Multi-Device Audio Calibration via Spatial Acoustic Sampling
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Solution Overview
Problem
Existing media playback systems face challenges in accurately calibrating audio playback across multiple devices in diverse environments, leading to inconsistencies in sound quality due to varying acoustic conditions.
Innovation Solution
The implementation of a calibration method using multiple recording devices that detect and analyze calibration sounds emitted by playback devices, allowing for space-averaged calibration by moving microphones through the environment to capture acoustic variability, and normalizing responses from different microphones to determine a calibration that offsets environmental acoustic characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single recording device is used for calibration, then the calibration process is simple, but the accuracy of acoustic measurement is insufficient to capture spatial variability
Solution Approach 1:
The calibration system is segmented into multiple recording devices distributed throughout the environment, each capturing acoustic characteristics from different positions. This segmentation allows the system to measure spatial variability in acoustic conditions while maintaining manageable complexity at each individual device node.
Solution Approach 2:
Multiple recording devices are merged into a unified calibration system where their individual measurements are combined to create a comprehensive acoustic profile. The processor integrates data from all recording devices to generate an overall calibration that accounts for spatial acoustic variations.
2Measurement precision
If multiple recording devices are used for calibration, then measurement accuracy improves, but the complexity of processing and normalizing responses increases
Solution Approach 1:
The system implements feedback through the processor that receives responses from multiple recording devices, analyzes them, and generates normalization adjustments. This feedback loop enables the system to automatically compensate for spatial acoustic variations by comparing measurements across different positions and applying corrective calibration factors.
Solution Approach 2:
The processor changes parameters by normalizing the responses from different recording devices based on their spatial positions and acoustic environments. This parameter transformation converts raw measurements into calibrated values that account for spatial variability, enabling accurate multi-device calibration.
3Measurement precision
If microphones are moved through the environment during calibration, then spatial acoustic variability is captured, but the calibration time increases
Solution Approach 1:
The system performs preliminary action by pre-positioning multiple recording devices at strategic locations throughout the environment before calibration begins. This preliminary setup eliminates the need to move microphones during calibration, as all measurement points are already in place, thereby capturing spatial acoustic variability without extending calibration time.
Solution Approach 2:
The calibration process uses periodic action by having the playback device emit calibration sounds at regular intervals. This periodic emission allows multiple stationary recording devices to capture acoustic characteristics simultaneously over time, efficiently covering spatial variability without requiring physical movement of microphones.
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
This approach ensures consistent and high-quality audio playback across multiple devices by accurately accounting for environmental acoustics, improving sound fidelity and user experience in various settings.
Implementation Method 1
a playback device of a media playback system begins output of a calibration sound
Implementation Method 2
detecting, via a microphone, at least a portion of one or more calibration sounds
Data Source
AI summary
Example techniques may involve calibration with multiple recording devices. An implementation may include a mobile device receiving data indicating that a calibration sequence for multiple playback devices has been initiated in a venue. The mobile device displays a prompt to include the first mobile device in the calibration sequence for the multiple playback devices and a particular selectable control that, when selected, includes the first mobile device in the calibration sequence. During the calibration sequence, the mobile device records calibration audio as played back by the multiple playback devices and transmits data representing the recorded calibration audio to a computing device. The computing device determines a calibration for the multiple playback devices in the venue based on the data representing the calibration audio recorded by the first mobile device and data representing calibration audio recorded by second mobile devices while the multiple playback devices played back the calibration audio.