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COMP.SGN.220
Advanced Audio Processing, 5 cr
Tampere University
- Description
- Completion options
Teaching periods
Active in period 3 (1.1.2025–2.3.2025)
Active in period 4 (3.3.2025–31.5.2025)
Course code
COMP.SGN.220Language of learning
EnglishAcademic years
2024–2025, 2025–2026, 2026–2027Level of study
Advanced studiesGrading scale
General scale, 0-5Persons responsible
Responsible teacher:
Tuomas VirtanenResponsible teacher:
Annamaria MesarosResponsible organisation
Faculty of Information Technology and Communication Sciences 100 %
Coordinating organisation
Computing Sciences Studies 100 %
Content
Core content
- Acoustic feature extraction and audio classification. Machine learning for audio classification: recurrent neural networks, connectionist temporal classification, convolutional neural networks. Environmental sound classification, music signal analysis and classification, spatial audio
- Source separation (one channel and multichannel). Time-frequency masking. Deep neural network based and spectrogram factorization based source separation techniques.
- Microphone array signal processing: beamforming, source localization and tracking.
Learning outcomes
Prerequisites
Compulsory prerequisites
Further information
Learning material
Equivalences
Kokonaisuudet, joihin opintojakso kuuluu
Completion option 1
Accepted exercises, project work, and exam.
All parts of the completion option are compulsory.
Participation in teaching
07.01.2025 – 27.02.2025
Active in period 3 (1.1.2025–2.3.2025)