audio content analysis | music information retrieval tasks and applications

These video lectures relate to edition 1 of the book, meaning that they will be differently structured and differ in content details. The lectures are an introduction to the software-based analysis of digital music signals (Music Information Retrieval) for students with existing background in audio processing. It covers the basic approaches for audio content analysis and provides students with the necessary algorithmic background to approach this class of problems. Topics include, for example, pitch tracking, beat tracking, audio feature extraction, and genre classification.

prerequisites

Prior coursework in signal processing is expected – the fundamentals module of the class will introduce concepts in detail. Familiarity with and access to Matlab or Octave is required.

learning outcomes

After successful completion of this class, the students will be able to

summarize and explain baseline approaches to typical tasks in Music Information Retrieval
describe and apply evaluation methods and metrics for audio content analysis systems,
implement audio content analysis systems in Matlab.

course outline

Module 0.0: Introduction to Online Class

chapter 1: introduction

Module 1.0: Introduction to Music Information Retrieval

chapter 2: fundamentals

Module 2.0: Fundamentals – Signals
Module 2.1: Fundamentals – Sampling
Module 2.2: Fundamentals – Quantization
Module 2.3: Fundamentals – Convolution
Module 2.4: Fundamentals – Blocking
Module 2.5: Fundamentals – Fourier Transform
Module 2.6: Fundamentals – Alternative Frequency Transforms
Module 2.7: Fundamentals – Correlation

chapter 3: instantaneous features

Module 3.0: Feature Intro and Pre-Processing
Module 3.1: Statistical Features
Module 3.2: Spectral Features
Module 3.3: Additional Features
Module 3.4: Feature Post-Processing
Module 3.5: Feature Dimensionality Reduction

chapter 4: intensity & loudness

Module 4.0: Intensity and Loudness

chapter 5: tonal analysis

Module 5.0: Pitch Perception
Module 5.1: Pitch in Music
Module 5.2: Monophonic Fundamental Frequency Detection
Module 5.3: Instantaneous Frequency
Module 5.4: Polyphonic Fundamental Frequency Detection
Module 5.5: Polyphonic Pitch Tracking with NMF
Module 5.6: Tuning Frequency Estimation
Module 5.7: Musical Key Recognition
Module 5.8: Chord Detection

chapter 6: temporal analysis

Module 6.0: Terminology for Temporal Events
Module 6.1: Onset Detection
Module 6.2: Tempo and Beat Detection
Module 6.3: Bar length and Downbeat Detection
Module 6.4: Rhythm Description

chapter 7: audio alignment

Module 7.0: Dynamic Time Warping
Module 7.1: Audio-to-Audio/Score Alignment

chapter 8: music genre classification, similarity, mood

Module 8.0: Classifiers
Module 8.1: Music Genre Classification
Module 8.2: Music Similarity
Module 8.3: Mood Recognition

chapter 9: audio fingerprinting

Module 9.0: Audio Fingerprinting

get the book

Book Cover Image: An Introduction to Audio Content Analysis

@ IEEE
@ Wiley

get the code

matlab
python
C++

get the slides

get the plots

get in touch

alexander lerch

support this resource