Open University

Tampere

Advanced Audio Processing, Lectures

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Advanced studies

Faculty of Information Technology and Communication Sciences

Technology and Natural Sciences

<p>Apply for Open University studies by using the following links:</p><p></p><ul><li><a href="https://tau-avoin.ilmoittaudu.fi/lobby/?realization=J-otm-ad7d3e11-e4cd-49ff-9f75-1234c98232d4_2023-tampere-COMPSGN220" target="_blank">COMP.SGN.220 Advanced Audio Processing, lectures, Tampere</a></li></ul><p>You can find a link to the Open University Admissions service and application instructions on the <a href="https://www.tuni.fi/en/study-with-us/open-higher-education-studies/open-university/applying-open-university">Applying to Open University studies</a> page.</p><p>Some of the studies require prior studies. For example, studying intermediate studies may require some completed basic studies. These are described on the Information about studies tab of the study unit under prior requirements.</p><p>When you have been admitted to the studies, you must enrol separately via the Sisu information system for the implementation of the unit of study and, for example if set, for a practise group of your choice. You will receive instructions on how to do this by e-mail after you have confirmed your acceptance of the study place by paying the required fee.</p>

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Hervanta Campus

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<h5>Core content</h5><ul><li>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.</li><li>Source separation (one channel and multichannel). Time-frequency masking. Deep neural network based and spectrogram factorization based source separation techniques.</li><li>Microphone array signal processing: beamforming, source localization and tracking.</li></ul>

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.<br><br>After completing this course, the student <br>-Can implement an audio classification system using some common programming language.<br>-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.<br>-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.<br>-Understands the effect of training data and external effects (channel, noise, reverberation) on audio classification systems.<br><br>-Understands how speech recognition is formulated as a pattern classification problem.<br>-Can list the components of a speech recognition system, and understands the effect of each of them on the recognition performance. <br><br>-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.<br><br>-Understands what kind of processing is enabled by a microphone array. Can implement a beamformer and a sound source localization algorithm.

<h2>Prerequisites</h2><h5>Prerequisite</h5><ul><li>Code: SGN-13006</li><li>Name: Introduction to Pattern Recognition and Machine Learning</li><li>ECTS credits: 5</li><li>Mandatory: Mandatory</li><li>Alternativity: Either SGN-13000 or SGN-13006.</li></ul><h5>Prerequisite</h5><ul><li>Code: SGN-14007</li><li>Name: Introduction to Audio Processing</li><li>ECTS credits: 5</li><li>Mandatory: Mandatory</li><li>Alternativity: Either SGN-14006 or SGN-14007</li></ul><h2>Compulsory Prerequisites</h2><ul><li>Introduction to Pattern Recognition and Machine Learning, DATA.ML.100, 5 cr</li><li>Introduction to Audio Processing, COMP.SGN.120, 5 cr</li></ul>

<p>Selected materials from book</p><p>T. Virtanen, M. D. Plumbley, D. Ellis (eds). <a href="http://www.springer.com/us/book/9783319634494">Computational Analysis of Sound Scenes and Events.</a> Springer, 2018.</p><p>Selected sections from article</p><p>H. Purwins , B. Li , T. Virtanen , J. Schlüter , S.-Y. Chang, and T. Sainath. <a href="https://arxiv.org/abs/1905.00078">Deep Learning for Audio Signal Processing</a>. IEEE Journal of Selected Topics in Signal Processing, volume 13, issue 2, 2019.</p>

Advanced Audio Processing, Lectures

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