Two-Stage Noise Classification for User Content Audio
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Solution Overview
Problem
Existing audio processing systems struggle to differentiate between user-generated content (UGC) noise and professionally-generated content (PGC) noise, leading to inadequate noise reduction or enhancement, which affects the quality of UGC with low audio quality due to environmental and recording device limitations.
Innovation Solution
A two-stage noise classification system using machine learning models to detect and distinguish UGC noise from PGC noise, applying specific audio processing techniques to improve audio quality by processing audio signals based on confidence scores calculated from feature extraction and classification processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If audio processing techniques are applied to enhance audio quality in UGC, then audio quality is improved, but noise reduction effectiveness deteriorates because the system cannot distinguish UGC noise from PGC noise
Solution Approach 1:
The patent divides noise detection into two distinct stages: first detecting whether noise is present, then classifying the type of noise (UGC or PGC). This segmentation allows the system to apply different processing strategies for different noise types, resolving the contradiction between enhancing audio quality and accurately detecting noise characteristics.
Solution Approach 2:
The patent introduces an intermediary classification mechanism that sits between noise detection and audio processing. This intermediary stage analyzes additional features (spectral characteristics, temporal patterns, source identification) to determine whether the detected noise is UGC or PGC, enabling the system to make informed decisions about whether to apply noise reduction, thereby improving both audio quality and detection accuracy.
2Object-affected harmful factors
If noise reduction methods are applied to all detected noise, then UGC noise is reduced, but PGC noise is incorrectly removed which degrades content quality
Solution Approach 1:
The patent applies different processing qualities to different types of noise based on their characteristics. For UGC noise, aggressive noise reduction is applied to eliminate harmful background sounds. For PGC noise, a more conservative approach is used to preserve the artistic intent and content integrity. This local differentiation resolves the contradiction between reducing harmful noise and maintaining content reliability.
Solution Approach 2:
The system dynamically adjusts the noise reduction strength based on the classified noise type. When UGC noise is detected, stronger reduction parameters are applied. When PGC noise is detected, weaker or no reduction is applied. This dynamic adaptation allows the system to effectively reduce harmful noise while preserving content integrity, resolving the contradiction between these two opposing requirements.
3Stability of the object's composition
If volume leveling techniques are applied to UGC, then audio consistency is improved, but UGC noise is boosted which negatively impacts user experience
Solution Approach 1:
The patent performs preliminary noise classification and reduction before applying volume leveling techniques. By removing or reducing UGC noise in advance, the subsequent volume leveling operation does not amplify harmful noise while still achieving audio consistency. This preliminary action resolves the contradiction between improving audio stability and preventing noise amplification.
Data Source
AI summary
A method of audio processing includes classifying an audio signal as noise or as non-noise using a first model. For a noise signal. the audio signal is classified as user-generated content (UGC) noise or as professionally-generated content (PGC) noise using a second model. For a non-noise signal or PGC noise. the audio signal is processed using a first audio processing process. For UGC noise. the audio signal is processed using a second audio processing process.