Simulated Audio Data Generation for AI Speech Training
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Solution Overview
Problem
Current AI speech processing technologies face challenges in collecting diverse audio data for training, which is resource-intensive and often results in overfitting and poor recognition performance due to insufficient or unvaried training data.
Innovation Solution
A method is introduced to generate simulated audio data by combining pure speech and noise data, using mathematical models to simulate changes in audio during spatial transmission, thereby creating diverse target audio data that can be used for training AI models, reducing the need for manual data collection and enhancing data diversity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If manual data collection is used to gather diverse audio data for AI training, then data diversity and quantity can be improved, but resource consumption and time cost increase significantly
Solution Approach 1:
The patent uses signal processing techniques to synthesize new audio training data by copying and transforming existing clean speech and noise data. Through operations like additive noise mixing, convolution with impulse responses, and parameter transformation, the system generates diverse simulated audio scenarios without manual recording, thereby increasing data quantity while reducing collection time
Solution Approach 2:
The patent pre-processes clean speech data and noise data into standardized formats and stores them as templates. When training data is needed, the system rapidly generates diverse audio samples by applying pre-defined signal processing operations to these templates, eliminating the need for real-time or on-demand manual data collection
2Adaptability or versatility
If manual data collection is used to gather diverse audio data for AI training, then data diversity can be improved, but resource consumption increases
Solution Approach 1:
The patent achieves data diversity by systematically varying parameters of audio signals including noise level, signal-to-noise ratio, impulse response characteristics, and mixing proportions. By changing these parameters computationally, the system generates diverse training scenarios from a limited set of base recordings, eliminating the need for manual collection across multiple environments and conditions
Solution Approach 2:
The patent creates a universal data synthesis system that can generate multiple types of audio training data (noisy speech, reverberant speech, echoic speech) from a single set of clean recordings. This multi-functional approach allows one recording session to produce diverse training data for various AI tasks, reducing overall resource consumption
3Loss of energy
If insufficient or unvaried training data is used for AI speech processing, then resource consumption is reduced, but model performance deteriorates with overfitting and poor recognition
Solution Approach 1:
The patent implements dynamic data generation where training data characteristics can be adjusted based on model training progress. The system varies noise levels, signal-to-noise ratios, and other parameters during different training phases, allowing the model to learn from progressively challenging scenarios. This dynamic approach improves model robustness without requiring proportionally more manual data collection
Solution Approach 2:
The patent introduces signal processing operations as intermediary processes between clean speech data and training data. These intermediaries (noise addition, convolution, filtering) transform simple recordings into complex training scenarios, enabling the model to learn from diverse data while keeping the original data collection resource-intensive work minimal
Data Source
AI summary
A method for noise reduction and echo cancellation includes obtaining original audio data, the original audio data including pure speech audio data and noise audio data, generating simulated noisy data based on the pure speech audio data and the noise audio data, and generating target audio data based on the simulated noisy data, the target audio data being used for simulating changes in the original audio data after spatial transmission.