Audio Source Isolation Using AI Localization in Noisy Environments
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Solution Overview
Problem
Noise interference in audio capture systems, particularly in microphone arrays, affects speech intelligibility and listener experience, and traditional beamforming techniques require numerous microphones, expensive hardware, and manual setup.
Innovation Solution
Employing AI-based deep neural networks to isolate audio signals using multiple capture devices, predicting location, class, and localized audio representations, reducing the need for traditional beamforming and manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional beamforming microphone arrays are used to capture audio from specific directions, then audio directionality is improved, but noise interference increases and speech intelligibility deteriorates
Solution Approach 1:
The patent replaces traditional mechanical beamforming approaches with AI-based deep neural networks that process audio signals computationally. This substitution allows for more sophisticated noise separation and audio source isolation without the physical constraints and noise amplification issues of conventional beamforming microphone arrays
Solution Approach 2:
The system changes the processing parameters by using machine learning models to dynamically adjust audio signal characteristics. The deep neural networks analyze and transform audio parameters such as frequency, amplitude, and temporal patterns to isolate desired audio sources while suppressing noise, rather than relying on fixed beamforming patterns
2Measurement precision
If beamforming microphone arrays are used to improve audio capture, then audio directionality is improved, but device complexity and hardware cost increase
Solution Approach 1:
The patent replaces complex mechanical microphone array systems with software-based AI processing. The deep neural networks perform audio source separation and noise reduction through computational algorithms, eliminating the need for precise physical microphone positioning and complex hardware configurations
Solution Approach 2:
The AI-based system provides multiple functions including noise reduction, audio source isolation, speech enhancement, and audio separation within a single software framework. This multi-functional approach replaces what would otherwise require multiple specialized hardware components and manual setup procedures
3Measurement precision
If traditional beamforming techniques are used for audio capture, then audio directionality is improved, but manual setup and configuration are required
Solution Approach 1:
The system performs self-configuration through automatic audio environment analysis. The deep neural networks adaptively learn the acoustic characteristics of the environment and automatically optimize audio processing parameters without requiring manual setup, calibration, or user intervention
Solution Approach 2:
The AI models are pre-trained on extensive audio datasets to perform common audio processing tasks automatically. This preliminary training enables the system to handle various audio scenarios without requiring manual configuration, as the models have already learned optimal processing strategies during the training phase
4Object-affected harmful factors
If AI-based deep neural networks are used to isolate audio signals, then noise reduction and audio quality improvement are achieved, but computational processing requirements increase
Solution Approach 1:
The system applies AI processing selectively to specific audio frequency ranges and time periods where noise is most problematic. Rather than processing the entire audio spectrum continuously, the deep neural networks focus computational resources on isolating and enhancing relevant audio sources, reducing overall energy consumption
Data Source
AI summary
Techniques for isolating audio signals related to audio sources within an audio environment are discussed herein. Examples may include receiving a plurality of audio data objects. Each audio data object includes digitized audio signals captured by a capture device positioned within an audio environment. Examples may also include inputting the audio data objects to a source localizer model that is configured to generate, based on the audio data objects, one or more audio source position estimate objects. Examples may also include inputting the audio data objects and each audio source position estimate object to a source generator model of one or more source generator models. The source generator model is configured to generate, based on the audio source position estimate object, a source isolated audio output component. The source isolated audio output component may include isolated audio signals associated with an audio source within the audio environment.