MS Thesis Proposal by Chi-Wei Wu

Event Details
  • Date/Time:
    • Tuesday January 10, 2017
      9:00 am - 11:00 am
  • Location: Couch Building (840 McMillan Street) Room 104
  • Phone:
  • URL:
  • Email:
  • Fee(s):
    N/A
  • Extras:
Contact
No contact information submitted.
Summaries

Summary Sentence: Extendable Learning for Automatic Music Transcription — Improving Automatic Drum Transcription with Labeled and Unlabeled Data

Full Summary: No summary paragraph submitted.

Title: 

Extendable Learning for Automatic Music Transcription — 

Improving Automatic Drum Transcription with Labeled and Unlabeled Data

 

 

Abstract:

Automatic drum transcription is a sub-task of Automatic Music Transcription (AMT) that involves the conversion of drum-related audio events, such as drum onset times and playing techniques, into musical notations. While noticeable progress has been made in the past by combining the pattern recognition methods with audio signal processing techniques, the major limitation of many state-of-the-art systems still originate from the difficulty of obtaining a meaningful amount of labeled data to support the data-driven algorithms. To address the challenge of insufficient labeled data, this work attempts to explore the possibility of utilizing unlabeled music data from online resources. Specifically, this work focuses on improving the drum transcription systems with two proposed methods. The first method is based on the concept of distillation, which improves the performance of a model by creating an ensemble system followed by a model compression process with the unlabeled data. The second method is based on the concept of self-taught learning, which learns appropriate feature representations from unlabeled data in order to achieve improvements. The contributions of this work will be: first, it will provide insights into the viability of leveraging easily accessible unlabeled music data for drum transcription. Second, it will enable a scheme for data-driven algorithms to benefit from the unlimited online resources and thus might have impact on many other audio and music related analysis tasks traditionally impeded by small amounts of labeled data. Last but not least, the resulting system could improve the state-of-the-art in automatic drum transcription

 

Committee members:

Dr. Alexander Lerch

Dr. Jason Freeman 

Dr. Mark Clements (ECE) 

 

Additional Information

In Campus Calendar
No
Groups

Graduate Studies

Invited Audience
Public
Categories
Other/Miscellaneous
Keywords
MS Proposal
Status
  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Jan 3, 2017 - 7:31am
  • Last Updated: Jan 3, 2017 - 7:31am