Deep Learning Audio Processing for Personalized Sound Quality
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Solution Overview
Problem
Current sound quality adjustment methods are inadequate as they lack automatic and personalized adjustments, struggle with unknown music types, rely on manual evaluation, and fail to quantify subjective user preferences effectively.
Innovation Solution
A deep learning-based method that processes sound quality characteristics by extracting features from user preference data to generate personalized sound quality adjustments using a neural network model trained on behavioral and audio data, enabling online learning and adaptation to user feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual evaluation and empirical data methods are used for sound quality adjustment, then existing music types can be processed with known sound settings, but the method cannot be applied to music with multiple types or unknown types and lacks personalization
Solution Approach 1:
The system enables automatic sound quality adjustment by having the model learn directly from user feedback data without requiring manual annotation of music styles or parameters. The baseline model processes audio waveforms and user feedback to automatically generate personalized sound quality adjustments, eliminating the need for manual evaluation and making the system adaptable to unknown music types.
2Extent of automation
If user feedback data is collected and processed online, then personalized sound quality adjustment can be achieved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the sound quality adjustment process into distinct functional modules: a baseline model for processing audio waveforms and extracting features, an online learning module for processing user feedback data, and a sound quality adjustment module for generating final adjustments. This segmentation allows each module to specialize in specific tasks, reducing overall system complexity while enabling personalized automatic adjustment through coordinated operation of these modular components.
Data Source
AI summary
The present invention provides a deep learning based method and system for processing sound quality characteristics. The method comprises: obtaining data characteristics of an audio data to be processed by extracting features from user preference data including the audio data to be processed; based on the data characteristics, generating a sound quality processing result of the audio to be processed by using a trained baseline model; wherein the baseline model is a neural network model trained by using audio data behavioral data, and other relevant data from multiple users or a single user.