In this lecture the basics of speech, audio, and music signal processing are treated. Often schemes that are based on statistical optimization are utilized for these applications. The involved cost function are matched to the human audio perception.

Topic overview:

• Preprocessing to reduce signal distortions
  • Noise reduction
  • Beamforming
• Feature extraction and data compression
• Pattern recognition and data regression
  • Learning types
    • Unsupervised learning
    • Supervised learning
    • Reinformcement learning
  • Codebooks
  • Object-to-vector conversion
  • Gaussian mixture models (GMMs)
  • Artificial Neural Networks (ANNs)
    • Multilayer perceptron
    • Convolutional neuronal networks (CNNs)
    • Recurrent neural networks (RNNs)
    • Autoencoder networks
    • Generative adversarial networks (GANs)
  • Hidden Markov models (HMMs)
  • Explainable artificial intelligence (EAI)

Noise suppression:

• Wiener Filter summary
• comprehension questions
• python demo

Beamforming:

• beamforming summary
• comprehension questions
• python demo

Feature extraction:

• linear prediction
• LPC, LPCC, MFCC features
• comprehension questions
• python demo

Codebook training:

• codebooks
• codebook training
• comprehension questions
• python demo

Bandwidth extension:

• bandwidth extension summary
• comprehension questions
• python demo

Gaussian Mixture Models:

• Gaussian Mixture Models
• EM algorithm
• comprehension questions
• python demo

Neural Networks:

• neural networks summary
• comprehension questions
• python demo

Hidden Markov Models:

• hidden markov models summary
• comprehension questions
• python demo

Speaker and Speech Recognition:

• speaker and speech recognition summary
• comprehension questions
• python demo