Network Microphone Training Data via Metadata-Based Audio Simulation
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Solution Overview
Problem
Existing methods for generating training data for machine learning models, particularly for network microphone devices, often result in unrealistic or insufficiently diverse samples, which can skew model training and require direct access to sensitive real-world data, posing privacy concerns and resource demands.
Innovation Solution
A method for generating diverse and realistic training data by simulating noisy scenarios based on metadata from real-world audio samples, using probabilistic models to create noised versions of annotated speech samples, and updating network microphone device software configuration parameters without storing raw data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing methods are used to generate training data for machine learning models, then model training can proceed, but the training data is unrealistic or insufficiently diverse, leading to skewed model performance
Solution Approach 1:
The patent transforms clean audio samples into diverse training data by systematically varying multiple parameters including noise types, noise levels, reverberation characteristics, and environmental conditions. This parameter-based transformation enables generation of realistic noisy scenarios from limited clean data, resolving the contradiction between data quality and diversity
Solution Approach 2:
The patent creates synthetic copies of real-world noisy audio scenarios by applying simulated noise and environmental effects to clean reference samples. These synthesized copies serve as realistic training data without requiring direct access to sensitive real-world recordings, maintaining both quality and diversity
2Manufacturing precision
If direct access to real-world data is obtained to improve training data realism, then training data quality improves, but privacy concerns and resource demands increase
Solution Approach 1:
The patent introduces clean audio samples as intermediary materials that mediate between the need for realistic training data and privacy protection. By synthesizing noisy scenarios from clean references rather than using direct real-world recordings, the system achieves realism without compromising privacy or requiring access to sensitive data
Solution Approach 2:
The patent creates synthetic replicas of real-world noisy audio environments through computational simulation rather than direct data collection. This copying approach preserves the statistical characteristics and realism of real-world data while eliminating privacy risks associated with handling actual user recordings
3Adaptability or versatility
If more diverse training scenarios are generated to improve model adaptability, then model performance enhances, but computational resources and processing time increase
Solution Approach 1:
The patent performs preliminary processing by generating and storing pre-computed noise profiles, environmental characteristics, and transformation parameters from limited real data. This preliminary action creates reusable templates that can be efficiently applied to generate diverse training scenarios without repeating computationally intensive processing for each sample
Solution Approach 2:
The patent achieves diverse training scenarios through efficient parameter manipulation of existing clean samples rather than generating entirely new audio content. By systematically varying noise parameters, reverberation settings, and environmental conditions, the system produces high-volume diverse training data with relatively low computational overhead
Data Source
AI summary
Systems and methods for generating training data are described herein. Pieces of metadata captured by a plurality of networked sensor systems can be captured, where each piece of metadata is associated with a specific set of sensor data captured by one of the plurality of networked sensor systems and includes a set of characteristics for the specific set of captured sensor data. A probabilistic model can be generated based on the received metadata and simulations can be performed based upon a training corpus by generating multiple scenarios, and, for each scenario, a scenario specific version of a particular annotated sample is generated by performing a simulation using the particular annotated sample. The scenario specific versions of annotated samples from the training corpus can be stored as a training data set on the at least one network device.