Audio Playlist Generation Using Feature Vector Proximity
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Solution Overview
Problem
Existing music services lack effective methods for generating playlists that accurately reflect user preferences by identifying similar audio tracks based on auditory features and metadata, leading to suboptimal music recommendations.
Innovation Solution
A system that designates a seed track and represents audio tracks as constructs derived from frequency representations and metadata, using cosine similarity measurements and machine-learning techniques to embed tracks in an N-dimensional space, allowing for the identification and selection of similar tracks based on their proximity in this space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If audio tracks are grouped randomly or with simple predefined preferences, then playlist generation is simple and fast, but the accuracy of music recommendations deteriorates
Solution Approach 1:
The patent transforms audio tracks into numerical feature vectors containing multiple parameters (tempo, key, energy, danceability, acousticness, instrumentalness, liveness, valence). This parameter transformation enables precise mathematical comparison between tracks using distance metrics, directly improving recommendation accuracy while maintaining computational efficiency through standardized feature representations.
Solution Approach 2:
The patent replaces manual or simple rule-based playlist generation with an automated computational system. The system automatically computes distance metrics between audio feature vectors and selects tracks based on mathematical optimization, substituting mechanical/manual processes with algorithmic automation that improves accuracy without proportionally increasing operational complexity.
2Measurement precision
If audio tracks are grouped based on detailed auditory features and metadata, then the accuracy of music recommendations is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary extraction and normalization of audio features from tracks before playlist generation. By pre-computing feature vectors including tempo, key, energy, and other characteristics, the system prepares data in advance for rapid comparison during playlist creation, reducing real-time processing requirements while maintaining high recommendation accuracy.
Solution Approach 2:
The patent converts complex audio characteristics into standardized numerical parameters with consistent scales. This parameter standardization enables efficient distance calculations using simple mathematical operations rather than complex audio analysis during playlist generation, significantly reducing processing time while preserving detailed auditory feature information for accurate recommendations.
3Adaptability or versatility
If a large number of audio tracks are analyzed to generate playlists, then the personalization and relevance of recommendations are improved, but the computational resources and processing time required increase
Solution Approach 1:
The patent extracts only the most relevant audio features from complete track analysis, selecting key parameters such as tempo, key, energy, danceability, acousticness, instrumentalness, liveness, and valence. This selective extraction obtains sufficient information for personalization without processing all possible audio characteristics, reducing computational resource requirements while maintaining high recommendation relevance.
Solution Approach 2:
The patent transforms audio tracks into compact feature vectors that condense complex auditory information into manageable numerical parameters. This parameter compression enables efficient storage and comparison of many tracks using minimal computational resources, allowing comprehensive analysis of large track libraries without proportionally increasing energy consumption or processing requirements.
Data Source
AI summary
Generating a playlist may include designating a seed track in an audio library; identifying audio tracks in the audio library having constructs that are within a range of a corresponding construct of the seed track, where the constructs for the audio tracks are derived from frequency representations of the audio tracks, and the corresponding construct for the seed track is derived from a frequency representation of the seed track; and generating the playlist using at least some of the audio tracks that were identified.