Neural Audio System Modeling for Low-Latency Amplifier Emulation
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Solution Overview
Problem
Existing amplifier modeling technologies are inefficient and time-consuming, particularly when emulating complex audio systems like guitar amplifiers, as they often require extensive computational resources and lengthy training times to achieve accurate emulation.
Innovation Solution
A neural network-based approach is employed to model audio systems, utilizing a sound source to electrically couple a test signal into a reference system, collecting output data, and training a neural network to converge towards the reference system's output, with features like perceptual loss functions and frequency masking to optimize training efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional digital signal processing methods are used to model audio systems, then the modeling process can be implemented with standard hardware, but the computational requirements are high and training times are long
Solution Approach 1:
The patent replaces traditional mechanical/digital signal processing approaches with neural networks that use learned representations. The neural network models capture complex audio system behaviors through trained weight matrices, substituting computationally intensive traditional DSP algorithms with more efficient neural representations that reduce both training time and runtime computational requirements.
Solution Approach 2:
The patent changes the fundamental parameters of how audio systems are modeled by transitioning from fixed digital signal processing algorithms to adaptive neural network parameters. The neural networks learn optimal parameter representations during training, enabling flexible modeling of non-linear and temporal dependencies with reduced computational overhead compared to traditional methods.
2Speed
If traditional digital signal processing methods are used to model audio systems, then the modeling can be performed with standard hardware, but the latency increases
Solution Approach 1:
The patent substitutes traditional DSP processing chains with neural network-based processing that operates more efficiently in terms of latency. The neural networks process audio signals through learned transformations that reduce processing steps and computational delays, enabling real-time or near-real-time audio system emulation with lower latency than conventional digital signal processing methods.
3Measurement precision
If complex non-linear behavior is modeled to accurately emulate physical amplifiers, then the modeling precision improves, but the computational resources required increase
Solution Approach 1:
The patent replaces computationally intensive traditional methods for modeling non-linear audio behavior with neural networks that learn accurate representations through training. The neural networks capture complex non-linear and temporal dependencies by learning from reference audio system data, achieving high modeling precision while reducing the computational resources needed for both training and inference compared to traditional approaches.
Solution Approach 2:
The patent transforms the approach to modeling non-linear behavior by changing from explicit mathematical modeling to implicit neural parameter learning. The neural networks learn optimal parameter representations that accurately capture non-linear audio system characteristics, achieving high precision modeling with reduced computational resource requirements through efficient parameter optimizations during training.
Data Source
AI summary
A process is provided for training a neural network that digitally models an audio system. A sound source is utilized to electrically couple a test signal into an input of a reference audio system. The output of the reference audio system is collected into an audio interface coupled to a computer. A neural network is then trained using the test signal and the captured information to derive a set of weight vectors with appropriate values such that the overall output of the neural network converges towards an output representative of the reference audio system, and a signal in the time domain from a musical instrument is processed through the trained neural network with a latency under 20 milliseconds. A graphical user interface then outputs a graphical representation of the trained neural network, where the graphical representation visually displays at least one virtual control for interaction by a user.