Audio Signal Timing Alignment Using Twin-Network Offset Prediction
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Solution Overview
Problem
Existing methods for timing alignment of audio signals fail to effectively handle noise signals, leading to inaccuracies and inefficiencies, especially in noisy environments.
Innovation Solution
Utilizing a deep neural network-based approach that generates frequency domain images for audio signals, employs a twin neural network for feature extraction, and a prediction network for end-to-end timing offset prediction, thereby aligning audio signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional timing alignment methods (moving delay value, middle alignment, feature point matching) are used, then the device complexity is low and ease of operation is high, but the reliability and measurement precision deteriorate in noisy environments
Solution Approach 1:
The patent replaces traditional mechanical signal processing methods (moving delay value, middle alignment, feature point matching) with a deep neural network-based timing offset prediction model. This substitution enables the system to automatically learn and extract robust features from frequency domain images, achieving reliable timing alignment even in noisy environments where traditional methods fail.
Solution Approach 2:
The patent transforms the timing alignment problem from the time domain to the frequency domain by generating frequency domain images of audio signals. This parameter transformation allows the deep neural network to process signals in a different representation space where noise is more easily filtered and timing offsets can be more accurately predicted.
2Measurement precision
If deep neural network-based timing offset prediction model is used, then the measurement precision and reliability improve, but the device complexity and computational requirements increase
Solution Approach 1:
The timing offset prediction model is divided into distinct functional modules: a twin neural network for feature extraction from frequency domain images, a feature fusion module for combining extracted features, and a prediction network for determining the timing offset. This segmentation allows each module to be optimized independently and makes the overall complex system more manageable and interpretable.
Solution Approach 2:
The patent introduces frequency domain images as an intermediary representation between the raw audio signals and the timing offset prediction. This intermediary transformation provides a more suitable input format for the deep neural network, enabling better feature extraction and improving prediction accuracy while maintaining reasonable computational complexity.
3Reliability
If deep neural network feature extraction is used, then the robustness and stability improve in noisy environments, but the processing time and computational resources increase
Solution Approach 1:
The patent performs a preliminary transformation of audio signals into frequency domain images before feeding them to the deep neural network. This pre-processing step organizes the signal data in a format that highlights temporal patterns and makes noise more distinguishable, allowing the network to process information more efficiently and reduce overall processing time despite the added transformation step.
Data Source
AI summary
A method and device for timing alignment of audio signals. The method includes: generating frequency domain images respectively for an audio signal to be aligned and a template audio signal (S110); inputting the frequency domain images into a twin neural network of a timing offset prediction model respectively, to obtain two frequency domain features output by the twin neural network (S120); fusing the two frequency domain features to obtain a fused feature (S130); inputting the fused features into a prediction network of the timing offset prediction model to obtain a timing offset output by the prediction network (S140); and performing timing alignment processing on the audio signal to be aligned according to the timing offset (S150). The technical solution is more robust, and especially in a noisy environment, features extracted by a deep neural network are more intrinsic and more stable. An end-to-end timing offset prediction model is more accurate and faster.