Automatic Audio Censoring via Lyric-Audio Timestamp Alignment
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Solution Overview
Problem
Manual creation of censored audio data by music producers is inefficient, as they often do not remove explicit lyrical content from audio uploads to platforms like YouTube and Google Play Music, leading to a need for automated solutions that can accurately identify and obscure objectionable words without excessive computational resources.
Innovation Solution
An automatic censoring system processes audio data with song lyrics using a finite state machine and neural networks to identify timestamps of explicit words, adjusting amplitude data to render these words incomprehensible, thereby creating a censored version of the audio.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual censoring is performed by music producers, then censorship accuracy can be maintained, but productivity is significantly reduced and manual effort increases
Solution Approach 1:
The system enables automatic self-censoring of audio content by processing audio data through amplitude analysis and lyric comparison without requiring manual intervention from music producers, thereby resolving the contradiction between maintaining censorship accuracy and improving productivity
Solution Approach 2:
The patent replaces the manual mechanical process of censorship with an automated computational system that uses amplitude data processing, timestamp generation, and lyric comparison algorithms to identify and censor explicit content, significantly improving productivity while maintaining accuracy
2Productivity
If automated censoring systems are implemented, then productivity increases, but device complexity and computational resources increase
Solution Approach 1:
The system divides the censoring process into distinct modular components: amplitude data processing, lyric retrieval, timestamp generation, and audio modification. This segmentation reduces overall system complexity by making each component independent and manageable while maintaining high productivity
Solution Approach 2:
The system creates a universal censoring framework that can process different audio files, lyric formats, and censorship requirements through a single integrated platform, reducing the need for multiple specialized systems and thereby decreasing overall device complexity
3Productivity
If automated censoring systems are implemented, then productivity increases, but manual effort is reduced
Solution Approach 1:
The system performs preliminary actions by automatically retrieving lyrics, comparing them against audio content, and generating timestamps before the actual censoring process, thereby eliminating the need for manual preprocessing and significantly reducing overall processing time and manual effort
Data Source
AI summary
Embodiments relate to censoring audio data. A censoring system receives audio data including a song tag and amplitude data as a function of time. The amplitude data represents spoken words occurring over a duration, as well as non-spoken word sound overlapping with some of the spoken words during the duration. The system accesses a set of song lyrics and processes the set of song lyrics and the amplitude data together to identify timestamps in the amplitude data. These timestamps indicate a time during the duration when one of the words from the lyrics begins in the amplitude data. The system compares the words in the set of song lyrics to a blacklist and adjusts the amplitude data at the timestamps of blacklisted word occurrences to render the audio at the blacklisted words incomprehensible. The system outputs the adjusted amplitude data.