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NM7613: Music Signal Analysis and Retrieval 音樂訊號分析與檢索 Jyh-Shing Roger Jang ( 張智星 ) CSIE Dept, National Taiwan University.

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Presentation on theme: "NM7613: Music Signal Analysis and Retrieval 音樂訊號分析與檢索 Jyh-Shing Roger Jang ( 張智星 ) CSIE Dept, National Taiwan University."— Presentation transcript:

1 NM7613: Music Signal Analysis and Retrieval 音樂訊號分析與檢索 Jyh-Shing Roger Jang ( 張智星 ) CSIE Dept, National Taiwan University

2 2 Course Objectives Objectives of Music Signal Analysis and Retrieval (MSAR) Analysis part: most for music feature extraction, including pitch tracking, beat tracking, onset detection, etc. Retrieval part: machine learning techniques for query by singing/humming, genre classification, mood classification, cover song identification, audio fingerprinting, scoring following, etc. Corpus collection: For analysis and modeling MATLAB programming: implementation of practical music retrieval systems (potentially for MIREX contests) Prerequisites Elementary calculus Linear algebra Probability

3 3 Instructor & TAs Instructor J.-S. Roger Jang 張智星 Email: jang@mirlab.orgjang@mirlab.org Skype: roger_jang Mobile: 0953-154-045 Office: 509 Office hours: After class or by appointments TAs Yenjung Tung ( 董晏儒 ), yenjung.tung@mirlab.orgyenjung.tung@mirlab.org Andy Lai ( 賴彥麟 ), andy.lai@mirlab.organdy.lai@mirlab.org Demo hours to be decided

4 4 Important Links Important links for MSAR MSAR on Facebook https://www.facebook.com/groups/601006016641298/ Course websites: Roger: http://mirlab.org/jang/courses/msarhttp://mirlab.org/jang/courses/msar

5 5 Course Material Textbooks (mostly available online) Audio Signal Processing and Recognition Data Clustering and Pattern Recognition Reference material MATLAB resources

6 6 Lecture Formats Will try a variety of lecture formats Normal lectures: 2~3 hours per week Flip learning: take-home reading Quiz: almost every week Assignments: mostly programming based, demo required Final project: for a team of 3 or so Feel free to let me know if you think of anything that can enhance effective learning

7 7 Grading Policy Percentages (subject to final change) Course participation: 10% Each in-class technical question asked: +2% (10% top) Interactions with TAs and fellow students (over FB, CEIBA, etc.) Quiz: 15% About 10 assignments: 25% 3~4 Programming contests: 25% Final project: 25% The final grades are based on both scores and rankings The instructors reserve the rights to Adjust percentages of each categories Determine the way to combine scores and rankings

8 8 Ask, Ask, and Ask Ask questions Will you repeat the previous code/slide? Yes! Will you discuss with me after class if I don’t understand? Yes! Will you pardon my silly questions? There is no silly questions at all! Can I raise my question in Mandarin? Of course, use whatever languages you feel comfortable! Feel free to ask the instructors and give feedbacks!

9 9 About Enrollment Extra enrollment Priority CSIE > others PhD > Master > Bachelor Senior > Junior You’ll be admitted if you are persistent! Auditing is also welcome. ( 歡迎旁聽! )

10 10 Can and Cannot Rules in the classroom Eating? Fine, but no smells and no noise Sleeping? Fine, but no snoring Cellphone? Fine, but use silent mode and speak outside

11 11 Other Requirements The following items will make your journey smooth A fast PC/notebook with Microphone Speakers Software MATLAB Audacity/GoldenWave/CoolEdit (or the likes) A heart willing to explore and experiment with sound & music

12 12 Todo List Update your secondary email address on CEIBA Make sure you understand the policy, rules, etc Prepare the required items for the class. (We shall cover MATLAB programming next week.) Welcome aboard! Questions? Welcome aboard! Questions?


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