Enrollment Closed

Open for Enrollment (In Development)

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Scheduled
5 Sessions / 10 hours of work per session
Price
Free
Included w/ premium membership ($20/month)
Skill Level
Expert
Topics
Music, Machine Learning, Music Information Retrieval, Audio Signal Processing, Feature Extraction
Open For Enrollment

Extracting Information From Music Signals

 Extracting Information From Music Signals
Open for Enrollment (In Development)

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Scheduled
5 Sessions / 10 hours of work per session
Price
Free
Included w/ premium membership ($20/month)
Skill Level
Expert
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 scheduled mode. Learn more about scheduled courses here.

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.
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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