Kadenze
Free Online Course
English
Paid Certificate Available
selfpaced
Overview
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.
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.
Syllabus
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.
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.
Taught by
George Tzanetakis
