Dual Microphone Audio Source Identification for Audience Measurement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In microphone-based audience measurement environments, accurately determining the source of audio signals is challenging due to interference from ambient noise and other sources, leading to erroneous data and false matches in audience measurement systems.
Innovation Solution
The use of a metering device with two microphones separated by a fixed distance, employing an adaptive least mean square algorithm and finite impulse response (FIR) filter to generate weighted coefficients and compare audio signals, determining the state of a media presentation device by analyzing similarity values and controlling the metering device accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single microphone is used to capture audio signals, then the device complexity is low, but the measurement precision deteriorates due to inability to distinguish audio source
Solution Approach 1:
The audio capture function is segmented across multiple microphones (first and second microphones) positioned at different locations. Each microphone captures audio signals independently, and the processor segments the analysis by comparing signals from different microphones to determine if they originate from the same source, thereby improving measurement precision through spatial segmentation.
2Reliability
If audio signals are captured without source verification, then the productivity is high, but the reliability deteriorates due to false matches from ambient noise
Solution Approach 1:
The system performs preliminary source verification by comparing audio signals from multiple microphones before proceeding with audience measurement. The processor determines whether audio signals originate from the same source as media content before recording measurement data, preventing false matches from ambient noise while maintaining efficient processing through pre-validation.
Solution Approach 2:
The system uses feedback by comparing audio signals captured by multiple microphones against each other and against media content audio. The processor continuously monitors signal similarity and uses this feedback to verify source authenticity, ensuring that only verified audio sources contribute to measurement data, thereby improving reliability without significantly impacting productivity.
3Loss of information
If the metering device monitors all audio signals, then the quantity of measured data increases, but the loss of information increases due to inability to distinguish relevant from irrelevant sources
Solution Approach 1:
The system extracts only the relevant audio signals by comparing signals from multiple microphones and identifying those that originate from the same source as the media content. The processor extracts and records only the audio source identification results that are relevant to the media being played, filtering out ambient noise and irrelevant sources, thereby reducing information loss while maintaining appropriate data volume.
Data Source
AI summary
Methods and apparatus to determine a state of a media presentation device are disclosed. Example disclosed methods include generating a first set of weighted coefficients based on first audio received by first and second microphones at a first time. Example disclosed methods include generating a second set of weighted coefficients based on second audio received by the first and second microphones at a second time after the first time. Example disclosed methods include comparing the first set of coefficients and the second set of coefficients to generate a similarity value. Example disclosed methods include, when the similarity value satisfies a threshold, determining that the media presentation device is in a first state. Example disclosed methods include, when the similarity value does not satisfy the threshold, determining that the media presentation device is in a second state.