Sound Source Characterization via Single-Source Zones
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Solution Overview
Problem
Existing sound source localization methods are inefficient for real-time processing of multiple sound sources and fail in reverberant environments, relying on assumptions that do not hold in practical scenarios, such as overlapping sources in anechoic conditions.
Innovation Solution
A processor-implemented method that uses time-frequency transforms of sound signals from multiple sensing devices to detect single-source constant-time analysis zones, estimating directions of arrival and counting sources by correlating signals across microphone pairs, and generating histograms for accurate source localization and counting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If existing sound source localization methods are used, then they can process sound sources, but they are inefficient for real-time processing of multiple sound sources and fail in reverberant environments
Solution Approach 1:
The patent segments the complex task of sound source localization into distinct processing stages: (1) time-frequency transform of microphone signals, (2) detection of single-source constant-time analysis zones using correlation, (3) direction of arrival estimation within detected zones, and (4) histogram-based source counting. This segmentation enables efficient real-time processing by focusing computational resources on promising regions identified through correlation analysis.
Solution Approach 2:
The patent introduces an intermediary correlation-based detection mechanism that identifies single-source constant-time analysis zones before performing direction of arrival estimation. This intermediary step acts as a filter, selecting only those time-frequency regions where a single dominant source is present, thereby improving reliability in reverberant environments while maintaining real-time processing efficiency.
2Measurement precision
If conventional methods are used, then they provide localization estimates, but they rely on assumptions that do not hold in practical scenarios, such as overlapping sources in anechoic conditions
Solution Approach 1:
The patent changes the analysis parameters by working in the time-frequency domain rather than pure time domain, and by identifying regions where single-source assumptions hold (constant-time analysis zones). This parameter transformation allows the method to achieve high localization accuracy while adapting to practical scenarios with overlapping sources and reverberation, where conventional anechoic assumptions fail.
Solution Approach 2:
The patent dynamically identifies single-source constant-time analysis zones through correlation-based detection, allowing the method to adapt to changing acoustic conditions in real-time. This dynamic approach enables the system to handle varying numbers of sources, overlapping signals, and reverberant environments by flexibly selecting appropriate analysis regions rather than relying on fixed assumptions.
3Measurement precision
If high accuracy localization is achieved, then source positions are precisely determined, but computational complexity increases
Solution Approach 1:
The patent segments the computational workload by first performing low-complexity correlation-based detection to identify single-source zones, then applying computationally intensive direction of arrival estimation only within these restricted zones. This segmentation dramatically reduces overall computational complexity while maintaining high localization accuracy, as the expensive estimation operations are performed only where needed rather than across the entire time-frequency spectrum.
Solution Approach 2:
The patent applies partial action by performing direction of arrival estimation only in detected single-source constant-time analysis zones rather than across all time-frequency data. This selective approach achieves high measurement precision for actual sources while avoiding unnecessary computational complexity in regions without sources or with overlapping signals.
Data Source
AI summary
A processor-implemented method for sound characterization is described. In one implementation, time-frequency transform of each of a plurality of sound signals from one or more sources, the sound signals being detected by a plurality of sensing devices, is derived. One or more single-source constant-time analysis zones based at least on correlation between the time-frequency transform signals from a pair of sensing devices are detected. At least one direction of arrival for each source in the detected single source analysis zones are detected. A histogram of the estimated directions of arrival is created and an estimate of a number of the sound sources and corresponding directions of arrival are generated based at least on the histogram.