Sound Source Estimation Using Neural Networks and Auralized Signals
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Solution Overview
Problem
Existing sound source localization techniques are limited by their reliance on simple geometric microphone arrangements, such as linear or circular arrays, which fail to accurately estimate sound source location in environments with arbitrarily shaped devices where sound waves experience scattering, diffraction, and reverberation effects.
Innovation Solution
A method using a neural network trained with auralized signals generated from array-related transfer functions and room impulse responses, incorporating spatial information to estimate sound source location in three-dimensional space, regardless of microphone arrangement, and capable of handling both stationary and moving sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If microphones are arranged in simple geometric patterns (linear or circular arrays), then analysis of detected sound waves becomes easier, but accuracy of sound source location estimation deteriorates in complex environments with arbitrarily shaped devices
Solution Approach 1:
The patent creates a virtual copy of the physical microphone array by generating auralized signals that simulate how sound waves would be detected by microphones in free space without scattering effects. This virtual model allows the neural network to learn accurate sound source localization by training on synthesized data that preserves the geometric relationships of the microphone array while eliminating environmental distortions.
Solution Approach 2:
The patent transforms the sound wave characteristics by applying array-related transfer functions and room impulse responses to generate auralized signals. This parameter transformation converts complex environmental acoustic effects into trainable features that the neural network can process, changing the representation of sound data from raw microphone signals to structured features with known spatial relationships.
2Adaptability or versatility
If microphones are positioned randomly across arbitrarily shaped devices, then adaptability to different device configurations improves, but sound wave analysis becomes complicated due to scattering and diffraction effects
Solution Approach 1:
The patent generates auralized signals that create a virtual representation of sound propagation, copying the essential spatial and temporal characteristics of sound waves without the complicating effects of arbitrary device geometries. This allows the system to handle any microphone configuration while maintaining analysis simplicity through the neural network's learned representations.
Solution Approach 2:
The neural network acts as an intermediary between the complex physical acoustic environment and the simplified analysis required for sound source localization. It learns to map complex auralized signals containing scattering and diffraction effects to accurate spatial locations, mediating between the arbitrary microphone arrangements and the need for precise source estimation.
3Productivity
If conventional signal processing techniques are used for sound source localization, then computational simplicity is maintained, but accuracy deteriorates in environments with scattering, diffraction, and reverberation
Solution Approach 1:
The patent replaces conventional mechanical signal processing techniques with a neural network-based system. Instead of using traditional algorithms that assume simple acoustic environments, the system uses a data-driven neural network trained on auralized signals that explicitly model complex acoustic effects, substituting physics-based methods with machine learning approaches that can handle arbitrary environments.
Solution Approach 2:
The patent performs preliminary generation of auralized signals and extraction of features before the actual sound source localization task. By pre-processing the acoustic data into structured features with known spatial relationships and training the neural network in advance on synthesized data, the system prepares optimized representations that enable accurate and efficient real-time localization without complex runtime computations.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively estimates sound source location in complex environments with arbitrarily shaped devices, accounting for scattering and reverberation effects, and can differentiate between stationary and moving sources, improving accuracy and adaptability.
Implementation Method 1
Sound waves may be diffracted and scattered across the device before they are detected by the microphones
Implementation Method 2
Sound waves may be diffracted and scattered across the device before they are detected by the microphones
Implementation Method 3
Scattering effects, reverberations, and other linear and nonlinear effects across an arbitrarily shaped device
Data Source
AI summary
A system for estimating the location of a stationary or moving sound source includes multiple microphones, which need not be physically aligned in a linear array or a regular geometric pattern in a given environment, an auralizer that generates auralized multi-channel signals based at least on array-related transfer functions and room impulse responses of the microphones as well as signal labels corresponding to the auralized multi-channel signals, a feature extractor that extracts features from the auralized multi-channel signals for efficient processing, and a neural network that can be trained to estimate the location of the sound source based at least on the features extracted from the auralized multi-channel signals and the corresponding signal labels.