Extracting Information From Music Signals

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.

• 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.