Mobile-Cluster Audio Control for Echo Reduction
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Solution Overview
Problem
Existing audio systems face challenges in managing audio outputs in real-time due to improper calibrations of signal propagation and degradation, unwanted harmonics, and soundwave reflections, particularly in outdoor settings where echoes and sound variance are prevalent.
Innovation Solution
A system comprising an audio control source, clusters of computing devices with sound sensing mechanisms and wireless transceivers, and output devices that can autonomously adjust audio outputs based on sensed noise, ensuring frequencies within predetermined thresholds and providing listener-centric solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If distributed sound systems are used to reduce echo and sound variance, then audio quality is improved, but device complexity increases
Solution Approach 1:
The system divides the audio output into multiple distributed sound sources (clusters of transducers) positioned throughout the environment. Each cluster independently emits audio signals, allowing the system to cover large areas while reducing echoes and sound variance through spatial distribution rather than requiring a single complex centralized system.
Solution Approach 2:
Mobile clusters continuously sense acoustic properties of the environment and provide real-time feedback to the audio control source. This feedback loop enables dynamic adjustment of audio signals to compensate for echoes and sound variance, improving audio quality without requiring manual calibration or complex pre-configured systems.
2Reliability
If audio engineers manually manipulate sound system output to overcome crowd noise, then audio intelligibility is improved, but loss of time increases
Solution Approach 1:
The system autonomously monitors acoustic conditions and adjusts audio output without requiring manual intervention from audio engineers. Mobile clusters sense environmental noise levels and automatically adapt audio signals in real-time, eliminating the delay associated with manual manipulation while maintaining audio intelligibility.
Solution Approach 2:
Real-time acoustic sensing by mobile clusters provides continuous feedback about crowd noise and environmental conditions. The audio control source uses this feedback to dynamically adjust audio signals, enabling automatic real-time adaptation that eliminates the time loss associated with manual audio engineering interventions.
3Adaptability or versatility
If mobile clusters sense acoustic properties and autonomously adjust audio outputs, then adaptability is improved, but device complexity increases
Solution Approach 1:
Mobile clusters are designed as multi-functional units that can sense acoustic properties, communicate with the audio control source, and autonomously adjust their audio output. This universal design allows a single device type to perform multiple functions, reducing the need for separate specialized components and thereby limiting the increase in overall system complexity while maximizing adaptability.
Solution Approach 2:
Each mobile cluster independently senses its local acoustic environment and autonomously adjusts its audio output without requiring complex centralized control for every adjustment. This self-service capability distributes the adaptation intelligence across multiple simple units rather than requiring one complex control system, improving adaptability while keeping individual device complexity low.
Data Source
AI summary
The systems and methods described relate to the concept that smart devices can be used to 1) sense various types of phenomena like sound, blue light exposure, RF and microwave radiation, and 2) in real-time analyze, report and/or control outputs (e.g., displays or speakers). The systems are configurable and use standard computing devices, such as wearable electronics, tablet computers, and mobile phones to measure various frequency bands across multiple points, allowing a single user to visualize and/or adjust environmental conditions.