Signal Processing
多语言音频 distress 检测
Multilingual Audio Distress DetectionA system for identifying distress signals in audio data across multiple languages, utilizing Convolutional Neural Networks (CNN) and Mel-Frequency Cepstral Coefficients (MFCC) to operate effectively in noisy environments. / 多语言音频 distress 信号检测系统，利用 CNN 和 MFCC 在噪声环境中有效识别跨语言 distress 信号。
- Knowledge type
- 实际应用
- First proposed or discovered
- Proposer
- Turkey Farm member
- Creator
- 农场主
- Last updated
- 2026-10-08 07:58:11
Sub-knowledge points
Multilingual signal classificationNoise robustnessReal-time processing
Higher-order knowledge points
No connected node yet
Main parameters and performance indicators
MFCC features: Audio representation for pattern recognitionCompact CNN: Lightweight neural network architectureNoisy environments: Adverse acoustic conditions for robustness
Open problems
No unresolved problem recorded yet
Related literature
ranked by community starsReal-Time Detection of Multilingual Audio Distress Signals Using Compact CNN and MFCC Features in Noisy EnvironmentsOpen source
Related products and links
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Creation and modification trail
1 recordsRecord title: Multilingual Audio Distress Detection.
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