Microphone Style Transfer Using One-Shot Audio Augmentation
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Solution Overview
Problem
Existing audio recognition models suffer from significant performance degradation due to domain shift caused by microphone variability, with existing methods either being limited to the cepstral domain, requiring multiple microphones during training, or introducing computational overhead.
Innovation Solution
A machine-learned microphone model that performs one-shot microphone style transfer by processing input audio data with impulse response, power-frequency, filtering, and clipping models to generate target audio data, capable of augmenting training data to reflect different microphone transformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If CycleGAN is used to learn mapping between microphones, then it can model microphone transformations without paired data, but it requires training a separate CycleGAN for every microphone type which introduces significant computational overhead during deployment
Solution Approach 1:
The patent segments the microphone transformation process into distinct components: a domain-agnostic speech encoder that extracts speech representations, and domain-specific microphone models that capture microphone characteristics. This segmentation allows the speech processing part to be trained once and reused across multiple microphones, while only the lightweight microphone-specific models need to be adapted for each microphone type, significantly reducing computational overhead compared to training complete CycleGANs for every microphone.
Solution Approach 2:
The patent creates a universal speech encoder that can process audio from any microphone type without requiring microphone-specific training. This encoder is trained on diverse audio data and can extract speech representations that are invariant to microphone characteristics. The universal encoder works across multiple domains (different microphones, recording conditions) while separate lightweight models handle microphone-specific transformations, achieving both versatility and efficiency.
2Adaptability or versatility
If CycleGAN is used for microphone style transfer, then it can work with unpaired data, but it requires several minutes of training data per microphone and cannot map completely unrelated samples effectively
Solution Approach 1:
The patent performs preliminary training of a universal speech encoder on diverse audio data from multiple microphones and recording conditions before deployment. This pre-trained encoder learns speech representations that are robust to microphone variations. When deploying to a new microphone, only the lightweight microphone-specific model needs to be trained on a few seconds of data, rather than training from scratch as CycleGAN requires. This preliminary action dramatically reduces the training time required for each new microphone.
Solution Approach 2:
The patent creates a universal speech encoder that copies and generalizes speech processing capabilities across different microphone domains. The encoder learns from diverse training data and copies its speech extraction capabilities to work with any microphone type without requiring extensive retraining. This allows the system to effectively process unpaired data from new microphones with minimal training, overcoming CycleGAN's limitation of requiring several minutes of training data per microphone.
3Reliability
If additive corrections in the cepstral domain are used, then model robustness to microphone variability can be improved, but the method is compatible only with applications operating on inputs in the cepstral domain
Solution Approach 1:
The patent introduces an intermediary universal speech encoder that transforms audio inputs from different microphone domains into a unified speech representation space. This encoder acts as a mediator between the diverse input domains and the downstream application, handling domain adaptation in a way that is transparent to the application. The application receives standardized speech representations regardless of the input microphone type, maintaining compatibility while achieving robustness through the encoder's domain-invariant feature extraction.
Data Source
AI summary
Example implementations of the present disclosure relate to machine learning for microphone style transfer, for example, to facilitate augmentation of audio data such as speech data to improve robustness of machine learning models trained on the audio data. Systems and methods for microphone style transfer can include one or more machine-learned microphone models trained to obtain and augment signal data to mimic characteristics of signal data obtained from a target microphone. The systems and methods can include a speech enhancement network for enhancing a sample before the style transfer. The augmentation output can then be utilized for a variety of downstream tasks.