Speaker Frequency Response Filtering for User Audio Profiles
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Solution Overview
Problem
Users have different preferences for audio characteristics such as volume and sound modes when experiencing media content, which existing systems fail to customize effectively, resulting in a one-size-fits-all listening experience.
Innovation Solution
A method and system that involve 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 customized audio settings, along with aggregating volume information to adjust playback accordingly.
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 content playback. When a user selects an audio profile, the corresponding pre-computed filters are applied to the content's audio track, enabling rapid customization without real-time complex processing. This resolves the contradiction by preparing customization data in advance, making user-specific audio processing both adaptable and computationally efficient.
2Manufacturing precision
If multiple filters are created and applied to customize audio output, then user audio profile precision is improved, but processing time increases
Solution Approach 1:
Filter transformations are pre-computed and stored in a database during system initialization or profile setup, rather than being created in real-time during content playback. When a user selects an audio profile, the system retrieves the pre-computed filters and applies them immediately to the audio track, maintaining high precision while minimizing processing time during actual use.
Solution Approach 2:
The system creates copies of filter transformations for different audio profiles and stores them for rapid retrieval. Instead of regenerating filters during playback, pre-computed filter copies are applied to matched audio tracks, preserving precision while significantly reducing the time penalty associated with filter creation and application.
3Measurement precision
If aggregate volume statistics are continuously monitored and applied, then audio playback accuracy is improved, but system resource consumption increases
Solution Approach 1:
Aggregate volume statistics are pre-computed and stored with the audio content metadata before playback. During content playback, the system retrieves these pre-calculated volume statistics and applies them directly without performing continuous real-time analysis, thereby maintaining accurate volume control while significantly reducing processing energy consumption during actual content delivery.
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.