Crowd-sourced Latency Estimation for Vocal Capture Sync
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Solution Overview
Problem
The variability in latency across different handheld devices, particularly in Android platforms, poses challenges for synchronizing vocal captures with background tracks in karaoke and vocal capture applications, as fixed latency estimates are inadequate and require frequent updates.
Innovation Solution
A system that estimates round-trip latency through audio subsystems using feedback recording and analysis, characterizing temporal offsets for devices with consistent hardware and software configurations, and crowd-sourcing latency characterizations across a subset of devices to apply to a larger population, ensuring accurate synchronization across diverse platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If fixed latency estimates are used for synchronization, then device complexity is reduced, but synchronization accuracy deteriorates due to latency variability across different handheld devices
Solution Approach 1:
The system performs preliminary latency measurement by capturing a test signal through the audio subsystem before actual vocal capture. The captured signal is analyzed to determine the actual latency of the device, and this measured latency is stored for use in subsequent synchronization operations. This preliminary measurement eliminates the need for complex real-time adjustment mechanisms while ensuring accurate synchronization.
Solution Approach 2:
The system implements feedback by capturing a known test signal through the audio subsystem, comparing the captured signal with the original, and using the temporal offset measurement to adjust synchronization parameters. This closed-loop feedback mechanism ensures that the system adapts to the actual latency characteristics of each device, resolving the contradiction between simplicity and accuracy.
2Measurement precision
If latency measurements are performed on all devices, then synchronization accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
Instead of measuring latency on every device individually, the system creates a latency profile or 'copy' of the latency characteristic from a representative sample of devices with the same hardware and software configuration. Once the latency is measured for one device in a configuration group, that measurement is copied and applied to all other devices with identical configurations, dramatically reducing the total measurement time while maintaining accuracy.
Solution Approach 2:
The system establishes that devices with the same hardware and software configuration share universal latency characteristics. By grouping devices into configuration families and determining a single latency profile for each family, the system makes the latency measurement process universal across device populations, reducing redundant measurements while ensuring accurate synchronization for all devices in the group.
3Measurement precision
If device-specific latency measurements are implemented, then synchronization accuracy is improved, but the system requires frequent updates to accommodate new device configurations
Solution Approach 1:
When a new device configuration is encountered, the system performs a latency measurement on the representative device and copies this measurement to the device profile database. Subsequently, all devices matching this configuration can utilize the copied latency profile without requiring individual measurements or system updates, reducing the frequency of updates while maintaining precision.
Solution Approach 2:
The system creates a universal latency profile for each hardware-software configuration combination that can be applied across multiple device instances. This universality means that once a latency profile is established for a configuration family, it serves all devices in that family indefinitely, eliminating the need for frequent updates even as new devices are deployed, while still providing device-specific accuracy through configuration-based grouping.
Data Source
AI summary
Latency on different devices (e.g., devices of differing brand, model, vintage, etc.) can vary significantly and tens of milliseconds can affect human perception of lagging and leading components of a performance. As a result, use of a uniform latency estimate across a wide variety of devices is unlikely to provide good results, and hand-estimating round-trip latency across a wide variety of devices is costly and would constantly need to be updated for new devices. Instead, a system has been developed for crowdsourcing latency estimates.