Neural Ambisonic Transcoding for Binaural Spatial Accuracy
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Solution Overview
Problem
Current transcoding methods for multi-channel audio, particularly ambisonic signals, suffer from noise coloring and attenuation at high frequencies, especially for lower order signals, using linear signal processing models.
Innovation Solution
Employ a machine learning technique using neural networks to model complex non-linear signal relationships for transcoding ambisonic signals into binaural audio, incorporating training with both real and synthetic data to enhance accuracy and resource efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If linear signal processing methods are used for ambisonic to binaural transcoding, then the transcoding process is simple and computationally efficient, but noise coloring and attenuation at high frequencies occur
Solution Approach 1:
The patent replaces traditional linear signal processing methods with a machine learning-based neural network system. The neural network is trained to model the complex non-linear relationship between ambisonic and binaural signals, substituting the mechanical linear processing approach with an intelligent system that can capture non-linear characteristics and avoid noise coloring while preserving high frequency content.
Solution Approach 2:
The patent changes the fundamental parameters of the transcoding approach by moving from fixed linear transformation matrices to adaptive non-linear mappings learned from data. The neural network learns optimal parameter transformations during training, enabling dynamic adaptation to different signal characteristics and avoiding the fixed limitations of linear methods.
2Measurement precision
If higher order ambisonic signals are used, then spatial accuracy and immersion are improved, but computational complexity and processing requirements increase
Solution Approach 1:
The patent applies partial action by training the neural network on a comprehensive dataset that includes various ambisonic orders and configurations. During deployment, the model can handle different input orders efficiently without requiring proportional increases in computational resources, as the non-linear mapping has already learned the essential transformations.
Solution Approach 2:
The patent uses synthetic data copying and augmentation during the training phase to create diverse training examples without requiring proportional increases in real recording data. This allows the model to learn from a wide variety of scenarios while keeping the actual processing requirements during deployment manageable.
3Measurement precision
If machine learning models are trained with both real and synthetic data, then model accuracy and generalization are improved, but training time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by generating and preparing synthetic training data in advance before actual model training begins. This pre-computation of diverse synthetic scenarios allows the model to be trained more efficiently, as the training dataset is already prepared and structured for optimal learning.
Solution Approach 2:
The patent merges real recorded data with synthetic generated data to create a comprehensive training dataset. This combination leverages the authenticity of real recordings with the diversity and controllability of synthetic data, achieving better generalization without requiring exponentially more real data collection time.
Data Source
AI summary
A method including receiving first audio having a first accuracy and a first number of channels and generating second audio based on the first audio, the second audio having a second accuracy and a second number of channels, the first accuracy is a greater spatial accuracy than the second accuracy, the first number of channels is greater than the second number of channels.