Speaker Audio Profile Filtering for Personalized Media Playback
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Solution Overview
Problem
Users have different preferences for audio characteristics such as volume and sound modes when experiencing media content, but existing systems lack the ability to customize these settings effectively for individual users in home entertainment systems.
Innovation Solution
A method and system that involves playing test signals on multiple speakers to record their frequency responses, creating filters based on user-selected audio profiles, and applying these filters to achieve the desired audio experience, while also aggregating volume information to enhance playback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If default audio track is used as established by content provider, then audio playback is simple and quick, but user listening preferences cannot be customized
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing filter transformations for various audio profiles before actual playback. When a user selects an audio profile, the pre-computed filters are applied to the content's audio track, enabling quick customization without real-time complex processing. This resolves the contradiction by preparing customization options in advance, making user-specific audio adaptation feasible without adding significant processing complexity during playback.
2Manufacturing precision
If multiple filters are applied to match user audio profile, then audio customization precision is improved, but processing time and computational resources increase
Solution Approach 1:
Filter transformations are pre-computed and stored for different audio profiles and content types. When playback occurs, the system simply applies the pre-prepared filters rather than computing them in real-time, significantly reducing processing time while maintaining high precision in matching user audio profiles to content characteristics.
Solution Approach 2:
The system applies filtering selectively based on content type and user profile, rather than applying all possible filters to all content. This partial action approach maintains audio fidelity where needed while avoiding unnecessary processing, thus balancing precision with processing efficiency.
3Reliability
If aggregate volume statistics are collected and applied, then volume consistency across content is improved, but user privacy and data processing requirements increase
Solution Approach 1:
The system implements feedback by collecting anonymous volume adjustment data from multiple users and using this aggregate information to pre-adjust volume levels for different content types. This feedback loop improves volume consistency across the service while preserving individual user privacy, as only aggregated statistical patterns are utilized rather than individual viewing habits.
Data Source
AI summary
Disclosed herein are system, method, and tangible computer readable medium for creating a desired audio effect for a user. The method includes operations including: causing a plurality of speakers to play test signals, each test signal being specific to one of the speakers; receiving from a remote device recorded frequency responses of the speakers resulting from the playing of the test signals; creating one or more filters to match an audio profile selected by a user; applying the filters to the recorded frequency responses to obtain filtered transformations of the speakers; and transmitting the filtered transformations to the speakers; wherein the filtered transformations are applied at the speakers to thereby achieve the user audio profile.