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Kadenze

Extracting Information From Music Signals

University of Victoria via Kadenze

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Overview

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The course introduces audio signal processing concepts motivated by examples from MIR research. More specifically students will learn about spectral analysis and time-frequency representations in general, monophonic pitch estimation, audio feature extraction, beat tracking, and tempo estimation.

Syllabus

  • DFT and Time-Frequency Representations
    • In This session, we will learn about Sampling, Quantization, RMS, and Loudness. We will also cover DFT, Hilbert Spaces, and Spectrograms.
  • Monophonic Pitch Detection
    • Pitch vs Fundamental Frequency, Time-domain, Frequency-domain, Perceptual Models, Overview of applications (Query-by-Humming, Auto-tunining) will be covered in this session.
  • Time, Frequency, and Sinusoids
    • In this session, we will cover Phasors, Sinusoids, and Complex Numbers.
  • Rhythm Analysis
    • This session is about Tempo estimation, beat tracking, drum transcription, pattern detection.
  • Audio Feature Extraction
    • We will go over Spectral Features, Mel-Frequency Cepstral Coefficients, temporal aggregation, chroma and pitch profiles.

Taught by

George Tzanetakis

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