Music Categorization via RTP Scores and Neural Networks
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Solution Overview
Problem
Current music selection systems, such as Pandora, struggle to provide meaningful recommendations due to their complexity, often recommending similar-sounding music that listeners cannot understand the reasoning behind, leading to divergent music selections unrelated to the initial artist choice.
Innovation Solution
A method for categorizing music based on rhythm, texture, and pitch (RTP) scores using low-level data analysis and mel-frequency cepstrum coefficients, which are input into a trained neural network to identify RTP scores and create playlists based on mood categories, allowing for more intuitive music organization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional music selection systems use complex identification systems with hundreds of musical attributes, then music recommendations can be generated, but the complexity increases and users cannot understand the reasoning behind recommendations
Solution Approach 1:
The patent extracts and emphasizes only the three most important musical attributes (rhythm, texture, pitch) from the complex set of hundreds of musical attributes. By taking out only the essential features, the system achieves effective music categorization without the complexity of analyzing all possible attributes, thus resolving the contradiction between recommendation capability and system complexity.
Solution Approach 2:
The patent segments the music analysis into three distinct and simple categories: rhythm, texture, and pitch. This segmentation allows the system to handle complex music analysis by breaking it down into manageable, intuitive components that can be independently analyzed and combined, reducing overall system complexity while maintaining recommendation effectiveness.
2Ease of operation
If music is organized by traditional categories such as genre, style, and artist, then users can browse music libraries, but the organization does not reflect inherent musical qualities that evoke emotions
Solution Approach 1:
The patent changes the organizational parameters from traditional metadata categories (genre, style, artist) to inherent musical qualities (rhythm, texture, pitch) that directly evoke emotions. This parameter change allows the system to organize music based on its actual sonic characteristics rather than external labels, preserving essential musical quality information while maintaining ease of operation through simple categorical organization.
3Measurement precision
If human analysts manually assign musical attributes to pieces of music, then accurate categorization can be achieved, but the process is time-consuming
Solution Approach 1:
The patent replaces the manual mechanical process of human analysts assigning musical attributes with an automated computer-based analysis system. The system automatically extracts rhythm, texture, and pitch information from music files using computational algorithms, achieving accurate categorization without the time-consuming manual analysis process, thus resolving the contradiction between measurement precision and time efficiency.
Data Source
AI summary
A method for categorizing music based on a sample set of RTP scores (rhythm, texture and pitch) for predetermined pieces of music. Some RTP scores correspond to human-determined RTP scores. Each RTP score corresponds to a category among categories. Unless an unknown piece of music was previously RTP scored based on a unique identification, low-level data is extracted from the unknown piece and analyzed to identify RTP scores based on the sample set. The identified RTP scores are then used to categorize each piece of unknown music and playlists may be created based on the categories. Each RTP score corresponds to an intensity level within the corresponding category, which may also be used in creating playlists. The low-level data may be converted to mel-frequency cepstrum coefficient (MFCC) data that is input into a trained neural network to identify the RTP scores.