School of Music

Candidate:

Chih-Wei Wu

PhD in Music Technology

Date: Tuesday, May 8th, 2018

Time: 11:00am to 1:00pm

Location: West Village room 163 (532 8th St NW, Atlanta, GA 30318)

Title:

Addressing the data challenge in automatic drum transcription with labeled and unlabeled data

Abstract:

Automatic Drum Transcription (ADT) is a sub-task of automatic music transcription that involves the conversion of drum-related audio events into musical notations. While noticeable progress has been made in the past by combining pattern recognition methods with audio signal processing techniques, many systems are still impeded by the lack of a meaningful amount of labeled data to support the data-driven algorithms. To address this data challenge in ADT, this work presents three approaches. First, a dataset for ADT tasks is created using a semi-automatic process that minimizes the workload of human annotators. Second, an ADT system that requires minimum training data is designed to account for the presence of other instruments (e.g., non-percussive or pitched instruments). Third, the possibility of improving generic ADT systems with a large amount of unlabeled data from online resources is explored. The main contributions of this work include the introduction of a new ADT dataset, the methods for realizing ADT systems under the constraint of data insufficiency, and a scheme for data-driven methods to benefit from the abundant online resources and might have impact on other audio and music related tasks traditionally impeded by small amounts of labeled data. 

Committee:

Dr. Alexander Lerch (Advisor, MT)

Dr. Mark Clements (ECE)

Dr. Gil Weinberg (MT)

Dr. Jason Freeman (MT)

Dr. Tim Hsu (MT)

Dr. Grant Davidson (Dolby Laboratories)