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This seminar systematically explains noise analysis, prediction, and improvement technologies using machine learning and deep learning with a focus on practical application. It addresses complex noise phenomena that cannot be captured by conventional FFT and CAE analysis, and organizes methods for factor extraction, anomaly detection, and predictive model construction using data-driven approaches. In addition, practical application methods for AI-based noise control are explained through real-world case studies, directly connecting to design, evaluation, and field improvement.

・ Fundamentals of noise pattern recognition using machine learning
・ Spectral analysis methods using CNN and RNN
・ Practical methods for anomaly detection and predictive model building
・ Hybrid design methods integrating CAE, experiments, and AI
・ Practical AI noise improvement processes through real-world case studies

• 1. Fundamentals of Noise Pattern Recognition Using Machine Learning
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  1-1 Estimation of noise factors and feature design using regression and classification models
  1-2 Feature engineering for noise data (RMS, spectral centroid, band energy)
  1-3 Use cases of supervised and unsupervised learning
  1-4 Hybridization of physical models and data-driven models
  
  2. Noise Spectral Analysis Using Deep Learning
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  2-1 Basic structure of spectrogram analysis using CNN
  2-2 Time-series learning of noise data using RNN and LSTM
  2-3 Automatic extraction of frequency structures and feature learning
  2-4 Trade-off between model accuracy and interpretability
  
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  3. Noise Anomaly Detection and Predictive Model Construction
  
  3-1 Design of classification models for normal and abnormal sound
  3-2 Anomaly detection using autoencoders
  3-3 Learning methods in data-scarce environments (transfer learning, data augmentation)
  3-4 Evaluation metrics for predictive models (accuracy, recall, false detection rate)
  
  4. Practical Application and Noise Reduction Strategy
  
  4-1 Process of translating AI analysis results into design specifications
  4-2 Hybrid design method integrating CAE and experimental analysis
  4-3 Gaps occurring in field application and countermeasures
  4-4 Development approach from noise control to “pleasant sound design”
  
• 5. Real-world Case Studies of AI Noise Analysis
  
  5-1 Anomaly detection applied to high-frequency motor noise in automobiles
  5-2 Early detection of abnormal vibration noise in industrial machinery
  5-3 Practical examples and effects of AI-based noise improvement projects using field data
  

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  6. Q&A