Acoustic Pattern Identification Using Spectral Characteristics
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Solution Overview
Problem
Traditional media editing applications face challenges in synchronizing audio and video captured from multiple cameras or multiple takes due to slight variances in timing, volume, and delivery, leading to manual effort and undesired sound delays or echoing effects.
Innovation Solution
The use of acoustic pattern identifiers that analyze spectral characteristics to identify matching audio portions, generating spectral signatures and correlating them to synchronize audio and video, reducing the need for manual time code adjustments and clapper sounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual time code adjustment is used to synchronize audio, then synchronization can be achieved, but significant manual effort and time are required
Solution Approach 1:
The system performs self-service by automatically analyzing audio spectral characteristics and determining synchronization points without requiring manual intervention. The audio portions self-identify their temporal relationships through spectral correlation, eliminating the need for editors to manually adjust time codes.
Solution Approach 2:
The patent replaces the mechanical manual adjustment process with an automated spectral analysis system. Instead of manually comparing time codes and adjusting synchronization, the system uses spectral correlation algorithms to automatically identify matching acoustic patterns and determine precise synchronization points.
2Reliability
If video time codes are used to synchronize audio, then synchronization can be achieved, but audio portions may be misaligned due to frame-rate differences
Solution Approach 1:
The patent transitions from synchronizing audio based on video time codes (temporal dimension tied to frame rate) to synchronizing based on spectral characteristics (frequency domain dimension). By analyzing audio in the spectral domain and correlating spectral patterns, the system achieves precise timing alignment independent of video frame rates.
Solution Approach 2:
The patent introduces spectral characteristics as an intermediary for synchronization. Instead of directly using video time codes to align audio, the system uses spectral correlation as an intermediate step to identify matching acoustic patterns, which then provides more precise timing information for synchronization.
3Reliability
If clapper sounds are used for synchronization, then audio can be aligned, but additional noise is introduced that must be removed
Solution Approach 1:
The patent extracts only the useful spectral information from audio portions without requiring external synchronization aids like clappers. By analyzing the intrinsic spectral characteristics of the audio content itself, the system identifies synchronization points without introducing or requiring additional noise-generating elements that would later need to be removed.
Solution Approach 2:
The patent converts the variability in audio delivery, volume, and utterances (which were previously harmful factors making synchronization difficult) into beneficial features for identification. The spectral analysis method is designed to recognize patterns despite these variations, turning what was once noise or interference into distinctive identifiers for synchronization.
4Adaptability or versatility
If multiple cameras capture audio at different positions, then comprehensive coverage is achieved, but audio synchronization becomes more difficult due to spatial variations
Solution Approach 1:
The patent creates a universal synchronization method that works across multiple cameras regardless of their spatial positions. The spectral correlation technique is applicable to any audio portion from any camera, providing a unified approach that adapts to different spatial arrangements, recording conditions, and environmental factors without requiring camera-specific synchronization procedures.
Data Source
AI summary
Embodiments of the invention relate generally to computing devices and systems, software, computer programs, applications, and user interfaces for identifying acoustic patterns, and more particularly, to determining equivalent portions of audio using spectral characteristics to, for example, synchronize audio and/or video captured at multiple cameras or different intervals of time.