Venue Speaker Placement Using Image-Based 3D Acoustic Modeling
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Solution Overview
Problem
Conventional sound engineering software lacks the ability to generate a 2D or 3D model of a venue without physical measurements, requiring tedious manual trial and error for speaker placement optimization.
Innovation Solution
A computer-implemented method using machine learning models to generate 2D or 3D representations of a venue from images, automatically optimizing speaker parameters such as number, position, and configuration based on these models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physical measurements of the venue are taken manually from blueprints or on-site, then accurate venue dimensions are obtained, but the process becomes time-consuming
Solution Approach 1:
The patent uses photogrammetry to create a scaled 2D planar representation and 3D model of the venue by processing photographs. This creates a digital copy of the physical venue that can be measured and analyzed without physical measurement, resolving the contradiction between measurement accuracy and time consumption
Solution Approach 2:
The patent replaces manual mechanical measurement processes with automated image processing and machine learning algorithms. The system automatically extracts venue dimensions and spatial relationships from photographs, eliminating the need for manual measuring tools and procedures
2Manufacturing precision
If manual trial and error approach is used to optimize speaker configuration, then speaker parameters can be adjusted, but the process becomes tedious and time-consuming
Solution Approach 1:
The patent implements an automated system that performs speaker configuration optimization without requiring manual trial and error by the sound engineer. The machine learning model automatically analyzes the venue model and determines optimal speaker parameters, making the system self-sufficient for the optimization task
Solution Approach 2:
The patent uses machine learning to automatically adjust and optimize speaker parameters such as position, orientation, and configuration based on the venue geometry and acoustic requirements. This automated parameter optimization replaces manual adjustment and finds optimal configurations efficiently
3Ease of operation
If conventional sound engineering software is used, then speaker configuration can be analyzed, but the software lacks automated venue modeling and speaker placement functionality
Solution Approach 1:
The patent integrates multiple functions into a unified system: photogrammetric venue modeling, automatic 3D reconstruction, and AI-based speaker placement optimization. This multi-functional system replaces the need for separate manual processes and tools, enhancing ease of operation while maintaining comprehensive functionality
Data Source
AI summary
Various embodiments set forth techniques for positioning speakers within a venue. The techniques include generating, via a machine learning model, at least one of a two-dimensional (2D) representation or a three-dimensional (3D) representation of a venue based on one or more images of the venue. The techniques further include determining one or more parameters associated with one or more speakers to be placed within the venue based on the at least one of the 2D representation or the 3D representation.