Mood Labeling via Musically-Coherent Segments
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Solution Overview
Problem
Current automated mood labeling techniques for musical compositions are inconsistent and unreliable due to their reliance on arbitrary snippets of music without consideration for musical structure, leading to flawed mood classification results.
Innovation Solution
The development of a computer-implemented method that segments musical compositions into musically-coherent segments and employs a mood classifier trained using mood-labeled segments, rather than arbitrary snippets, to assign accurate mood labels, improving consistency and reliability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If arbitrary snippets of music are used for mood labeling, then the labeling process is simple and fast, but the accuracy and reliability of mood classification deteriorates
Solution Approach 1:
The musical composition is segmented into musically-coherent segments based on musical structure (e.g., intro, verse, chorus, bridge, outro) rather than using arbitrary snippets. This segmentation allows the mood classifier to analyze meaningful musical units that maintain consistent moods, thereby improving classification accuracy while maintaining computational efficiency through targeted analysis of structurally-defined portions.
2Reliability
If musically-coherent segments are used for mood labeling, then the reliability of mood classification improves, but the complexity of the labeling system increases
Solution Approach 1:
The system performs preliminary segmentation of the musical composition into musically-coherent segments before applying the mood classifier. By pre-identifying structurally meaningful segments (intro, verse, chorus, etc.), the system prepares the data in an optimal format for classification, ensuring that each segment analyzed by the classifier has consistent musical characteristics and mood, thereby improving reliability without requiring complex real-time analysis.
3Measurement precision
If the entire musical composition is analyzed for mood labeling, then comprehensive mood assessment is achieved, but the processing time and computational resources increase
Solution Approach 1:
Instead of analyzing the entire musical composition as a single unit, the system divides it into smaller musically-coherent segments. This segmentation enables the mood classifier to process each segment independently and efficiently, reducing the computational burden and processing time while maintaining comprehensive mood assessment across the entire composition through aggregation of segment-level classifications.
Data Source
AI summary
Computer-based systems, devices, and methods for assigning mood labels to musical compositions are described. A mood classifier is trained based on mood-labeled musically-coherent segments of musical compositions and subsequently applied to automatically assign mood labels to musically-coherent segments of musical compositions. In both cases, the musically-coherent segments are generated using automated segmentation algorithms.