collection from reference audio system, neural network training with segmented audio data, and model deployment. This segmentation makes the complex white-box modeling process more manageable and implementable by breaking it into automated stages rather than requiring manual expert intervention throughout.
Solution Approach 2:
The patent introduces an intermediary automated training process that bridges expert knowledge and neural network implementation. The system uses intermediate representations (spectrograms, mel-frequency features) as mediators between the physical audio system and the neural network, allowing expert knowledge to be encoded in the architecture while automation handles the complex training details.
2Ease of operation
If black-box optimization methods using neural networks are used, then training can be automated, but the methods do not take advantage of known information about the reference audio system and are difficult to understand and evaluate
Solution Approach 1:
The patent merges black-box neural network automation with white-box domain knowledge by combining automated training procedures with architecture designs that incorporate audio processing expertise. The neural network is trained automatically using standard optimization techniques, while the architecture itself encodes domain knowledge through specialized layers and feature representations, achieving both automation and knowledge retention.
Solution Approach 2:
The patent applies local quality by incorporating domain knowledge specifically in the neural network architecture and feature extraction layers, while allowing the weight optimization to remain purely data-driven. Different parts of the system have different qualities: the architecture reflects expert knowledge, while the learned parameters are purely empirical, achieving a localized combination of approaches.
3Measurement precision
If complex neural network models are trained to accurately simulate audio systems, then modeling precision improves, but training time and computational resources increase
Solution Approach 1:
The patent applies partial action by training the neural network to achieve sufficient accuracy for the target application rather than perfect fidelity. The system uses appropriate loss functions and training criteria that target the specific requirements of audio synthesis, stopping training when adequate performance is reached rather than continuing to minimize all possible errors, thus reducing training time while maintaining practical accuracy.
Solution Approach 2:
The patent changes parameters during training including learning rates, batch sizes, and architectural hyperparameters to optimize the training process. The system adapts training parameters dynamically and uses efficient representations (compressed audio features, downsampling) to reduce the computational burden while maintaining modeling accuracy, balancing precision and training time.
Data Source
AI summary
A closed loop system with trainable parameters and known dynamics is trained to model a reference audio system. Solving the closed loop equations in continuous time can involve an implicit solver. Training may be carried out by repeating a set of operations. The set of operations may include computing in the time domain (via the closed loop system) a model output, based upon an input. The output is expected to approximate the output of the reference audio system. The set of operations may further include applying a loss function, where the loss function measures the difference between the output of the closed loop system and the output of the reference audio system. The set of operations also includes adjusting the trainable parameters according to the output of the loss function. The training produces an output model that can be used to filter input signals.