Lyrics Analyzer Using N-Dimensional Vectors for Explicit Content Detection
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Solution Overview
Problem
Current methods for systematically tagging and categorizing musical tracks are inconsistent and require significant human intervention, and there is a need for an automatic method to classify songs as explicit or not, especially considering lyrical and acoustic content.
Innovation Solution
A system that uses a generative statistical model to extract n-dimensional vectors from lyrics and acoustic features, allowing for the calculation of similarity scores and generation of playlists or explicitness indicators, based on these vectors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual tagging by music producers is used, then tracks can be labeled with genre, mood, and other tags, but the tagging becomes inconsistent and requires significant human intervention
Solution Approach 1:
The system enables tracks to tag themselves automatically by analyzing their own acoustic features and lyrics without requiring human producers to manually add tags. The automated tagging system processes tracks independently, extracting genre, mood, and other metadata through computational analysis of the track's inherent properties.
Solution Approach 2:
The patent replaces the mechanical process of manual human tagging with an automated computational system that uses acoustic feature extraction and lyrical analysis. This substitution eliminates the need for human producers to manually categorize tracks, thereby improving consistency while reducing human intervention.
2Productivity
If pre-generated song tags are used for playlist creation, then playlists can be generated efficiently, but the playlists lack lyrical similarity and acoustic coherence
Solution Approach 1:
The system merges multiple data sources including acoustic features, lyrical content, and pre-generated tags into a unified playlist generation framework. By combining these diverse information sources, the system creates playlists that satisfy both efficiency requirements and lyrical-acoustic coherence, overcoming the limitations of using any single tagging approach alone.
Solution Approach 2:
The patent transforms the playlist generation process by changing the parameters used for similarity calculation to include both acoustic features and lyrical content. This parameter expansion allows the system to generate playlists that are not only efficient but also maintain high lyrical and acoustic coherence among selected tracks.
3Measurement precision
If music producers manually label explicit content, then explicit tracks can be identified, but the classification lacks flexibility and cannot detect implicit explicit concepts
Solution Approach 1:
The system introduces an intermediary automated analysis layer between the track and the explicitness classification. This intermediary processes both acoustic features and lyrical content to detect explicit concepts, providing a flexible and accurate classification mechanism that goes beyond simple keyword matching while maintaining adaptability to different explicitness criteria.
Solution Approach 2:
The patent creates a universal classification system that handles multiple types of explicit content detection through a single flexible framework. This multi-functional approach enables the system to detect both overt explicit words and subtle implicit explicit concepts across diverse musical genres and lyrical styles, improving both accuracy and adaptability.
Data Source
AI summary
A lyrics analyzer generates tags and explicitness indicators for a set of tracks. These tags may indicate the genre, mood, occasion, or other features of each track. The lyrics analyzer does so by generating an n-dimensional vector relating to a set of topics extracted from the lyrics and then using those vectors to train a classifier to determine whether each tag applies to each track. The lyrics analyzer may also generate playlists for a user based on a single seed song by comparing the lyrics vector or the lyrics and acoustics vectors of the seed song to other songs to select songs that closely match the seed song. Such a playlist generator may also take into account the tags generated for each track.