Acoustic DSSS Playback Signals for Adaptive Audio Scene Calibration
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Solution Overview
Problem
Existing audio device calibration and mapping methods require dedicated procedures or manual user intervention, which are cumbersome and do not adapt to changes in the acoustic environment, such as device repositioning or movement of people, posing barriers to widespread adoption.
Innovation Solution
Utilizing direct sequence spread spectrum (DSSS) signals injected into audio content playback, allowing devices to generate, detect, and process these signals to estimate acoustic scene metrics like audibility, impulse response, and device location, enabling adaptive calibration and coordination among multiple audio devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If dedicated calibration procedures or manual user intervention are used for audio device calibration, then calibration accuracy can be achieved, but the system complexity and user burden increase significantly
Solution Approach 1:
The system performs self-calibration by automatically generating DSSS signals, detecting them through microphones, and computing acoustic scene metrics without requiring manual user intervention or dedicated calibration procedures. The audio devices autonomously adapt to changes in the acoustic environment through continuous signal processing and metric estimation.
Solution Approach 2:
The system changes the operational parameters by injecting DSSS signals with specific spreading codes and frequencies into the audio playback path. By modulating these signals and analyzing their reflected characteristics, the system dynamically estimates acoustic metrics such as impulse response, audibility, and device location, replacing complex manual calibration with parameter-based automatic adaptation.
2Ease of operation
If manual calibration procedures are implemented, then initial setup can be completed, but the system cannot adapt to dynamic environmental changes
Solution Approach 1:
The system continuously performs calibration by constantly generating DSSS signals during normal audio playback and continuously processing microphone inputs to update acoustic scene metrics. This ongoing process ensures the system adapts to environmental changes such as device repositioning or movement of people without requiring repeated manual calibration procedures.
Solution Approach 2:
The system implements feedback loops where acoustic scene metrics estimated from DSSS signal analysis are used to automatically adjust audio playback parameters and device coordination. This closed-loop feedback enables real-time adaptation to environmental changes while maintaining simple operation for users.
3Extent of automation
If DSSS signals are injected into audio playback, then automated calibration is enabled, but the audio signal processing complexity increases
Solution Approach 1:
The DSSS signal injection serves multiple functions simultaneously: it enables automated calibration, provides acoustic scene metric estimation, supports device identification and coordination, and maintains compatibility with normal audio playback. This multi-functionality reduces the need for separate calibration hardware or procedures, offsetting the signal processing complexity with operational efficiency.
Solution Approach 2:
The DSSS signals act as intermediaries that carry calibration information within the existing audio playback stream. By embedding calibration data in the form of spread-spectrum signals that are imperceptible to users, the system enables automated calibration without requiring separate calibration modes or additional user-facing complexity.
4Adaptability or versatility
If acoustic scene metrics are continuously estimated, then adaptive calibration is achieved, but the computational load increases
Solution Approach 1:
The system performs partial calibration by estimating only the essential acoustic scene metrics needed for adaptive operation, such as impulse response and audibility, rather than computing all possible acoustic parameters. This selective approach enables dynamic adaptation while controlling computational energy consumption to acceptable levels.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient, automated, and responsive calibration of audio devices in dynamic environments, improving spatial playback and voice interaction quality without requiring manual intervention or repeated calibration procedures.
Implementation Method 1
causing a loudspeaker system to play back the modified audio playback signals
Implementation Method 2
detecting audio device playback sound from other orchestrated audio devices in the audio environment
Data Source
Figure 1A
Figure 1B
Figure 2
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 direct sequence spread spectrum (DSSS) signals, generating first modified audio playback signals by inserting the first DSSS 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 DSSS signals) played back by second through Nth audio devices, extracting second through Nth DSSS signals from the microphone signals and estimating at least one acoustic scene metric based, at least partly, on the second through Nth DSSS signals.