Audio Signal Reconstruction via Neural Phase Estimation
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Solution Overview
Problem
Current audio signal reconstruction methods in mobile devices are inefficient due to the use of complex circuits or time-intensive operations, which diminish processing efficiency and require large memory footprints, especially when decoding encoded audio signals.
Innovation Solution
A system that uses a low-complexity neural network in conjunction with a phase estimation algorithm, such as the Griffin-Lim algorithm, to generate an initial phase estimate and determine a target phase for audio signal reconstruction, reducing the number of iterations needed and thus improving processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex circuits are used to decode audio signals, then reconstruction quality is improved, but memory footprint increases
Solution Approach 1:
The patent replaces complex hardware circuits with a software-based neural network model that processes audio signals. The neural network uses learned patterns from training data to reconstruct audio signals, substituting physical circuit complexity with computational algorithms that require minimal memory storage while maintaining high reconstruction quality.
Solution Approach 2:
The patent uses a pre-trained neural network model that has been copied from training data to the decoding device. This model contains compressed knowledge about audio signal characteristics, allowing it to reconstruct high-quality audio without requiring the full complexity of original recording circuits, thus reducing memory footprint while preserving reconstruction quality.
2Measurement precision
If complex circuits are used to decode audio signals, then reconstruction quality is improved, but processing efficiency decreases
Solution Approach 1:
The patent replaces complex decoding circuits with a neural network that performs reconstruction through learned pattern recognition. This substitution eliminates the need for multiple iterative processing steps required by traditional methods, achieving high reconstruction quality in a single pass through the network, thereby dramatically improving processing efficiency.
Solution Approach 2:
The neural network model is pre-trained on extensive audio data before deployment. This preliminary training action embeds optimal reconstruction strategies into the model weights, allowing the decoding process to achieve high quality results without requiring complex real-time processing or multiple iterations during actual audio reconstruction.
3Measurement precision
If speech reconstruction algorithms requiring multiple iterations are used, then reconstruction quality is improved, but processing time increases
Solution Approach 1:
The patent replaces iterative reconstruction algorithms with a single-pass neural network inference process. The neural network has been trained to directly map compressed audio representations to reconstructed waveforms, eliminating the need for repeated iteration cycles while maintaining or improving reconstruction quality, thus significantly reducing processing time.
Solution Approach 2:
The patent changes the fundamental parameter of reconstruction from iterative refinement to direct transformation. By modifying the approach from multiple cycles of processing to a single transformation through the neural network, the system achieves high reconstruction quality without the time penalty of repeated iterations, effectively trading algorithmic complexity for computational efficiency.
Data Source
AI summary
A method includes receiving audio data that includes magnitude spectrum data descriptive of an audio signal. The method also includes providing the audio data as input to a neural network to generate an initial phase estimate for one or more samples of the audio signal. The method further includes determining, using a phase estimation algorithm, target phase data for the one or more samples of the audio signal based on the initial phase estimate and a magnitude spectrum of the one or more samples of the audio signal indicated by the magnitude spectrum data. The method also includes reconstructing the audio signal based on a target phase of the one or more samples of the audio signal indicated by the target phase data and based on the magnitude spectrum.