Key-Guided Audio Transformation for Dynamic ML Signal Matching
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Solution Overview
Problem
Existing machine learning models struggle with dynamically varying signal transformations, often learning suboptimal stochastically averaged transformations, particularly in cases involving multiple signal transformations or continuously time-varying scenarios.
Innovation Solution
A joint training process for a key generator ML model and an audio synthesis ML model, where the key generator generates metadata to guide the audio synthesis model in transforming input audio to desired output audio, optimizing the signal transformation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a static ML model is used to learn signal transformation, then the model structure is simple and easy to implement, but the model learns suboptimal stochastically averaged transformation and cannot adapt to dynamically varying transformations
Solution Approach 1:
The patent segments the transformation process into two distinct components: a key generator model that produces transformation parameters and an audio synthesis model that applies transformations. This segmentation allows the system to adapt to dynamic transformations while maintaining manageable model complexity through specialized functional divisions.
Solution Approach 2:
The patent introduces dynamic key parameters generated by the key generator model that continuously adapt to varying input conditions. These dynamic keys enable the audio synthesis model to perform time-varying transformations, transitioning from static to adaptive behavior without requiring complete model redesign.
2Adaptability or versatility
If multiple different signal transformations are supported, then the system versatility is improved, but the ML model tends to learn suboptimal averaged transformations
Solution Approach 1:
The patent introduces key parameters as intermediary representations that bridge the gap between diverse input conditions and specific transformation operations. These keys act as mediators that encode transformation intent, allowing the audio synthesis model to execute precise transformations without learning averaged behaviors from multiple transformation types.
Solution Approach 2:
The patent utilizes dynamic key parameters that change based on input characteristics to control transformation behavior. By varying these key parameters, the system can precisely control the audio synthesis model to perform different transformations with high quality, avoiding the degradation that occurs when a single model learns multiple transformations simultaneously.
3Speed
If continuously time-varying transformation is implemented, then the system responds to dynamic conditions, but static ML models learn suboptimal averaged transformation
Solution Approach 1:
The patent performs preliminary action by generating key parameters before the actual audio transformation. The key generator model processes input conditions and produces transformation keys in advance, which then guide the audio synthesis model to execute accurate time-varying transformations, ensuring both speed and precision.
Solution Approach 2:
The patent implements feedback through the key generator model that continuously monitors input conditions and adjusts key parameters accordingly. This feedback mechanism enables the system to respond to time-varying conditions in real-time while maintaining transformation accuracy through adaptive parameter adjustment.
Data Source
AI summary
A method comprise: receiving input audio and target audio having a target audio characteristic; using a first neural network, trained to generate key parameters that represent the target audio characteristic based on one or more of the target audio and the input audio, generating the key parameters; and configuring a second neural network, trained to be configured by the key parameters, with the key parameters to cause the second neural network to perform a signal transformation of the input audio, to produce output audio having an output audio characteristic corresponding to and that matches the target audio characteristic.