Feature-Based Music Selection System for Accurate Track Matching
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Solution Overview
Problem
Digital music services face challenges in efficiently selecting music for users due to the vastness of options, leading to user inconvenience, as traditional methods rely on co-presence rather than intrinsic music features.
Innovation Solution
A music-retrieval system that selects music compositions based on the initial user input by identifying and matching musical and lyrical features, using a computer-implemented method involving input, feature identification, comparison, and selection modules to create a music station with similar tracks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional co-presence based music selection is used, then the system is simple to implement, but the music selection accuracy and relevance to user preferences deteriorates
Solution Approach 1:
The patent segments music compositions into multiple independent feature dimensions including musical features (tempo, key, instrumentation) and lyrical features (themes, emotions, topics). This segmentation allows the system to analyze and compare specific feature aspects rather than treating music as a monolithic entity, thereby improving selection accuracy while managing complexity through modular feature processing.
Solution Approach 2:
The patent transforms the music selection problem from co-presence based ranking to feature-based parameter matching. By changing the selection parameters from contextual co-presence to intrinsic musical and lyrical features, the system achieves more accurate and relevant music recommendations that directly reflect user preferences for specific music characteristics.
2Adaptability or versatility
If feature-based music selection is implemented, then personalized music recommendations improve, but the computational complexity and processing time increases
Solution Approach 1:
By dividing music analysis into separate musical feature extraction and lyrical feature extraction modules, the system can process different feature types independently and in parallel. This segmentation enables personalized recommendations through comprehensive feature matching while managing computational complexity through modular, reusable processing components.
Solution Approach 2:
The system performs preliminary action by pre-computing and storing musical features and lyrical features for music compositions in advance. This allows the recommendation engine to quickly retrieve and compare pre-analyzed features when generating personalized recommendations, reducing real-time computational complexity while maintaining high adaptability to user preferences.
3Reliability
If comprehensive feature analysis is performed on all music compositions, then music selection quality improves, but the processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by analyzing only the most relevant musical and lyrical features for each music composition rather than performing exhaustive analysis of all possible attributes. This selective feature analysis maintains high music selection quality by focusing on discriminative features while reducing processing time and computational resource requirements.
Solution Approach 2:
The system performs preliminary feature extraction and analysis during music ingestion and storage, so that when music selection is needed, the comprehensive feature analysis has already been completed. This shifts the computational burden to off-peak times and enables rapid music recommendation generation when users request personalized playlists.
Data Source
AI summary
Systems and methods for feature-based music selection may include (1) receiving user input selecting a music composition, (2) identifying features of the music composition including (i) a musical feature, relating to a musical quality of the music composition and (ii) a lyrical feature, relating to one or more of the music composition's lyrics, (3) determining that an additional music composition is similar to the music composition based on a comparison of the features of the music composition with features of the additional music composition, and (4) selecting the additional music composition to be added to a queue associated with the music composition based on the determination. Various other methods, systems, and computer-readable media are also disclosed.