Transformer-Based Audio Signal Conversion Architecture
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Solution Overview
Problem
Traditional signal processing methods face challenges such as adaptability issues for real-time signal adaptation, difficulty in accurately modeling non-linear signals, and the need for domain knowledge or manual engineering to extract meaningful features from raw data.
Innovation Solution
The use of a Transformer-based architecture for signal conversion, which leverages deep learning to efficiently convert one-dimensional audio signals into various secondary signals, capturing positional and global relationships between input and output signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional signal processing methods are used, then the system is simpler to implement, but adaptability for real-time signal adaptation deteriorates
Solution Approach 1:
The patent replaces traditional mechanical signal processing methods with a neural network-based system. The neural network automatically learns and adapts to real-time signal characteristics without requiring manual tuning or domain knowledge, thereby improving adaptability while the automated nature of the system manages complexity through software rather than hardware reconfiguration.
Solution Approach 2:
The patent employs a neural network that dynamically adjusts its internal parameters (weights and biases) based on input signal characteristics. This allows the system to adapt to different signal types and conditions in real-time by changing its processing parameters automatically, resolving the contradiction between adaptability and complexity.
2Measurement precision
If traditional signal processing methods are used, then the system is easier to operate, but accuracy in modeling non-linear signals deteriorates
Solution Approach 1:
The neural network performs self-service by automatically extracting meaningful features from raw audio signals without requiring manual engineering. The network learns the appropriate feature representations during training and applies them automatically during inference, improving non-linear signal modeling accuracy while eliminating the need for expert intervention in feature extraction.
Solution Approach 2:
The patent performs preliminary training of the neural network offline using labeled data, so that when deployed, the system already possesses the learned knowledge for accurate non-linear signal modeling. This preliminary action separates the complex learning phase from the operational phase, improving accuracy while maintaining ease of operation during actual use.
3Productivity
If domain knowledge and manual engineering are used for feature extraction, then interpretability is improved, but productivity in processing diverse signals deteriorates
Solution Approach 1:
The patent implements a universal neural network architecture that can process diverse signal types (audio, vibration, etc.) through the same underlying mechanism. The network learns domain-specific features automatically for each signal type while maintaining a consistent processing framework, thereby improving productivity across different signal domains without requiring separate manual engineering efforts for each domain.
Data Source
AI summary
Systems and methods for converting a primary one-dimensional signal into a secondary one-dimensional signal of another modality. The primary signal is spliced into a plurality of consecutive frames. A first linear transformation transforms the frames into corresponding vectors. Positional encodings are provided on the vectors to encode relative positional information associated with each sample within each frame. A multi-head self-attention machine-learning model compares relative importance of the samples within each vector to each other in that vector to yield high-level representation vectors. A second linear transformation transforms the high-level representation vectors into corresponding secondary signal frames. The secondary signal frames are concatenated into a reconstructed one-dimensional secondary signal having a different modality than the primary signal.