Not available for purchase in India
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Not available for purchase in India
The course introduces audio signal processing concepts motivated by examples from MIR research. More specifically students will learn about spectral analysis and time-frequency representations in general, monophonic pitch estimation, audio feature extraction, beat tracking, and tempo estimation.
Below you will find an overview of the Learning Outcomes you will achieve as you complete this course.
• Understanding of the ideas, notation and intuition behind the short-time Fourier Transform (STFT) arguably the most fundamental technique in audio signal processing.
• Understanding of the general concept of a time-frequency representations and how audio features are computed from such representations.
• Ability to discuss how spectral analysis and audio features are used in MIR tasks such as audio classification, tagging, and recommendation.
• Understanding of various types of monophonic pitch detection algorithms based on time-domain, frequency-domain and perceptual modeling.
• Ability to illustrate how pitch detection can be used in applications such as query-by-humming and auto-tuning.
• Understanding of the terminology used to characterize rhythm in music as well as concepts used in rhythm analysis by computers such as onsets, onset strength function, and inter-onset intervals.
• Understanding of the fundamental ideas behind rhythm related MIR tasks such as tempo estimation, beat tracking, rhythm features, swing analysis, and drum transcription.
George Tzanetakis is a Professor in the Department of Computer Science with cross-listed appointments in ECE and Music at the University of Victoria, Canada. He is the Canada Research Chair (Tier II) in the Computer Analysis of Audio and Music and received the Craigdarroch research award in artistic expression at the University of Victoria in 2012. In 2011 he was Visiting Faculty at Google Research. He received his PhD in Computer Science at Princeton University in 2002 and was a Post-Doctoral fellow at Carnegie Mellon University in 2002-2003. His research spans all stages of audio content analysis such as feature extraction, segmentation, classification with specific emphasis on music information retrieval. He is also the primary designer and developer of Marsyas an open source framework for audio processing with specific emphasis on music information retrieval applications. His pioneering work on musical genre classification received a IEEE signal processing society young author award and is frequently cited. More recently he has been exploring new interfaces for musical expression, music robotics, computational ethnomusicology, and computer-assisted music instrument tutoring.
Prior Knowledge
Equipment
Software
PLEASE NOTE: Taking part in a Kadenze course as a Premium Member does not affirm that you have been enrolled or accepted for enrollment by the institution offering this course.
In order to receive college credit for these program courses, you must successfully complete and pass all 3 courses in this program. If a student signs up for the Music Information Retrieval program, it is recommended that these courses are taken sequentially.
*Partial credit will not be awarded for completion of only one course.
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