Computational Analysis of Sound and Music (CASM) - Interactive curriculae of TU Ilmenau
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You can find all details on planned lectures and classes in the course catalogue.
Please note that this page is no longer updated. All modules and study plans from PO version 2021 onwards (Bachelor and Master study programs) are now available on the Campus Portal.
| module properties module number 201260 - common information | |
|---|---|
| module number | 201260 |
| department | Department of Electrical Engineering and Information Technology |
| ID of group | 2182 (Audiovisual Technology) |
| module leader | Prof. Dr. Alexander Gerd Raake |
| language | Englisch |
| term | Sommersemester |
| previous knowledge and experience | Basic knowledge of audio signal processing, machine learning, statistics, linear algebra, and Python programming |
| learning outcome | After the lectures students - can explain different perceptual audio attributes- are able to differentiate between different audio domains such as speech, music, and environmental sounds - have gained knowledge about different audio and time-frequency representations - are able to explain and implement individual steps of a common machine learning model lifecycle - have gained knowledge about common deep neural network architectures for audio analysis - are able to explain research objectives, challenges, and common approaches for different music and environmental audio analysis tasks - have demonstrated their practical knowledge in solving a selected audio analysis task by implementing, training, and evaluating a deep neural network and presenting the research results as a scientific publication and presentation |
| content | Foundations of Audio Processing (audio signals and domains, audio and time-frequency representations, sound perception, perceptual audio features) Foundations of Deep Learning (data representation & processing, model training and evaluation, selected neural network architectures)
|
| media of instruction and technical requirements for education and examination in case of online participation | Presentation, Moodle, Audio Examples, Python examples in Jupyter Notebook Environment, Laptop with access to https://colab.google/ and headphones required for all lectures |
| literature / references | Goodfellow, I., Bengio, Y., and Courville, A: Deep Learning, MIT Press, 2016. Virtanen, T., Plumbley, Mark D., and Ellis, D.: Computational Analysis of Sound Scenes and Events, Springer, 2018.Müller, M.: Fundamentals of Music Processing Using Python and Jupyter Notebooks, Springer, 2021.Müller, M.: Fundamentals of Music Processing - Notebooks (https://www.audiolabs-erlangen.de/FMP)Müller, M.: Preparation Course Python Notebooks (https://www.audiolabs-erlangen.de/resources/MIR/PCP/PCP.html)scikit-learn (https://scikit-learn.org/stable/)librosa (https://librosa.org/doc/latest/index.html) |
| evaluation of teaching | |
| Details reference subject | |
|---|---|
| module name | Computational Analysis of Sound and Music (CASM) |
| examination number | 210550 |
| credit points | 5 |
| SWS | 4 (2 V, 2 Ü, 0 P) |
| on-campus program (h) | 45 |
| self-study (h) | 105 |
| obligation | obligatory module |
| exam | examination performance with multiple performances |
| details of the certificate | Das Modul Computational Analysis of Sound and Music (CASM) mit der Prüfungsnummer 210550 schließt mit folgenden Leistungen ab:
Details zum Abschluss Teilleistung 1: Research Project During the practical phases in the last 4 weeks of the lectures, small teams of students will work on a selected audio analysis task, conduct a literature and dataset research, and implement in Python all required steps ranging from the importing and processing of audio data to the implementation, training, and evaluation of suitable deep neural network architectures. This group work will be performed during practical phases in the lectures in the last 4 semester weeks. Student groups will document their project results in a short scientific paper and give a final project presentation in the final lecture. Details zum Abschluss Teilleistung 2: Written exam consisting of multiple-choice and free-form questions to evaluate the professional competences in the course topics |
| link to Moodle course | |
| teacher | Dr. Jakob Abeßer |
| signup details for alternative examinations | Dieses Modul enthält mindestens eine alternative semesterbegleitende Abschlussleistung. Bitte beachten Sie, dass diese in der Regel schon zu Beginn des Semesters, in dem diese angeboten wird, angemeldet werden muss. This module contains at least one alternative exam part. Please note that this must usually be registered at the beginning of the semester in which it is offered. |
| maximum number of participants | |
| Details in degree program Master Medieningenieurwissenschaften 2023 | |
|---|---|
| module name | Computational Analysis of Sound and Music (CASM) |
| examination number | 210550 |
| credit points | 5 |
| on-campus program (h) | 45 |
| self-study (h) | 105 |
| obligation | elective module |
| exam | examination performance with multiple performances |
| details of the certificate | Das Modul Computational Analysis of Sound and Music (CASM) mit der Prüfungsnummer 210550 schließt mit folgenden Leistungen ab:
Details zum Abschluss Teilleistung 1: Research Project During the practical phases in the last 4 weeks of the lectures, small teams of students will work on a selected audio analysis task, conduct a literature and dataset research, and implement in Python all required steps ranging from the importing and processing of audio data to the implementation, training, and evaluation of suitable deep neural network architectures. This group work will be performed during practical phases in the lectures in the last 4 semester weeks. Student groups will document their project results in a short scientific paper and give a final project presentation in the final lecture. Details zum Abschluss Teilleistung 2: Written exam consisting of multiple-choice and free-form questions to evaluate the professional competences in the course topics |
| link to Moodle course | |
| signup details for alternative examinations | Dieses Modul enthält mindestens eine alternative semesterbegleitende Abschlussleistung. Bitte beachten Sie, dass diese in der Regel schon zu Beginn des Semesters, in dem diese angeboten wird, angemeldet werden muss. This module contains at least one alternative exam part. Please note that this must usually be registered at the beginning of the semester in which it is offered. |
| maximum number of participants | |