Audio Signal Alignment Detection Using Transformer Features
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Solution Overview
Problem
Audio synchronization issues in dubbed media are prevalent and difficult to detect efficiently, leading to viewer disengagement, as current quality control methods are manual, costly, and ineffective in identifying small errors.
Innovation Solution
A system using a prediction network with transformer-based branches to analyze reference and target audio signals, extracting features in a higher dimension space, and detecting synchronization issues such as global and intermittent offsets, trained with contrastive learning to recognize various audio manipulations and errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual quality control methods are used to detect audio synchronization errors, then human expertise can identify quality issues, but the process is costly, inefficient, and unable to detect small isolated errors
Solution Approach 1:
The patent replaces manual human quality control processes with an automated machine learning system. The system uses a prediction network with transformer-based branches to automatically detect audio synchronization errors, substituting human mechanical inspection with computational analysis. This enables both high precision detection of small errors and improved productivity through automation.
Solution Approach 2:
The patent introduces a prediction network as an intermediary between the audio signals and the quality control decision. This neural network intermediary processes the audio features and predicts synchronization errors, acting as a bridge that translates raw audio data into actionable quality control insights with high precision and efficiency.
2Reliability
If subjective evaluation by specialized users is used for audio quality control, then expert listening can detect audio differences, but the process is costly and time-consuming
Solution Approach 1:
The patent replaces subjective human listening evaluation with objective automated audio analysis. The prediction network objectively measures audio synchronization errors through computational methods, eliminating the time required for human listening while maintaining or improving detection accuracy through consistent, repeatable measurements.
Solution Approach 2:
The patent enables the audio quality control system to perform self-evaluation through automated error detection. The prediction network independently analyzes audio signals and identifies synchronization issues without requiring external human expertise, making the system self-sufficient and dramatically reducing quality control time.
3Ease of operation
If manual waveform comparison is used to detect synchronization errors, then visual inspection can identify misalignment, but small isolated errors remain undetected
Solution Approach 1:
The patent transforms the audio analysis from simple visual waveform comparison to multi-dimensional feature space analysis. The prediction network extracts and compares audio features in higher dimensional spaces, enabling detection of subtle synchronization errors that are invisible in traditional visual waveform inspection while maintaining operational simplicity through automated processing.
Data Source
AI summary
In some embodiments, a method analyzes a first sample of a first audio signal to determine a first representation in a space. A plurality of second samples for a second audio signal is analyzed to determine a plurality of second representations in the space. The method compares the first representation and the plurality of second representations in the space to select a second representation. An offset is determined between the first sample and a second sample that is associated with the second representation. The offset is output.