Loudspeaker Placement Identification Using Listener Vocalization
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Solution Overview
Problem
Existing loudspeaker placement methods often deviate from recommended positions due to room design constraints and imperfect user setups, leading to suboptimal sound quality and varying listening experiences for different listener locations.
Innovation Solution
A method utilizing human directivity index (DI) pattern data, collected through vocalizations recorded by microphones at each loudspeaker, to determine optimal loudspeaker placement relative to a listener. This involves extracting DI features, processing them with machine-learning models to estimate loudspeaker distances and angles, and applying spatial corrections to enhance sound quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If loudspeakers are placed according to recommended positions, then sound quality is optimized, but room design constraints and user setup imperfections cause deviations from ideal placement
Solution Approach 1:
The system uses the listener's vocalization as feedback to measure actual loudspeaker placement. The microphone records the vocalization from each loudspeaker's position, and the machine learning model compares this feedback against expected patterns to determine placement accuracy, enabling continuous adjustment toward optimal positioning.
Solution Approach 2:
The system performs self-calibration by using the listener's own voice as the test signal. The listener's vocalization automatically serves as the reference for measuring loudspeaker placement, eliminating the need for external test equipment or complex calibration procedures.
2Reliability
If loudspeaker placement is fixed according to standards, then sound quality is consistent for ideal listener positions, but listening experience varies for different listener locations
Solution Approach 1:
The system transitions from static loudspeaker placement to dynamic adaptation. By using the listener's vocalization to determine placement and applying spatial corrections based on measured positions and orientations, the system dynamically adjusts to accommodate different listener locations while maintaining consistent sound quality.
Solution Approach 2:
The system changes the parameters of loudspeaker output based on measured placement and listener position. Spatial corrections are applied to adjust volume levels and timing of each loudspeaker, transforming the static audio output into a dynamic system that adapts to varying listener locations.
3Ease of operation
If manual loudspeaker placement adjustment is used, then setup is simple, but placement accuracy deviates from recommended values
Solution Approach 1:
The system replaces manual mechanical adjustment with an automated acoustic measurement and machine learning-based determination system. Instead of physically adjusting loudspeaker positions based on manual measurement, the system uses vocalization recording and ML model analysis to automatically determine optimal placement, achieving higher precision without complex manual procedures.
Data Source
AI summary
In one embodiment, a method includes determining human directivity index (DI) pattern data corresponding to a location of a listener, based on vocalization recorded by a microphone at each of a plurality of loudspeakers. The method further includes extracting a set of DI features from the DI pattern data; providing the set of DI features to a trained machine-learning model; and determining, by the trained machine-learning model and based on the set of DI features, a placement of each of the plurality of loudspeakers relative to the listener.