Audio Track Analysis for Category-Based Personalization
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Solution Overview
Problem
Users face difficulties in efficiently and consistently personalizing audio settings across different audio categories due to variations in audio properties, leading to a tedious and error-prone process with suboptimal listening experiences.
Innovation Solution
A method for determining audio personalization settings by selecting a representative audio track, analyzing its properties, and adjusting settings based on user input, using a system that includes audio environments, user profiles, and audio metrics to suggest optimal settings for specific categories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually adjust audio settings for each audio category, then personalization accuracy is improved, but time consumption and operational complexity increase
Solution Approach 1:
The system performs preliminary audio category classification and representative sample selection before the user needs to adjust settings. By pre-analyzing audio tracks and identifying category representatives, the system prepares personalization settings in advance, reducing the time users need to spend on manual adjustments while maintaining accuracy.
Solution Approach 2:
The system enables self-service personalization by automatically analyzing audio content, determining audio categories, selecting representative samples, and generating personalized settings without requiring manual user intervention. The user simply provides feedback on automatically generated settings, significantly reducing time consumption while maintaining personalization accuracy.
2Measurement precision
If users manually adjust audio settings for each audio category, then personalization accuracy is improved, but operational complexity increases
Solution Approach 1:
The system performs automatic audio category classification, representative sample selection, and personalization setting generation without requiring users to understand audio processing concepts. The user interface presents simple feedback options, reducing operational complexity while maintaining personalization accuracy through automated backend processing.
Solution Approach 2:
The system introduces an intermediary automated processing layer between the user and the complex audio personalization process. This intermediary handles audio analysis, category determination, and settings generation, presenting only simple feedback options to the user, thereby reducing operational complexity while preserving personalization accuracy.
3Ease of manufacture
If a specific audio track is selected as representative sample, then personalization settings can be configured, but selection difficulty and error probability increase
Solution Approach 1:
The system automatically performs audio track analysis, category classification, and representative sample selection without requiring user expertise in audio evaluation. The automated process identifies suitable representative samples based on audio properties, making settings configuration easy while eliminating selection difficulty and error probability.
Solution Approach 2:
The system replaces the manual mechanical process of selecting representative audio samples with automated computational analysis. By using audio property analysis and category classification algorithms, the system objectively identifies representative samples, eliminating the subjectivity and error-proneness of manual selection while ease of configuration.
Data Source
AI summary
Techniques for enabling personalization of audio tracks include selecting a portion of an audio track that is representative of the audio category, creating an audio sample from the portion of the audio track, playing the audio sample for a user, and adjusting, based on an input from the user while the audio sample is playing, a personalization setting for the user to be used when playing back audio from the audio category.