Sound Source Identification Using Threshold-Based Signal Filtering
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Solution Overview
Problem
Existing sound source identification technologies, such as those using pulse neuron models, face challenges in accurately detecting specific sound sources in noisy environments due to signal quality issues.
Innovation Solution
A sound source identification apparatus and method that incorporates a sound collection unit with multiple microphones, a sound source localization unit, a sound source separation unit, and a sound source identification unit, where the identification unit processes signals only when they exceed a predetermined threshold value, calculated based on noise spectrum estimation, to improve detection accuracy in noisy conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sound source identification is performed using all collected acoustic signals, then the identification process covers all potential sound sources, but the detection accuracy decreases in noisy environments due to low signal quality
Solution Approach 1:
The patent performs preliminary sound source localization and separation before identification, and introduces a quality evaluation step that assesses signal characteristics (such as SNR, spectral features) before the identification process. This preliminary filtering ensures that only high-quality signals are subjected to identification, preventing noise from degrading accuracy.
Solution Approach 2:
The patent applies different processing strategies to different signal segments based on their quality characteristics. High-quality signals (above threshold) undergo full identification processing, while low-quality signals (below threshold) are either enhanced through noise reduction algorithms or discarded. This localized quality-based processing optimizes overall identification accuracy.
2Measurement precision
If a threshold value is introduced to filter signals before identification, then detection accuracy improves by excluding noise, but the complexity of the identification process increases
Solution Approach 1:
The patent dynamically adjusts the signal quality threshold based on environmental noise levels and signal characteristics. The threshold is not fixed but adapts to changing conditions, optimizing the balance between excluding noise and maintaining process simplicity. This parameter adaptation allows the system to maintain high accuracy without requiring complex fixed-threshold configurations.
3Reliability
If signal quality filtering is applied before identification, then erroneous identifications are reduced, but the time required for signal processing increases
Solution Approach 1:
The patent applies partial filtering by setting a quality threshold that excludes only the poorest quality signals while processing the majority of acceptable signals. This partial action approach prevents erroneous identifications from the worst noise-contaminated signals without unnecessarily delaying processing of good-quality signals, thus balancing reliability and processing time.
Data Source
AI summary
A sound source identification apparatus includes a sound collection unit including a plurality of microphones, a sound source localization unit configured to localize a sound source on the basis of an acoustic signal collected by the sound collection unit, a sound source separation unit configured to perform separation of the sound source on the basis of the signal localized by the sound source localization unit, and a sound source identification unit configured to perform identification of a type of sound source on the basis of a result of the separation in the sound source separation unit, and a signal input to the sound source identification unit is a signal having a magnitude equal to or greater than a first threshold value which is a predetermined value.