Siamese Network Audio Alignment Detection for Dubbed Media
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Solution Overview
Problem
The manual process of audio quality control in dubbed media is inefficient and unable to detect small synchronization errors, leading to viewer disengagement due to misalignment between audio and video.
Innovation Solution
A system that uses a prediction network to analyze two audio signals, extracting features from both the reference and target audio signals, and determines offsets using a Siamese network architecture, trained with contrastive learning to detect global and intermittent synchronization issues, even in the presence of audio variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual quality control processes are used with specialized listeners or visual waveform comparison, then subjective evaluation capability is maintained, but detection precision and productivity deteriorate due to inability to detect small isolated errors and high cost
Solution Approach 1:
The patent replaces the manual mechanical process of audio quality control (human listeners comparing waveforms) with an automated machine learning system. The system uses trained neural networks to automatically detect synchronization errors, eliminating the need for human operators to manually compare audio tracks while achieving superior detection precision for even subtle misalignments.
Solution Approach 2:
The patent introduces an automated analysis system as an intermediary between the audio signals and the quality control decision. This system processes the audio signals through multiple neural network models that compare reference and target audio tracks, automatically identifying synchronization errors without requiring direct human intervention in the comparison process.
2Ease of operation
If manual quality control with visual waveform comparison is used, then operational simplicity is maintained, but detection capability deteriorates due to inability to detect small isolated synchronization errors
Solution Approach 1:
The patent replaces the simple but imprecise manual waveform comparison method with an automated machine learning system. The system uses trained neural networks to automatically detect synchronization errors, eliminating the need for human operators to manually compare audio tracks while achieving superior detection precision for even subtle misalignments.
Solution Approach 2:
The patent transforms the quality control process by changing the analytical parameters used for detection. Instead of relying on human visual inspection of waveforms, the system uses automated audio feature extraction and comparison, analyzing temporal offsets, spectral characteristics, and other acoustic parameters to detect synchronization errors that are imperceptible to human operators.
3Productivity
If automated systems are introduced to improve productivity, then quality control efficiency improves, but device complexity increases due to need for trained prediction networks and contrastive learning mechanisms
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network models using contrastive learning on large datasets of synchronized and desynchronized audio pairs. This offline training phase prepares the system in advance, so that during actual quality control operations, the pre-trained models can rapidly analyze audio signals without requiring complex real-time computations, thus reducing operational complexity while maintaining high productivity.
Solution Approach 2:
The patent uses copying by creating multiple specialized neural network models (Siamese networks, contrastive learning models) that replicate and specialize in different aspects of audio synchronization detection. These copied models work in parallel or sequence to comprehensively analyze audio signals, distributing the complexity across multiple specialized components rather than requiring one monolithic complex system.
4Reliability
If specialized manual evaluation processes are used, then subjective quality assessment capability is maintained, but reliability deteriorates due to high cost and inconsistency in detecting synchronization errors
Solution Approach 1:
The patent replaces the unreliable manual evaluation process with a consistent automated machine learning system. The system uses trained neural networks to automatically detect synchronization errors, eliminating human variability and subjectivity. Once trained, the system provides reliable, repeatable检测结果 across different audio content and operators, ensuring consistent quality control standards are applied universally.
Solution Approach 2:
The patent incorporates feedback mechanisms where the automated system continuously analyzes audio signals and provides detailed synchronization error detection results. The system can identify specific temporal offsets, locate error positions, and provide confidence metrics, creating a feedback loop that enables precise and reliable quality control decisions without requiring subjective human judgment.
Data Source
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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.