Music Playlist Organization Using Semantic Feature Vectors
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Solution Overview
Problem
Current music streaming services face challenges in recommending relevant music tracks due to reliance on statistical user preferences and metadata analysis, which can be incomplete or misleading, and require significant storage and computing resources for direct audio signal analysis, while indirect metadata analysis fails to capture rich semantic nuances.
Innovation Solution
A method and system that use feature vectors representing semantic characteristics of music tracks, combined with metadata and similarity matrices, to efficiently organize music into playlists based on user input, reducing storage and computational needs while improving relevance and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If direct similarities between digital audio signals are determined, then measurement precision of music track similarities is improved, but storage capacity and computing power requirements increase significantly
Solution Approach 1:
The patent extracts only the essential semantic characteristics from complete digital audio signals by analyzing feature vectors (acoustic, musical, emotional features) and comparing them against stored reference feature vectors. This extraction approach allows similarity measurement without storing or processing entire audio files, resolving the contradiction between precision and storage requirements.
Solution Approach 2:
The system performs preliminary analysis of audio signals to extract and store compact feature vectors representing semantic characteristics before actual similarity comparison. This pre-processing step enables efficient subsequent comparisons without requiring access to original audio files, reducing storage needs while maintaining measurement precision.
2Measurement precision
If direct similarities between digital audio signals are determined, then measurement precision of music track similarities is improved, but computing power requirements increase significantly
Solution Approach 1:
The patent extracts only the essential semantic characteristics from complete digital audio signals by analyzing feature vectors (acoustic, musical, emotional features) and comparing them against stored reference feature vectors. This extraction approach allows similarity measurement without storing or processing entire audio files, resolving the contradiction between precision and storage requirements.
Solution Approach 2:
The system uses compact copies (feature vectors) of the essential characteristics of audio signals instead of the original signals. These feature vector copies contain sufficient semantic information for similarity comparison while requiring minimal computational resources to store and process, resolving the contradiction between measurement precision and computing power requirements.
3Quantity of substance
If indirect similarities between metadata are determined, then storage capacity and computing power requirements are reduced, but measurement precision of music track similarities deteriorates
Solution Approach 1:
The patent creates a composite representation combining multiple types of features (acoustic features, musical features, emotional features) into a unified feature vector. This composite approach captures rich semantic characteristics beyond simple metadata while maintaining compact storage and efficient processing, resolving the contradiction between storage efficiency and similarity measurement precision.
4Quantity of substance
If feature vectors are used for similarity analysis, then storage capacity requirements are reduced, but the ability to capture context-specific nuances (special occasions, religious context) deteriorates
Solution Approach 1:
The patent creates a composite representation combining multiple types of features (acoustic features, musical features, emotional features) into a unified feature vector. This composite approach captures rich semantic characteristics beyond simple metadata while maintaining compact storage and efficient processing, resolving the contradiction between storage efficiency and similarity measurement precision.
Data Source
AI summary
A method, device and system for organizing media content on a computer-based system to form a playlist, wherein the device has access to a database with a plurality of music tracks and associated feature vectors including feature values representing different semantic characteristics of a music track, as well as metadata including at least one type of metadata record representing associated information about the respective music track. The playlist is determined based on a query from the client device that includes an input vector, and at least one input metadata record, using an additional similarity matrix representing a measure of similarity between different metadata records of the same type.