Microphone Signal Equalization via Automated Filter Calibration
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Solution Overview
Problem
Existing filter calibration procedures for microphones require manual triggering and are not suitable for live sound or spatial audio mixing, especially when background noise is significant, and they struggle to accurately match the long-term spectrum of audio signals captured by microphones of different types and locations.
Innovation Solution
An automated method that analyzes signals from multiple microphones, determines quality measures, and applies a filter to equalize the long-term spectrum by calculating a cross-correlation measure and aggregating data from multiple time windows to design an equalization filter that matches the frequency response of closer and farther microphones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual filter calibration procedures are used to match the long term spectrum of audio signals captured by first and second microphones, then the spectrum matching accuracy is improved, but the complexity of operation increases and the time required for calibration increases
Solution Approach 1:
The system performs automatic filter calibration by analyzing audio signals captured by multiple microphones and computing equalization filters without manual intervention. The processor automatically determines frequency responses, calculates spectral differences, and adjusts filters to match the long term spectrum between first and second microphones, eliminating the need for operator-triggered calibration procedures while maintaining high spectrum matching accuracy
Solution Approach 2:
The system dynamically adjusts filter parameters based on real-time analysis of audio signal characteristics. By continuously monitoring frequency responses and spectral differences between microphones, the system modifies equalization filter parameters to optimize spectrum matching, enabling adaptive calibration that responds to changing acoustic conditions without manual reconfiguration
2Measurement precision
If manual filter calibration procedures are used to match the long term spectrum of audio signals captured by first and second microphones, then the spectrum matching accuracy is improved, but the calibration time increases
Solution Approach 1:
The system performs continuous automatic filter calibration by continuously analyzing audio signals from multiple microphones and adjusting equalization filters in real-time. Rather than requiring discrete manual calibration sessions, the system maintains ongoing spectrum matching through continuous signal processing and adaptive filter adjustment, eliminating calibration downtime and enabling live sound applications
Solution Approach 2:
The system replaces manual mechanical calibration operations with automated digital signal processing. The processor automatically computes frequency responses, determines spectral differences, and adjusts filter parameters through algorithmic analysis of audio signals, substituting the manual mechanical process with an automated electronic system that operates continuously without operator intervention
3Adaptability or versatility
If multiple microphones are used to capture audio signals from multiple sound sources, then the spatial audio mixing capability is improved, but the difficulty of filter calibration increases
Solution Approach 1:
The system segments the calibration process by individually analyzing audio signals from each first microphone associated with different sound sources. The processor computes separate equalization filters for each microphone-sound source pair by analyzing their respective frequency responses and spectral characteristics, then applies these individualized filters to maintain accurate spectrum matching across multiple sound sources in spatial audio mixing
Solution Approach 2:
The system implements a universal automatic calibration algorithm that handles multiple sound sources and microphone configurations through a single integrated process. The processor applies the same spectral analysis and filter computation methodology across all first and second microphone pairs, enabling the system to adapt to various spatial audio mixing scenarios without requiring source-specific manual calibration procedures
4Measurement precision
If filter calibration is performed in post-production settings with manual triggering, then the spectrum matching accuracy is improved, but the applicability to live sound decreases
Solution Approach 1:
The system implements dynamic automatic filter calibration that adapts to changing acoustic conditions in real-time. The processor continuously analyzes audio signals from multiple microphones and adjusts equalization filters dynamically during live sound events, enabling the system to maintain high spectrum matching accuracy in dynamic live environments rather than static post-production settings
Data Source
AI summary
A method, apparatus and computer program product provide an improved filter calibration procedure to reliably equalize the long term spectrum of the audio signals captured by first and second microphones that are at different locations relative to a sound source and/or are of different types. In the context of a method, the signals captured by the first and second microphones are analyzed. The method also determines one or more quality measures based on the analysis. In an instance in which one or more quality measure satisfy a predefined condition, the method determines a frequency response of the signals captured by the first and second microphones. The method also determines a difference between the frequency response of the signals captured by the first and second microphones and processes the signals captured by the first microphone for filtering relative to the signals captured by the second microphone based upon the difference.