Faculty of Information Technology and Communication Sciences
Language of instruction
Technology and Natural Sciences
Mode of study
Advanced Audio Processing, 5 cr
Language of instruction: English
This course is an advanced source and audio and speech signal processing, focusing on algorithms that can be used to automatically analyze and classify audio signals, and to do advanced processing to them.
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.
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.
Name: Introduction to Pattern Recognition and Machine Learning
ECTS credits: 5
Alternativity: Either SGN-13000 or SGN-13006.
Name: Introduction to Audio Processing
ECTS credits: 5
Alternativity: Either SGN-14006 or SGN-14007
Introduction to Pattern Recognition and Machine Learning, DATA.ML.100, 5 cr
Introduction to Audio Processing, COMP.SGN.120, 5 cr