Audio Isolation Neural Network for Arena Acoustic Feedback
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Solution Overview
Problem
Existing audio processing systems for arena environments face challenges in accurately isolating desired audio signals due to noise, reverberation, and acoustic feedback, which degrade the quality of broadcast audio and speech reinforcement.
Innovation Solution
The audio isolation signal processing system employs machine learning and AI techniques to classify and isolate audio sources, utilizing multi-lobe capture devices and neural network models to generate an immersive audio stream by suppressing unwanted sounds while amplifying desired sounds, and allows for user control of noise cancellation and audio optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional audio capture methods are used in arena environments, then the system structure is simple, but noise, reverberation, and acoustic feedback significantly degrade audio quality
Solution Approach 1:
The audio processing system is segmented into multiple functional modules: audio capture devices (microphones) positioned at specific locations, digital signal processing components that separately handle different audio signals, and machine learning models that independently classify and process different audio sources. This modular segmentation enables sophisticated audio enhancement while maintaining manageable system complexity through organized functional decomposition.
Solution Approach 2:
Machine learning models serve as intermediaries between the raw audio capture and the final audio output. These models act as intelligent mediators that analyze captured audio signals, classify different sound sources (speech, crowd noise, music), and apply appropriate processing techniques. This intermediary layer enables complex audio optimization without requiring direct complex hardware configurations.
2Measurement precision
If multiple audio capture devices are deployed to improve audio isolation, then audio quality improves, but the difficulty of detecting and measuring audio sources increases
Solution Approach 1:
The patent replaces traditional mechanical/audio engineering approaches with machine learning-based classification systems. Instead of relying solely on physical microphone positioning and acoustic principles to separate sound sources, the system uses trained neural networks to automatically classify and isolate different audio sources (speech, crowd noise, music, advertisements) based on their acoustic characteristics. This substitution of mechanical separation methods with intelligent classification significantly reduces the difficulty of detecting and measuring multiple overlapping audio sources.
Solution Approach 2:
The system changes the parameters used for audio source identification from simple physical positioning to complex acoustic feature analysis. Machine learning models analyze multiple parameters simultaneously (frequency spectrum, temporal patterns, spatial characteristics) to classify audio sources. This parameter transformation enables precise audio source isolation even when multiple sources are present, as the system can distinguish sources based on their unique acoustic parameter signatures rather than relying on difficult physical measurements.
3Reliability
If noise cancellation and audio optimization features are added, then immersive audio experience improves, but device complexity increases
Solution Approach 1:
The audio processing system is designed with multi-functional capabilities that handle various audio enhancement tasks through a unified machine learning framework. The same system can perform noise cancellation, reverberation reduction, speech enhancement, and source separation by applying different processing techniques to the classified audio sources. This universal approach improves immersive audio experience quality while managing complexity by avoiding separate dedicated systems for each function.
Solution Approach 2:
The system incorporates automatic audio classification and processing that operates autonomously without requiring manual configuration or intervention. Machine learning models automatically analyze captured audio, identify different sound sources, and apply appropriate enhancement or suppression techniques based on the desired audio output (broadcast, speech reinforcement, or immersive experience). This self-service capability improves audio quality while minimizing the operational complexity for users.
Data Source
AI summary
Techniques are disclosed herein for providing audio enhancement and optimization of an immersive audio experience. Examples may include generating an audio feature set for a transduced audio stream captured in an environment, inputting the audio feature set to a neural network model configured to generate an audio isolation mask associated with the transduced audio stream, and generating isolated audio for the transduced audio stream based at least in part on the audio isolation mask.