Music Emotion Recognition via Vocal Separation and Deep Learning
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Solution Overview
Problem
Current music feeling characterization relies heavily on manual methods, which are expensive and inefficient, especially when dealing with lengthy music files and complex sound structures, limiting their ability to accurately classify music emotions and meet the demands of various fields and users.
Innovation Solution
A music recommendation system utilizing deep learning with fine-grained division and vocal separation techniques preprocesses sound data to improve processing speed and precision, incorporating an 'equivocal sparse attention' network to reduce the impact of irrelevant data and enhance emotion recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual checking is used for music feeling characterization, then accuracy can be maintained, but cost becomes excessively high and efficiency decreases
Solution Approach 1:
The patent replaces manual mechanical checking with an automated deep learning system that uses convolutional neural networks (CNNs) to process music audio features. The system automatically extracts temporal, spectral, and chromatic features and classifies music feelings without human intervention, achieving both high accuracy and efficiency.
Solution Approach 2:
The system enables self-service by allowing the deep learning model to automatically learn and classify music feelings from training data. The model performs self-training and self-evaluation, eliminating the need for continuous manual annotation while maintaining recognition accuracy.
2Reliability
If lengthy music files are processed without division, then complete emotion context is preserved, but processing time increases and overfitting risk increases
Solution Approach 1:
The patent divides lengthy music files into smaller segments or clips before processing. Each segment is independently analyzed by the deep learning model to extract local emotion features, which are then aggregated to represent the overall music feeling. This segmentation reduces processing time and prevents overfitting while maintaining contextual accuracy through proper feature integration.
3Measurement precision
If complex sound structures are analyzed in detail, then recognition precision improves, but computational complexity and processing cost increase
Solution Approach 1:
The patent applies local quality by using different feature extraction strategies for different sound elements. The system selectively extracts temporal features for rhythmic analysis, spectral features for timbre identification, and chromatic features for harmonic analysis, rather than uniformly processing all sound elements with the same complexity. This reduces overall computational complexity while maintaining precision where needed.
Data Source
AI summary
The system comprises an input device for collecting sound and sound information or extracting sound information from a music sample; a pre-processor for pre-processing the informational collection to generate an input information test set for a characterization model, wherein the pre-processor utilizes fine-grained division and different techniques to preprocess the example informational collection; a central processor for combining sound feeling data and further developing arrangement speed, such that review makes fine-grained division for genuine music informational collection and results the inclination results by casting a ballot direction, which is configured to promote precision of music feeling grouping; a vocal division device for dividing vocal of the complicated structure of genuine music sound, and voice and foundation sound are incorporated together; and a reviewing device for reviewing the vocal detachment of music and reviewing the grouping impact of vocal and foundation sound individually, which incredibly builds the convergence of sound elements.