Neural Audio Augmentation for Real-World Background Acoustics
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Solution Overview
Problem
Conventional data augmentation methods for speech processing systems fail to account for background acoustic properties, leading to performance mismatches between training and real-world conditions due to the reliance on predefined acoustic environments that may not match the target environment.
Innovation Solution
A neural network-based data augmentation process that estimates noise spectrum and acoustic neural embeddings from target audio signals, allowing for dynamic augmentation of input speech signals to match the background acoustics of a target environment, including both noise and reverberation adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional data augmentation methods use predefined acoustic environments, then the training process is simplified, but the performance of speech processing systems deteriorates due to mismatch with real-world acoustic conditions
Solution Approach 1:
The patent replaces conventional signal processing techniques with neural networks to estimate background acoustic properties. Instead of using predefined acoustic models or manual noise characterization, the system employs neural networks to automatically learn and replicate background acoustic characteristics from target audio signals, enabling more accurate data augmentation that matches real-world conditions.
Solution Approach 2:
The patent changes the approach from using fixed predefined acoustic parameters to dynamically estimating acoustic parameters through neural networks. The system estimates noise spectrum and acoustic neural embeddings from target audio signals, allowing the augmented data to adapt to various real-world acoustic environments rather than being constrained by predetermined acoustic models.
2Productivity
If data augmentation uses signal processing techniques with predefined noise and room impulse response files, then the computational process is more efficient, but the accuracy of matching target acoustic properties deteriorates
Solution Approach 1:
The patent replaces traditional signal processing methods with neural network-based estimation. Instead of using conventional algorithms to process predefined noise and room impulse response files, the system employs neural networks to directly estimate background acoustic properties from target audio signals, achieving both computational efficiency and high accuracy in matching real-world acoustic characteristics.
3Device complexity
If conventional approaches assume known background acoustic properties, then the data augmentation process is simpler, but the system cannot adapt to unknown or varying acoustic environments
Solution Approach 1:
The patent introduces dynamic estimation of background acoustic properties through neural networks. Instead of relying on static predefined acoustic models, the system dynamically estimates noise spectrum and acoustic neural embeddings from target audio signals, enabling adaptation to unknown and varying acoustic environments while maintaining manageable system complexity through automated estimation processes.
Data Source
AI summary
A method, computer program product, and computing system for estimating noise spectrum from a target audio signal segment. An acoustic neural embedding is generated from the target audio signal segment. An augmented audio signal segment is generated with background acoustic properties of the target audio signal segment by processing an input audio signal segment with the noise spectrum and the acoustic neural embedding using a neural network.