Playlist Generation Using N-Dimensional Audio Embeddings
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Solution Overview
Problem
Current music services lack an efficient method to generate playlists based on input acoustic information, such as environmental sounds or recognized audio tracks, which limits user experience in creating personalized audio collections.
Innovation Solution
The system generates playlists by receiving acoustic information, obtaining seed information, and identifying audio tracks in a library using constructs in an N-dimensional space, where audio tracks are embedded based on features and metadata, allowing for similarity-based playlist creation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If music services use traditional playlist generation methods (random or predefined grouping), then the implementation is simple, but the personalization and user experience are limited
Solution Approach 1:
The patent embeds audio tracks and acoustic information into an N-dimensional space where each dimension represents a specific acoustic feature (tempo, energy, danceability, etc.). This dimensional transformation enables similarity-based playlist generation by comparing positional distances in the multi-dimensional space, achieving personalization without requiring complex rule-based systems.
Solution Approach 2:
The patent introduces an embedding model as an intermediary that transforms raw acoustic features and metadata into compact vector representations. This embedding layer mediates between the input acoustic information and the playlist generation process, enabling efficient similarity comparison while maintaining system manageability.
2Productivity
If the system processes acoustic information in real-time to generate playlists, then the user experience is enhanced, but the processing time and computational resources increase
Solution Approach 1:
The patent pre-computes embedding vectors for all audio tracks in the library and stores them in the N-dimensional space. When a user provides acoustic information, the system only needs to embed the query and perform distance calculations against pre-computed vectors, significantly reducing real-time processing requirements while maintaining personalization quality.
3Measurement precision
If the system uses detailed acoustic features and metadata for track identification, then the playlist accuracy improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The patent extracts only the most relevant acoustic features (tempo, energy, danceability, valence, etc.) and metadata elements needed for playlist generation, discarding redundant information. This selective extraction maintains identification accuracy while reducing computational complexity and processing requirements.
Solution Approach 2:
The patent transforms multiple acoustic parameters and metadata fields into a unified embedding vector representation. This parameter transformation consolidates diverse data types into a consistent format that simplifies comparison and reduces processing complexity while preserving the essential information needed for accurate track identification.
Data Source
AI summary
Techniques for generating a playlist include: receiving acoustic information, obtaining seed information based on the acoustic information, identifying audio tracks in an audio library based on the seed information, and generating the playlist using at least some of the identified audio tracks.