Frequency Loss Model for Masked Voice Clarity
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Solution Overview
Problem
Facemasks obstruct voice frequencies, making it difficult for individuals to communicate effectively, especially in professional or virtual settings, due to varying mask materials and compositions causing different levels of frequency loss.
Innovation Solution
A computer-implemented method using processors to generate a frequency loss model by analyzing unobstructed and obstructed voice data, determining frequency deltas and attenuation values, and processing audio data to mitigate frequency loss through frequency shaping, which can be implemented in wearable devices and web conferencing software.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If facemasks are worn to prevent virus transmission, then health safety is improved, but voice frequency transmission deteriorates
Solution Approach 1:
The patent converts the harmful frequency-blocking effect of facemasks into a beneficial solution by creating a frequency loss model that identifies which frequencies are blocked and applies inverse filtering to restore them. The system takes the unwanted side effect (frequency attenuation) and transforms it into useful information for audio restoration, thereby maintaining both health safety and communication quality
Solution Approach 2:
The patent introduces an intermediary processing system between the mask-wearer and the listener. This intermediary includes a frequency loss model, audio processing module, and communication system that analyzes the obstructed audio signal, identifies frequency losses based on mask type, and applies compensatory filtering to restore the original voice frequencies before transmission to the recipient
2Reliability
If different types of facemasks are used, then protection effectiveness varies, but frequency loss characteristics become more complex
Solution Approach 1:
The patent applies preliminary action by pre-characterizing different mask types (surgical, cloth, scarf) and their frequency loss characteristics before actual use. The system creates a library of frequency loss models for different mask types, allowing the appropriate model to be selected and applied without real-time complexity. This pre-processing of mask characteristics simplifies the runtime processing while maintaining accuracy across different mask types
Data Source
AI summary
Computer-implemented methods, computer program products, and computer systems for mitigating frequency loss may include one or more processors configured for receiving first audio data corresponding to unobstructed user utterances, receiving second audio data corresponding to first obstructed user utterances, generating a frequency loss (FL) model representing frequency loss between the first audio data and the second audio data, receiving third audio data corresponding to one or more second obstructed user utterances, processing the third audio data using the FL model to generate fourth audio data corresponding to a frequency loss mitigated version of the second obstructed user utterances, and transmitting the fourth audio data to a recipient computing device. The first obstructed user utterances are obstructed by a facemask and the one or more second obstructed user utterances is obstructed by the facemask. The FL model may be executed as an audio plugin in a web conferencing program.