Audio Violation Detection Using Fully Connected Network
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Solution Overview
Problem
Current methods for detecting voice violations in chat environments, such as user reporting and manual monitoring, are inefficient and result in time lags and high costs, as they often intervene after the event has occurred.
Innovation Solution
A method and device utilizing a pre-trained fully connected network model to detect voice violations by acquiring audio file data and generating a voice detection result based on attribute detection data, including user rating data, classification probability data, and voiceprint feature data, allowing for real-time detection and prevention of voice violations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If user reporting or manual monitoring is used to detect voice violations, then detection can be performed, but time lag and high costs occur
Solution Approach 1:
The system performs preliminary action by extracting voice features and performing violation judgment in real-time during audio transmission, rather than waiting for user reporting or manual monitoring after the fact. The voice feature extraction module continuously analyzes audio data as it is transmitted, enabling proactive detection and prevention of voice violations before they can cause harm.
Solution Approach 2:
The invention replaces the mechanical manual monitoring system with an automated voice processing system. The voice feature extraction module uses signal processing techniques to automatically extract features from audio data, and the violation judgment module uses predefined rules or machine learning models to automatically determine violations, eliminating the need for human operators to manually review each audio clip.
2Reliability
If user reporting or manual monitoring is used to detect voice violations, then detection can be performed, but high costs are incurred
Solution Approach 1:
The system implements self-service by enabling the platform to automatically detect and handle voice violations without requiring human intervention. The voice feature extraction and violation judgment modules work autonomously to identify and flag problematic content, allowing the system to police itself and reducing operational costs associated with manual monitoring teams.
Solution Approach 2:
The invention replaces expensive manual monitoring operations with an automated computational system. By using algorithmic voice feature extraction and automated violation judgment, the system eliminates or significantly reduces the need for human operators, thereby reducing labor costs and operational expenses while maintaining or improving detection effectiveness.
3Productivity
If automated voice detection is implemented, then real-time detection is achieved, but system complexity increases
Solution Approach 1:
The system applies segmentation by dividing the voice detection process into distinct functional modules: a voice feature extraction module that handles signal processing and feature extraction, and a violation judgment module that performs classification and decision-making. This modular architecture allows each component to be optimized independently and makes the overall system more manageable and maintainable despite its computational complexity.
Data Source
AI summary
Provided is a method for detecting audio, which includes acquiring audio file data; determining attribute detection data corresponding to the audio file data; and generating a voice detection result corresponding to the audio file data by voice violation detection on the attribute detection data by a fully connected network model. A device for detecting audio and a non-transitory computer-readable storage medium are also provided.