Machine Listening


Jakob Abesser,


Fraunhofer IDMT

Content and organization

This short course includes 6 lectures on the topic of Machine Listening.
After two foundation lectures on audio signal representations and machine learning / deep learning, the course will provide an in-depth overview with 2 lectures on each of the application scenarios music information retrieval (music tagging, music similarity, tempo estimation, music transcription, instrument recognition, source separation) and environmental sound analysis (acoustic scene classification, sound event detection, and acoustic anomaly detection).
A special focus will be on deploying machine listening algorithms in real-life scenarios.

Course material will be soon provided on the accompanying website

Interested AIDA students can send an email with the subject “Enroll to Machine Listening Lecture” to until “Enroll on this course button” becomes operational.

Lecture 1: Introduction to Audio Representations.

Lecture 2: Introduction to Machine Learning.

Lecture 3: Music Information Retrieval 1/2.

Lecture 4: Music Information Retrieval 2/2.

Lecture 5: Environmental Sound Analysis 1/2.

Lecture 6: Environmental Sound Analysis 2/2.


Graduate student course

Course Duration

Short course

Course Type

AI PhD Curriculum

Lecture Plan

6 lectures + 4 seminars


Winter semester



Modality (online/in person):


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