Pervasive Acoustic Mapping with DSSS Self-Calibration
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Solution Overview
Problem
Existing audio systems lack efficient methods for estimating acoustic scene metrics and calibrating smart audio devices in dynamic environments, requiring manual or dedicated calibration procedures that hinder widespread adoption and fail to adapt to changes in the acoustic scene.
Innovation Solution
An orchestrated system of smart audio devices uses direct sequence spread spectrum (DSSS) signals injected into audio content for automated acoustic mapping, enabling self-calibration and adaptation to changes in the audio environment, including geometric mapping and audibility mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual or dedicated calibration procedures are used for audio devices, then calibration accuracy can be achieved, but device complexity and ease of operation deteriorate due to requiring user intervention and dedicated procedures
Solution Approach 1:
The system performs automatic acoustic mapping and calibration without user intervention. Audio devices autonomously emit calibration signals, receive responses from other devices, and compute their positions and acoustic characteristics automatically, eliminating the need for manual calibration procedures
Solution Approach 2:
The system performs preliminary acoustic mapping by having devices emit calibration signals and measure acoustic responses before actual audio playback. This preliminary calibration phase establishes the acoustic model that enables subsequent automatic adaptation without requiring user intervention
2Measurement precision
If existing acoustic estimation methods are used, then some acoustic metrics can be obtained, but adaptability to dynamic environmental changes deteriorates due to lack of continuous monitoring
Solution Approach 1:
The system continuously performs acoustic mapping by repeatedly emitting calibration signals and measuring responses. This continuous monitoring enables the system to detect and adapt to environmental changes such as moving objects, changing acoustic conditions, or device repositioning in real-time
Solution Approach 2:
The system uses feedback from acoustic measurements to continuously update the acoustic model. Devices measure the responses to their calibration signals, compute acoustic metrics, and use this feedback to adapt their operation and re-calibrate when environmental changes are detected
3Ease of operation
If automated calibration using calibration signals is implemented, then ease of operation improves, but device complexity increases due to signal injection and processing requirements
Solution Approach 1:
The system uses universal calibration signals that can be emitted by any audio device and received by any other device in the network. The same signal type and processing methodology is used across all devices, simplifying implementation despite the automated functionality
Solution Approach 2:
The system uses acoustic signals as intermediaries to convey position and calibration information between devices. Rather than requiring direct device-to-device communication protocols, devices exchange positional and calibration data through the acoustic medium, simplifying the interaction model
4Measurement precision
If calibration signals are injected into audio content, then calibration effectiveness improves, but loss of information increases due to potential interference with audio quality
Solution Approach 1:
The system extracts calibration signals from the audio content stream before playback, processes them separately for calibration purposes, and removes them after the calibration measurements are completed. This extraction approach allows calibration without permanently altering the audio content
Solution Approach 2:
The system injects calibration signals periodically or in specific time windows within the audio content rather than continuously. This periodic injection allows calibration measurements to be taken at intervals while minimizing interference with the overall audio experience
Data Source
AI summary
Some methods may involve receiving a first content stream that includes first audio signals, rendering the first audio signals to produce first audio playback signals, generating first calibration signals, generating first modified audio playback signals by inserting the first calibration signals into the first audio playback signals, and causing a loudspeaker system to play back the first modified audio playback signals, to generate first audio device playback sound. The method(s) may involve receiving microphone signals corresponding to at least the first audio device playback sound and to second through Nth audio device playback sound corresponding to second through Nth modified audio playback signals (including second through Nth calibration signals) played back by second through Nth audio devices, extracting second through Nth calibration signals from the microphone signals and estimating at least one acoustic scene metric based, at least partly, on the second through Nth calibration signals.