Application of deep learning models in sound recognition

News Detail

The application of deep learning models in sound recognition has formed a comprehensive technical framework. Its core value lies in achieving high-precision, multi-scenario sound feature extraction and semantic understanding through end-to-end learning. The following are key technical application directions and typical model architectures:

1. Acoustic Feature Extraction

Optimization of Time-Frequency Analysis

Using CNNs to automatically learn local features (such as harmonic structure and formants) from mel-spectrograms, replacing traditional manual feature engineering using MFCCs, this approach improves classification accuracy by 27% in noisy environments on the UrbanSound8K dataset.
Lightweight models such as MobileNetV3, using depthwise separable convolutions and PSA attention modules, achieve 100% top-5 bird sound recognition accuracy with only 2.6M parameters.

Enhanced Time Series Modeling

The CRNN hybrid architecture (CNN + BiLSTM) simultaneously captures the spectral characteristics and temporal dependencies of sound events, achieving an F1 score of 92.3% for detecting sudden events such as glass breaking.
Transformer uses a self-attention mechanism to process long audio sequences, achieving over 99% accuracy in classifying infant cries for hunger and pain.

II. Specific Application Scenarios

Application Areas	Technical Solutions	Performance Metrics
Pet Health Monitoring	RNN-Based Voice Emotion Analysis System, Supporting Classification of Over 10 Voice Types
Smart Home Security	End-to-End Abnormal Sound Detection Using CNN+CTC	Response Latency <200ms
Medical Aid Diagnosis	Transfer Learning Voiceprint Model (e.g., Urbansound Architecture) for Pathological Cough Recognition	AUC 0.98

III. Cutting-Edge Technological Breakthroughs

Multimodal Fusion: Joint training of the YOLOv8 visual model and LSTM audio network simultaneously analyzes infant movements and crying frequency, reducing false positives by 38%.
Lightweight Deployment: Chips such as the WT2605A integrate DNN inference engines, reducing power consumption of the voiceprint recognition module to 15mW.

(Note: Reference numerals in the table are indicated outside the table.)