DCNN-LSTM Audio Classification

Deep learning approach combining feature engineering and data augmentation for audio recognition

DCNN-LSTM Based Audio Classification with Advanced Feature Engineering and Data Augmentation

This research project develops a novel deep learning approach for audio classification by combining Deep Convolutional Neural Networks (DCNN) and Long Short-Term Memory (LSTM) networks with advanced feature engineering and data augmentation techniques.

Research Motivation

Audio classification is a fundamental task in machine learning with applications ranging from speech recognition to environmental sound detection. This project addresses the challenge of improving classification accuracy through innovative architectural design and data processing techniques.

Technical Architecture

🎵 Hybrid Model Design: Integration of DCNN for spatial feature extraction and LSTM for temporal modeling

🔧 Feature Engineering: Advanced techniques for extracting meaningful audio characteristics

📈 Data Augmentation: Sophisticated methods to enhance dataset diversity and model robustness

Key Innovations

DCNN-LSTM Integration:

  • Convolutional layers for spectral feature extraction
  • LSTM networks for temporal sequence modeling
  • Optimized fusion of spatial and temporal information

Advanced Feature Engineering:

  • Mel-frequency cepstral coefficients (MFCC) extraction
  • Spectral rolloff and centroid features
  • Zero-crossing rate analysis
  • Custom domain-specific feature development

Data Augmentation Strategies:

  • Time-domain augmentations (pitch shifting, time stretching)
  • Frequency-domain modifications
  • Noise injection and filtering techniques
  • Synthetic data generation methods

Research Methodology

Dataset Preparation:

  • Comprehensive audio dataset collection
  • Quality assessment and preprocessing
  • Class balancing and stratification

Feature Extraction Pipeline:

  • Multi-scale spectral analysis
  • Temporal feature characterization
  • Statistical feature computation

Model Development:

  • Architecture optimization and hyperparameter tuning
  • Cross-validation and performance evaluation
  • Comparison with baseline methods

Performance Achievements

High Accuracy: Superior classification performance compared to traditional methods

Robustness: Enhanced performance across diverse audio conditions

Generalization: Strong performance on unseen audio samples

Applications

Speech Recognition: Enhanced accuracy for spoken language processing

Environmental Sound Classification: Automated detection of environmental audio events

Music Genre Classification: Improved categorization of musical content

Audio Security: Detection of anomalous or suspicious audio patterns

Technical Contributions

Architectural Innovation: Novel combination of DCNN and LSTM for audio processing

Feature Engineering: Advanced techniques for audio feature extraction

Data Augmentation: Comprehensive strategies for improving model robustness

Research Team

Principal Investigator: Md Zesun Ahmed Mia

Collaborators:

  • Md Moinul Islam
  • Monjurul Haque
  • Saiful Islam
  • SMA Mohaiminur Rahman

Conference Publication

Successfully published in Intelligent Computing & Optimization: Proceedings of the 4th International Conference on Intelligent Computing and Optimization 2021 (ICO2021)

Publication Details: Springer, Pages 227-236

Impact and Future Directions

Research Contributions: Novel deep learning architecture for audio classification

Practical Applications: Real-world audio processing and recognition systems

Future Work:

  • Real-time audio classification systems
  • Multi-modal audio-visual processing
  • Integration with edge computing platforms
  • Applications in IoT and smart devices

This research demonstrates the effectiveness of combining multiple deep learning architectures with advanced data processing techniques to achieve superior performance in audio classification tasks.

References