UGC and PGC Audio Classification for Adaptive Playback Processing
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Solution Overview
Problem
Existing audio processing systems struggle to differentiate between professionally generated content (PGC) and user-generated content (UGC) due to varying production conditions, leading to suboptimal playback experiences for UGC which often contains noise and reverberation issues.
Innovation Solution
A computer-implemented method that extracts spatial, temporal, and spectral features from audio content to classify it as PGC or UGC, using a classification model trained on labeled data to determine appropriate processing pipelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single audio processing pipeline is used for all digital audio content, then the system complexity is reduced, but the playback quality for UGC deteriorates due to noise and reverberation issues
Solution Approach 1:
The system dynamically selects between different processing pipelines (PGC pipeline or UGC pipeline) based on the classification result. The processing approach changes adaptively depending on whether the audio content is identified as PGC or UGC, allowing optimal processing for each content type without requiring manual configuration.
Solution Approach 2:
The system changes processing parameters by selecting different pipelines with distinct processing characteristics. The PGC pipeline uses parameters optimized for professionally produced content, while the UGC pipeline uses parameters designed to handle noise and reverberation in user-generated content, improving playback quality for each category.
2Loss of time
If digital audio content is processed without classification, then the processing time is reduced, but the playback experience deteriorates due to inappropriate processing
Solution Approach 1:
The system performs classification before processing to determine the appropriate processing pipeline. This preliminary action enables the system to prepare the correct processing parameters in advance, ensuring that the optimal processing path is selected before the actual audio processing begins, thereby maintaining both efficiency and quality.
3Measurement precision
If spatial indicators are computed for all frames and frequency bands, then the classification accuracy is improved, but the computational complexity increases
Solution Approach 1:
The system computes spatial indicators for only a subset of frames and all frequency bands, rather than for every single frame. This partial computation approach provides sufficient classification accuracy while significantly reducing the computational burden, as the spatial characteristics of audio content are relatively stable over short time periods.
Data Source
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AI summary
A system for managing user-generated content (UGC) and professionally generated content (PGC) is disclosed. The system is programmed to receive digital audio data having two channels from a social media platform. The system is programmed to extract spatial features that capture differences in the two channels from the digital audio data. The system is programmed to also extract temporal features, spectral features, and background features from the digital audio data. The system is programmed to then use the extracted features to determine whether to process the digital audio data as UGC or PGC before playback.