University of Victoria Courses

Music Information Retrieval

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Ability to critically read, understand, and implement algorithms and systems described in research publications at the International Conference of the Society for Music Information Retrieval (ISMIR) and other peer-reviewed journals and conferences.Understanding of the wide diversity of evaluation metrics and methodologies required to develop effective music information retrieval software systems.Ability to integrate interdisciplinary knowledge in the process of developing a non-trivial potentially collaborative project.

Extracting Information From Music Signals

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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.Session 1: Overview And Introduction To DSP In this session, we will cover Phasors, Sinusoids, and Complex Numbers.Session 2: Time-Frequency Representations In This session, we will learn about Sampling, Quantization, RMS, and Loudness. We will also cover DFT, Hilbert Spaces, and Spectrograms.Session 3: Monophonic Pitch Analysis/Autocorrelation Pitch vs Fundamental Frequency, Time-domain, Frequency-domain, Perceptual Models, Overview of applications (Query-by-Humming, Auto-tunining) will be covered in this session.Session 4: Audio Feature Extraction We will go over Spectral Features, Mel-Frequency Cepstral Coefficients, temporal aggregation, chroma and pitch profiles.Session 5: Rhythm Analysis This session is about Tempo estimation, beat tracking, drum transcription, pattern detection.

Music Data Mining

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An introduction to data mining through the lens of music information retrieval. Topics explored include classification (genre, mood, instrument), multi-label classification (tagging), and regression (emotion/mood).

Machine Learning for Music Information Retrieval

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An introduction to data mining through the lens of music information retrieval. Topics explored include classification (genre, mood, instrument), multi-label classification (tagging), and regression (emotion/mood).