Hierarchical Audio Detection System for Computational Cost Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current audio detection systems face significant computational challenges in distinguishing between real and generated audio due to the vast amount of data and the complexity of models required for high accuracy, leading to high resource consumption and time inefficiencies.
Innovation Solution
A hierarchical generated audio detection system comprising an audio preprocessing module, CQCC and LFCC feature extraction modules, a lightweight coarse-level detection model, and a fine-level deep identification model, which performs two-stage screening to reduce computational costs without compromising identification performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a high-precision system is used to accurately screen generated audio from large amounts of data, then identification accuracy is improved, but computational cost and time consumption increase significantly
Solution Approach 1:
The detection system is divided into two distinct stages: a first-stage lightweight coarse-level detection model for initial screening, and a second-stage fine-level deep identification model for detailed analysis. This segmentation allows the system to apply different computational resources to different tasks, reducing overall computational cost while maintaining high accuracy.
Solution Approach 2:
The system applies partial action by using the lightweight first-stage model to process all audio data for coarse screening, and only applies the computationally intensive second-stage model to audio clips that are identified as potentially generated. This avoids the excessive computation that would result from applying the fine-level model to all data.
2Measurement precision
If a complex model structure is used to achieve high accuracy in audio discrimination, then identification performance is improved, but device complexity and computational requirements increase
Solution Approach 1:
The model structure is segmented into two distinct architectures: a simplified lightweight model for the first stage and a complex deep model for the second stage. This segmentation allows each model to be optimized for its specific purpose, avoiding the need for a single complex model to handle all tasks.
Solution Approach 2:
Different model complexities are applied to different stages of the detection process. The first stage uses a lightweight model with simpler structure suitable for rapid coarse screening, while the second stage employs a fine-level deep model with higher complexity only where needed for detailed identification of potentially generated audio.
3Measurement precision
If detailed analysis is performed on all audio data to ensure high detection accuracy, then measurement precision is improved, but productivity and processing speed decrease
Solution Approach 1:
The processing workflow is segmented into two stages with different processing depths. The first-stage lightweight model performs rapid coarse-level detection on all audio data, and only audio clips flagged as potentially generated undergo second-stage fine-level deep identification. This segmentation maintains high detection accuracy while dramatically improving overall processing speed.
Solution Approach 2:
The system applies partial detailed analysis only to the subset of audio data that the first-stage model identifies as potentially generated, rather than performing detailed analysis on all audio data. This approach maintains detection accuracy for generated audio while significantly improving productivity by avoiding unnecessary detailed analysis of real audio.
Data Source
AI summary
Disclosed is a hierarchical generated audio detection system, comprising an audio preprocessing module, a CQCC feature extraction module, a LFCC feature extraction module, a first-stage lightweight coarse-level detection model and a second-stage fine-level deep identification model; the audio preprocessing module preprocesses collected audio or video data to obtain an audio clip with a length not exceeding the limit; inputting the audio clip into CQCC feature extraction module and LFCC feature extraction module respectively to obtain CQCC feature and LFCC feature; inputting CQCC feature or LFCC feature into the first-stage lightweight coarse-level detection model for first-stage screening to screen out the first-stage real audio and the first-stage generated audio; inputting the CQCC feature or LFCC feature of the first-stage generated audio into the second-stage fine-level deep identification model to identify the second-stage real audio and the second-stage generated audio, and the second-stage generated audio is identified as generated audio.