Course Description
To audit the course, please email vishalku@
This course aims at introducing the students to machine learning (ML) techniques used for various signal processing applications. There will be spectral processing techniques for analysis and transformation of audio signals. The lectures will focus on mathematical principles, and there will be coding based assignments for implementation. Prior exposure to ML is not required. The course will be focused on applications in audio signal processing, and the theory will be tailored towards that end.
Grading Scheme
- Continuous Assessment – 50% (5 best out of 6)
Assignments (coding and theory), Quizzes - Mid-semester Exam – 20%
Written and/or oral exam - End-semester Exam – 30%
Written exam and/or project submission
There may be oral exams or viva via video chat or phone call.
Plagiarism Penalty:
As heavy as possible. Zero-tolerance policy.
Course Content
- Linear Algebra Refresher
- Programming Basics: Python and bash scripting
- Digital Signal Processing for audio
- Probability Theory Refresher
- Machine Learning basics
- Neural Networks
- Music Information Retrieval
- Speech Recognition
- Other applications in audio processing: E.g., acoustic event detection, speaker diarization, music genre classification, auto-tagging, query by humming, melody estimation, etc.
References:
This course will take excerpts from some standard books on machine learning and signal processing. But it will largely be based on articles and research papers in ML and SP conferences (e.g., ICASSP, NeurIPS, ICML, Interspeech, ISMIR, etc.) and journals (e.g., IEEE TASLP, JMLR, IEEE PAMI, etc.).
Books:
- "Pattern Recognition and Machine Learning", C.M. Bishop, 2nd Edition, Springer, 2011.
- "Deep Learning", I. Goodfellow, Y, Bengio, A. Courville, MIT Press, 2016.
- "An Introduction to Audio Content Analysis", A. Lerch, Wiley-IEEE Press, 2012.
- "Speech and audio signal processing: processing and perception of speech and music", B. Gold, N. Morgan, D. Ellis, Wiley, 2011
- "Automatic Speech Recognition: A Deep Learning Approach", D. Yu and L. Deng, Springer, 2016.
- "Signal Processing Methods for Music Transcription", A. Klapuri and M. Davy, Springer, 2007.
Articles:
- Hendrik Purwins, Bo Li, Tuomas Virtanen, Jan Schlüter, Shuoyiin Chang, Tara Sainath. "Deep Learning for Audio Signal Processing", in Journal of Selected Topics of Signal Processing, Vol. 13, No. 2, May 2019, pages 206–219.
- Preeti Rao. "Audio signal processing", Speech, Audio, Image and Biomedical Signal Processing using Neural Networks. Springer, Berlin, Heidelberg, 2008. 169-189.
Course Audience
Pre-requisites:
- Digital signal processing (EE301A or equivalent)
- Basics of Programming (ESc101 or equivalent)
The course will need a strong background in linear algebra and probability theory.