Neural Network Audio Denoising for Low-Latency Speech
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Solution Overview
Problem
Captured audio data often contains significant noise, which degrades the quality and understandability of speech, particularly in applications like online multiplayer gaming and teleconferencing.
Innovation Solution
An audio denoising system using a neural network-based pipeline that includes a feature extractor to generate an audio spectrogram, a noise model to predict noise presence, and a post-processing module to remove noise, executed on GPUs or CPUs, achieving low latency and low GPU consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional audio processing methods are used, then computational resources are consumed, but noise removal effectiveness is insufficient
Solution Approach 1:
The patent replaces traditional mechanical audio processing methods with a neural network-based system. The neural network automatically learns optimal noise removal parameters and patterns from training data, substituting manual tuning and conventional signal processing algorithms with an intelligent system that adapts to different noise conditions dynamically.
Solution Approach 2:
The neural network dynamically adjusts processing parameters based on the input audio characteristics. By changing parameters such as filter coefficients, time-frequency transformation settings, and noise threshold values according to the specific audio scene, the system achieves effective noise removal while optimizing computational resource usage for different conditions.
2Reliability
If complex noise removal algorithms are applied, then noise removal quality improves, but processing latency increases
Solution Approach 1:
The neural network is trained in advance on extensive audio data containing various noise types and conditions. This preliminary training phase allows the network to learn optimal noise removal strategies beforehand, so that during actual audio processing, the network can apply pre-learned knowledge directly without requiring complex real-time computations, thereby reducing latency.
Solution Approach 2:
The audio processing is divided into distinct segments: feature extraction, noise classification, and noise removal. Each segment is handled by specialized neural network components that process specific aspects of the audio signal independently, allowing for optimized processing of each stage and reducing overall latency through parallel processing capabilities.
3Productivity
If real-time processing is implemented, then responsiveness is improved, but computational complexity increases
Solution Approach 1:
The patent extracts and processes only the essential features from the audio signal using the neural network. Rather than analyzing the entire audio spectrum in detail, the network identifies and focuses on the most relevant acoustic features for noise detection and removal, reducing computational complexity while maintaining real-time processing capability.
Data Source
AI summary
Apparatuses, systems, and techniques are presented to reduce noise in audio. In at least one embodiment, one or more neural networks are used to determine a noise signal in one or more speech signals.