Deep Audio Encoder for Editable Effects Parameter Estimation
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Solution Overview
Problem
Existing audio signal processing technologies face limitations such as requiring custom modeling strategies, expensive human-labeled data, and high engineering effort due to their complexity and lack of editable parameter control, which restricts their effectiveness and usability.
Innovation Solution
A deep learning-based audio signal processing system that uses a deep encoder to estimate parameters for black-box audio effects plugins, allowing for end-to-end training and efficient processing of audio signals without the need for neural proxies or re-implementation of audio effects, utilizing a loss function and stochastic gradient approximation for optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If direct transform approaches are used for audio effects modeling, then audio processing capability is improved, but model complexity and computational cost increase significantly
Solution Approach 1:
The patent introduces an intermediary parameter estimation layer that bridges the audio input and the audio effect processing. This layer estimates the parameters needed for audio effects without requiring a complete custom neural network model for each effect, thereby reducing model complexity while maintaining processing capability.
Solution Approach 2:
The patent creates a universal audio effects processing system that can handle multiple different audio effects through a single framework. By using parameter estimation combined with existing audio effect processors, the system achieves multi-functionality without requiring separate complex models for each effect type.
2Ease of operation
If parameter estimator methods are used to predict audio effect parameters, then ease of operation is improved, but training data requirements and computational cost increase
Solution Approach 1:
The system enables self-service by allowing the parameter estimator to learn directly from audio pairs without requiring manual parameter annotation. The model automatically discovers parameter relationships during training by comparing predicted parameters with actual parameters from reference audio, eliminating the need for expensive human-labeled data.
3Manufacturing precision
If differentiable digital signal processing is implemented, then audio quality is improved, but engineering effort and implementation complexity increase
Solution Approach 1:
The patent uses copying by integrating existing audio effect processors into the training framework rather than re-implementing them from scratch. The system copies the functionality of standard audio effects and makes them differentiable through parameter estimation, reducing engineering effort while maintaining audio quality.
4Reliability
If custom modeling strategies are used per audio effect, then audio processing effectiveness is improved, but device complexity and development time increase
Solution Approach 1:
The patent applies parameter changes by estimating parameters that control existing audio effect processors rather than creating custom models for each effect. This approach maintains the effectiveness of proven audio effects while simplifying the modeling process to parameter prediction, reducing both complexity and development time.
Data Source
AI summary
Embodiments are disclosed for determining an answer to a query associated with a graphical representation of data. In particular, in one or more embodiments, the disclosed systems and methods comprise receiving an input including an unprocessed audio sequence and a request to perform an audio signal processing effect on the unprocessed audio sequence. The one or more embodiments further include analyzing, by a deep encoder, the unprocessed audio sequence to determine parameters for processing the unprocessed audio sequence. The one or more embodiments further include sending the unprocessed audio sequence and the parameters to one or more audio signal processing effects plugins to perform the requested audio signal processing effect using the parameters and outputting a processed audio sequence after processing of the unprocessed audio sequence using the parameters of the one or more audio signal processing effects plugins.