Advanced Audio Processing, Lectures
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5 crCourse dates
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Advanced Audio Processing, 5 cr
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After completing this course, the student
-Can implement an audio classification system using some common programming language.
-Knows what are the most commonly used acoustic features is audio classification, understands what information they represent, and is able to select suitable acoustic features for specific audio analysis tasks.
-Knows what are the most commonly used classifiers suitable for audio classification, understands their functioning, and is able to select suitable classifier for specific audio analysis tasks.
-Understands the effect of training data and external effects (channel, noise, reverberation) on audio classification systems.
-Understands how speech recognition is formulated as a pattern classification problem.
-Can list the components of a speech recognition system, and understands the effect of each of them on the recognition performance.
-Can identify applications where source separation is used or can be used. Understands the basic techniques used in source separation and will be able to implement some source separation algorithm.
-Understands what kind of processing is enabled by a microphone array. Can implement a beamformer and a sound source localization algorithm.
Core content
- Acoustic feature extraction and audio classification. Automatic speech recognition. Use of temporal information in classification: hidden Markov models, recurrent neural networks, connectionist temporal classification, convolutional neural networks.
- 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.
Common
Lecture:
09.01.2023 12:00 - 14:00, TAU Rakennustalo RN201 auditorio (90)
13.01.2023 10:00 - 12:00, TAU Rakennustalo RH215 opetustila (70)
16.01.2023 12:00 - 14:00, TAU Rakennustalo RN201 auditorio (90)
20.01.2023 10:00 - 12:00, TAU Rakennustalo RH215 opetustila (70)
23.01.2023 12:00 - 14:00, TAU Rakennustalo RN201 auditorio (90)
27.01.2023 10:00 - 12:00, TAU Rakennustalo RH215 opetustila (70)
30.01.2023 12:00 - 14:00, TAU Rakennustalo RN201 auditorio (90)
03.02.2023 10:00 - 12:00, TAU Rakennustalo RH215 opetustila (70)
06.02.2023 12:00 - 14:00, TAU Rakennustalo RN201 auditorio (90)
10.02.2023 10:00 - 12:00, TAU Rakennustalo RH215 opetustila (70)
13.02.2023 12:00 - 14:00, TAU Rakennustalo RN201 auditorio (90)
17.02.2023 10:00 - 12:00, TAU Rakennustalo RH215 opetustila (70)
20.02.2023 12:00 - 14:00, TAU Rakennustalo RN201 auditorio (90)
24.02.2023 10:00 - 12:00, TAU Rakennustalo RH215 opetustila (70)
Groups
Group 1:
17.01.2023 10:00 - 12:00, TAU Tietotalo TC303 tietokoneluokka (16)
24.01.2023 10:00 - 12:00, TAU Tietotalo TC303 tietokoneluokka (16)
31.01.2023 10:00 - 12:00, TAU Tietotalo TC303 tietokoneluokka (16)
07.02.2023 10:00 - 12:00, TAU Tietotalo TC303 tietokoneluokka (16)
14.02.2023 10:00 - 12:00, TAU Tietotalo TC303 tietokoneluokka (16)
21.02.2023 10:00 - 12:00, TAU Tietotalo TC303 tietokoneluokka (16)
Group 2:
18.01.2023 12:00 - 14:00, TAU Tietotalo TC303 tietokoneluokka (16)
25.01.2023 12:00 - 14:00, TAU Tietotalo TC303 tietokoneluokka (16)
01.02.2023 12:00 - 14:00, TAU Tietotalo TC303 tietokoneluokka (16)
08.02.2023 12:00 - 14:00, TAU Tietotalo TC303 tietokoneluokka (16)
15.02.2023 12:00 - 14:00, TAU Tietotalo TC303 tietokoneluokka (16)
22.02.2023 12:00 - 14:00, TAU Tietotalo TC303 tietokoneluokka (16)
Prerequisites
Prerequisite
- Code: SGN-13006
- Name: Introduction to Pattern Recognition and Machine Learning
- ECTS credits: 5
- Mandatory: Mandatory
- Alternativity: Either SGN-13000 or SGN-13006.
Prerequisite
- Code: SGN-14007
- Name: Introduction to Audio Processing
- ECTS credits: 5
- Mandatory: Mandatory
- Alternativity: Either SGN-14006 or SGN-14007
Compulsory Prerequisites
- Introduction to Pattern Recognition and Machine Learning, DATA.ML.100, 5 cr
- Introduction to Audio Processing, COMP.SGN.120, 5 cr
Selected materials from book
T. Virtanen, M. D. Plumbley, D. Ellis (eds). Computational Analysis of Sound Scenes and Events. Springer, 2018.
Selected sections from article
H. Purwins , B. Li , T. Virtanen , J. Schlüter , S.-Y. Chang, and T. Sainath. Deep Learning for Audio Signal Processing. IEEE Journal of Selected Topics in Signal Processing, volume 13, issue 2, 2019.
General scale, 0-5
Contact information
Email: open.studies.tau [at] tuni.fi
Phone: 0294 520 200
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