Music Categorization Using RTP Scores
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Solution Overview
Problem
Traditional music selection systems, such as Pandora and Spotify, often recommend tracks that diverge from the user's initial selection due to their complexity and reliance on human analysis, leading to user dissatisfaction and limited control over playlist direction.
Innovation Solution
A method for categorizing streamed music using computer-derived RTP scores based on rhythm, texture, and pitch attributes, allowing for objective music categorization and playlist creation, which includes determining high-level acoustic attributes from low-level data and using a greedy algorithm or neural networks to assign RTP scores, enabling users to customize playlists based on mood and intensity levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional music selection systems use complex human analysis and genre-based organization, then they can provide diverse music recommendations, but the recommendations often diverge from user preferences and lose control over playlist direction
Solution Approach 1:
The patent replaces complex human analysis mechanisms with automated computer-based RTP scoring systems. The system automatically extracts rhythm, texture, and pitch features from music tracks and assigns objective scores, eliminating the need for manual genre classification and giving users precise control over playlist direction through quantitative criteria rather than subjective algorithms.
Solution Approach 2:
The patent transforms music classification from qualitative genre labels to quantitative RTP scores. By converting music characteristics into measurable parameters (rhythm score, texture score, pitch score), the system enables precise user control through numerical thresholds while maintaining objective and reproducible classification results.
2Measurement precision
If music categorization relies on human analysis of genre and style, then the system can understand musical nuances, but the process is time-consuming and subjective
Solution Approach 1:
The patent substitutes manual human analysis with automated computational algorithms that objectively measure rhythm, texture, and pitch characteristics. The system automatically processes music files, extracts acoustic features, and assigns RTP scores without human intervention, dramatically reducing analysis time while maintaining consistent and reproducible results.
Solution Approach 2:
The system performs self-service analysis by automatically extracting musical features and generating classifications without requiring human experts. The automated algorithms independently analyze music tracks, extract acoustic attributes, and assign categories, eliminating the time-consuming human analysis process while maintaining measurement precision.
3Productivity
If the system uses automated algorithms for music recommendation, then the process is fast and objective, but the recommendations may lack musical nuance and context
Solution Approach 1:
The patent segments music analysis into three distinct independent components: rhythm, texture, and pitch. Each component is analyzed separately using specialized algorithms, allowing the system to capture nuanced musical characteristics through detailed breakdown of individual attributes while maintaining fast automated processing speeds.
Solution Approach 2:
The patent transforms complex musical nuances into measurable parameters through the RTP scoring system. By converting subtle musical characteristics into quantifiable rhythm, texture, and pitch scores, the system maintains precision in capturing musical nuance while enabling fast automated processing and objective comparison of music tracks.
Data Source
AI summary
A method for categorizing streamed music based on a sample set of RTP scores for predetermined tracks. High-level acoustic attributes for tracks are determined by an analyzed extraction of low-level data from the tracks. The high-level acoustic attributes are used to develop computer-derived RTP scores for the tracks based on the sample set, which includes RTPs score for a plurality of possible combinations of a rhythm score (R), a texture score (T), and a pitch score (P) respectively from a R range, a T range, and a P range. At least some of the RTP scores correspond to human-determined RTP scores for predetermined tracks among a plurality of predetermined tracks. Each RTP score corresponds to a category among a plurality of categories. The computer-derived RTP scores are used to determine a category for each track among the plurality of categories. Playlists of the tracks are based on one or more of the categories.