Open for Enrollment

Starts on Sep 29, 2020

You can also start immediately after joining!
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Go at your own pace
5 Sessions / 10 hours of work per session
Price
Free
Included w/ premium membership ($20/month)
Skill Level
Expert
Video Transcripts
English
Topics
Music, Machine Learning, Music Information Retrieval, Audio Signal Processing, Feature Extraction
Open for Enrollment

Extracting Information From Music Signals

Starts on Sep 29, 2020
You can also start immediately after joining!

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Go at your own pace
5 Sessions / 10 hours of work per session
Price
Free
Included w/ premium membership ($20/month)
Skill Level
Expert
Video Transcripts
English
Topics
Music, Machine Learning, Music Information Retrieval, Audio Signal Processing, Feature Extraction
Course Description

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.

schedule

This course is in adaptive mode and is open for enrollment. Learn more about adaptive courses here.

Session 1: Time, Frequency, and Sinusoids (September 29, 2020)
In this session, we will cover Phasors, Sinusoids, and Complex Numbers.
11 lessons
1. Welcome
2. About, Background, and Learning Outcomes
3. MIR History and Tasks
4. Importance of DSP, Digital Audio Recordings and Time Domain Waveforms, Sampling and Quantization
5. Pitch, Time, Music Notation, and Time-Frequency Representations
6. Spectrum and Spectrograms
7. Sinusoids
8. Sound of Tuning Fork, Physics of Sound Projection, LTI Systems (Premium Exclusive)
9. Measuring Amplitude, Frequency and Phase of Sinusoids (Premium Exclusive)
10. Phasors and Complex Numbers (Premium Exclusive)
11. DSP Concepts Using Phasors (Premium Exclusive)
Session 2: DFT and Time-Frequency Representations (October 6, 2020)
In This session, we will learn about Sampling, Quantization, RMS, and Loudness. We will also cover DFT, Hilbert Spaces, and Spectrograms.
10 lessons
1. Welcome and Overview
2. A Geometric View of Frequency Representations
3. Fourier Series
4. The Discrete Fourier Transform and the FFT
5. Understanding the Basis Functions, Magnitude and Phase Spectrum
6. Plotting the Spectrum and Interpreting it (Premium Exclusive)
7. Windowing, The Short-Time Fourier Transform, and Spectrograms (Premium Exclusive)
8. Filters
9. Amplitude in dB, Loudness (Premium Exclusive)
10. Summary
Session 3: Monophonic Pitch Detection (October 13, 2020)
Pitch vs Fundamental Frequency, Time-domain, Frequency-domain, Perceptual Models, Overview of applications (Query-by-Humming, Auto-tunining) will be covered in this session.
8 lessons
1. Welcome and Overview
2. Pitch and Fundamental Frequency
3. Time-Domain Pitch Extraction Using Zero-Crossings
4. Frequency-Domain Pitch Extracting Using Magnitude Spectra
5. Autocorrelation and Average Magnitude Difference Function (Premium Exclusive)
6. Perceptually Informed Hearing Models
7. Query-by-Humming
8. Auto-Tuning (Premium Exclusive)
Session 4: Audio Feature Extraction (October 20, 2020)
We will go over Spectral Features, Mel-Frequency Cepstral Coefficients, temporal aggregation, chroma and pitch profiles.
8 lessons
1. Welcome and Overview
2. State Space Representations for Music Tracks
3. Introduction to Audio Features
4. Frequency and Temporal Summarization
5. Spectral Descriptors and MFCCs (Premium Exclusive)
6. Temporal Summarization (Premium Exclusive)
7. Pitch Histograms and Chroma Vectors
8. Summary
Session 5: Rhythm Analysis (October 27, 2020)
This session is about Tempo estimation, beat tracking, drum transcription, pattern detection.
8 lessons
1. Overview
2. Rhythm Analysis Terminology
3. Tempo Estimation
4. Beat Tracking
5. Beat Strength and Rhythm Features (Premium Exclusive)
6. Drum Transcription and Pattern Analysis (Premium Exclusive)
7. Multi-Modal Real-Time Beat Tracking
8. Summary
Reviews
Learning Outcomes

Below you will find an overview of the Learning Outcomes you will achieve as you complete this course.

Instructors & Guests
What You Need to Take This Course

Prior Knowledge

  • Good knowledge of programming, basic linear algebra, probability, and statistics.

Equipment

  • Computer with installation privileges.

Software

  • The course is mostly software agnostic but existing frameworks for MIR and audio will be used. All software will be freely available and typically also open source. Examples include: Audacity, Marsyas, Sonic Visualizer, and VAMP plugins.
Additional Information

PLEASE NOTE: Taking part in a Kadenze course as a Premium Member does not affirm that you have been enrolled or accepted for enrollment by the institution offering this course.

In order to receive college credit for these program courses, you must successfully complete and pass all 3 courses in this program. If a student signs up for the Music Information Retrieval program, it is recommended that these courses are taken sequentially.

*Partial credit will not be awarded for completion of only one course.

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