Talks and Poster Presentations (with Proceedings-Entry):

R. Vogl, M. Dorfer, G. Widmer, P. Knees:
"Drum Transcription via Joint Beat and Drum Modeling Using Convolutional Recurrent Neural Networks";
Talk: 18th International Society for Music Information Retrieval Conference, Suzhou, China; 2017-10-23 - 2017-10-27; in: "Proceedings of the 18th International Society for Music Information Retrieval Conference", (2017), ISBN: 978-981-11-5179-8; 150 - 157.

English abstract:
Existing systems for automatic transcription of drum tracks from polyphonic music focus on detecting drum instrument onsets but lack consideration of additional meta information like bar boundaries, tempo, and meter. We address this limitation by proposing a system which has the capability to detect drum instrument onsets along with the corresponding beats and downbeats. In this design, the system has the means to utilize information on the rhythmical structure of a song which is closely related to the desired drum transcript. To this end, we introduce and compare different architectures for this task, i.e., recurrent, convolutional,
and recurrent-convolutional neural networks. We evaluate our systems on two well-known data sets and an additional new data set containing both drum and beat
annotations. We show that convolutional and recurrent-convolutional
neural networks perform better than state-of-the-art methods and that learning beats jointly with drums can be beneficial for the task of drum detection.

drum transcription, convolutional recurrent neural networks, machine learning, music information retrieval

Electronic version of the publication:

Related Projects:
Project Head Peter Knees:

Created from the Publication Database of the Vienna University of Technology.