AI Video Censoring with User-Defined Word Muting
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Solution Overview
Problem
Current censoring applications lack the ability to tailor the viewing experience to individual user preferences and effectively censor vulgar language in online/social media platform videos or live videos, failing to provide customizable and real-time content moderation.
Innovation Solution
A system that utilizes user input via a UI to create a word bank, converts audio to text using an AI engine, identifies timestamps of prohibited words, and manipulates a mute function in the digital video player using API commands to censor specific language, with buffer times customizable by the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current censoring applications are used, then vulgar language can be detected and censored, but the viewing experience cannot be tailored to individual user preferences
Solution Approach 1:
The system dynamically adapts censorship parameters based on user preferences. Users can customize word banks, buffer times, and censorship thresholds, allowing the system to adjust its behavior in real-time according to individual needs rather than using fixed parameters.
Solution Approach 2:
The censorship system is divided into modular components: word bank management, audio processing, timestamp generation, and mute function manipulation. Each component can be independently configured and adjusted, allowing users to customize specific aspects without affecting the entire system.
2Productivity
If real-time censorship is implemented for live videos, then content moderation is effective, but processing time and system resources increase
Solution Approach 1:
The system pre-processes and segments audio from live videos into manageable chunks before analysis. By preparing audio segments in advance and maintaining a buffer, the system reduces real-time processing pressure and minimizes delays during actual censorship operations.
Solution Approach 2:
The system uses buffer times to strategically skip or rush through non-critical processing steps. During live video censorship, less critical audio segments can be processed faster or with reduced scrutiny, allowing the system to maintain real-time performance while focusing resources on potentially problematic content.
3Measurement precision
If audio is converted to text and analyzed for prohibited words, then accurate censorship is achieved, but processing time increases
Solution Approach 1:
The system applies partial action by converting only portions of audio to text for analysis rather than entire audio streams. By selectively processing audio segments that are more likely to contain prohibited language, the system maintains high detection accuracy while reducing overall processing time.
Solution Approach 2:
The system performs preliminary audio segmentation and preprocessing before text conversion. By organizing audio into discrete, analyzable segments in advance, the system streamlines the text conversion process and reduces the time required for accurate word detection during actual censorship operations.
4Manufacturing precision
If buffer times are made customizable by users, then censorship precision is improved, but system complexity increases
Solution Approach 1:
Buffer times are made dynamic and user-adjustable rather than fixed. Users can modify buffer time parameters based on their specific censorship needs, allowing precise control over when censorship actions occur while maintaining system flexibility.
Solution Approach 2:
The system provides self-service capabilities allowing users to independently configure and adjust buffer time parameters without requiring technical assistance. Through an intuitive interface, users can optimize censorship timing precision according to their preferences and needs.
Data Source
AI summary
A method for censoring digital media can include obtaining a user input via a user interface in regard to a digital video, obtaining an audio file for the digital video, and sending the audio file and a word back to an artificial intelligence (AI) engine. In some embodiments, the method can further include converting the audio file to text and identifying occurrences of words from the word bank and timestamps corresponding to the occurrences via the AI engine. In at least one embodiment, the timestamps received from the AI engine can correspond to moments in the digital video where the words in the word bank are used. Additionally, the method can include manipulating a mute function of a player of the digital video based on the timestamps received from the AI engine.