Contents

In this lecture the basics of speech, audio, and music signal processing are treated.

You will learn a lot about fundamentals as well as about recent research in this interesting field of signal processing.

The topics of the lecture are:

• 1. Introduction to the properties of speech and audio signals

• 2. Basic methods of audio signal processing

• • a. Frequency analysis methods
  • b. Estimation of the auto correlation functions (ACF) and power spectral density functions (PSD)
  • c. Tracking and detection methods including computational efficient calculation methods
  • d. Measurements of room impulse responses and reverberation times
  • e. Typically used filter methods with corresponding design methods

• 3. Methods for codebook processing

• • a. Principle
  • b. Training of codebooks
  • c. Efficient codebook search

• 4. Audio coding: prediction coding, line spectral frequencies (LSF), code excited linear prediction (CELP)

• 5. Noise reduction and beamforming

• 6. Cepstral processing as one important tool of speech processing, Example: Cepstral smoothing for noise reduction to avoid the “Musical Tones” problem

• 7. Methods for pitch frequency calculation and applications

• 8. “Mel frequency cepstral coefficients” (MFCC) as one important feature analysis in speech processing

• 9. Speaker detection based on MFCCs combined with LDA (linear discriminant anaylsis) and PCA (principle component analysis)

• 10. Hidden Markov Models (HMM)

• 11. Speech regognition

• 12. Acoustic classification methods: Bayes methods, Gaussians mixture models (GMM), etc.

• 13. Music signal processing, e.g. beat detection