Audio Dereverberation via Segmented Acoustic Impulse Response Synthesis
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Solution Overview
Problem
Existing audio enhancement techniques, such as dereverberation and noise suppression, often introduce unwanted perceptual distortions and are computationally intensive, prone to overfitting due to the complexity of machine learning models required for accurate performance.
Innovation Solution
The use of low-complexity machine learning models, specifically combining convolutional neural networks (CNNs) with recurrent elements, and the generation of augmented training sets through synthesized acoustic impulse responses, to efficiently dereverberate audio signals while minimizing reverberation and preserving audio quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex machine learning models are used for accurate dereverberation, then dereverberation accuracy is improved, but device complexity and computational load increase
Solution Approach 1:
The patent segments the acoustic impulse response into early reflection portion and late reflection portion, and processes them separately through different neural network branches. This segmentation allows the model to focus computational resources on the more critical early reflections while using a simpler approach for late reflections, thereby reducing overall model complexity while maintaining dereverberation accuracy.
Solution Approach 2:
The patent transforms the dereverberation problem from direct time-domain processing into frequency-domain processing using Fourier transforms. This dimensionality change allows the neural network to operate on spectral representations, enabling more efficient computation and reducing model complexity while achieving accurate dereverberation results.
2Measurement precision
If complex machine learning models are used for accurate dereverberation, then dereverberation accuracy is improved, but computational load and processing time increase
Solution Approach 1:
By dividing the impulse response processing into separate early and late reflection branches, the patent reduces the computational burden on any single neural network component. This segmentation allows for more efficient parallel processing and reduces overall computational load while maintaining high dereverberation accuracy.
Solution Approach 2:
The patent applies preliminary Fourier transforms to convert the input signal and impulse response into frequency domain representations before neural network processing. This preliminary action simplifies subsequent computational operations and reduces the overall computational load required for accurate dereverberation.
3Ease of manufacture
If standard training sets are used for machine learning models, then training simplicity is maintained, but model accuracy and generalization performance deteriorate
Solution Approach 1:
The patent generates synthesized impulse responses with controlled early and late reflection characteristics before training the neural network. This preliminary generation of diverse training data from limited real recordings enables the model to learn robust dereverberation patterns without requiring extensive manual data collection, thus maintaining training simplicity while improving model accuracy.
Solution Approach 2:
The patent creates synthetic copies of acoustic environments by generating artificial impulse responses that mimic real reverberation characteristics. These synthesized copies expand the training dataset diversity without requiring proportional increases in real-world data collection efforts, thereby improving model generalization while keeping the training process manageable.
4Measurement precision
If enhancement techniques are applied to audio content, then audio quality is improved, but unwanted perceptual distortions are introduced
Solution Approach 1:
The patent applies different processing strategies to different portions of the impulse response: early reflections are processed with one neural network branch while late reflections are processed with another branch. This local quality approach allows tailored optimization for each reflection type, improving overall audio quality while minimizing perceptual distortions that would result from uniform processing.
Solution Approach 2:
The patent employs a loss function that incorporates both spectral convergence and perceptual quality metrics during training. This feedback mechanism guides the neural network to minimize both technical error metrics and perceptual distortions, ensuring high audio quality without introducing unwanted artifacts in the enhanced output.
Data Source
AI summary
A method for dereverberating audio signals is provided. In some implementations, the method involves obtaining a real acoustic impulse response (AIR); identifying a first portion of the real AIR corresponding to early reflections of a direct sound and a second portion of the real AIR that corresponding to late reflections of the direct sound; generating one or more synthesized AIRs by modifying the first portion of the real AIR and/or the second portion of the real AIR; and using the real AIR and the one or more synthesized AIRs to generate a plurality of training samples, each training sample comprising an input audio signal and a reverberated audio signal, wherein the reverberated audio signal is generated based on the input audio signal and one of the real AIR or one of the one or more synthesized AIRs, which plurality of training samples are used to train a machine learning model.