The Music Cognition Reading Group has been meeting regularly since 2004. We discuss recent literature and present research from the Music Cognition Group in Amsterdam and other music research groups. Want to stay informed? Join our mailinglist, subscribe to our public calendar, or join our Zotero group.

Modeling enculturated bias in entrainment to rhythmic patterns

14 March 2023. 11.00. Our discussion in March will center around the paper Modeling enculturated bias in entrainment to rhythmic patterns. This is a computational modeling paper on rhythm perception, a topic that has a long history within the Music Cognition Group (check their reference list for several familiar names!). The discussion will be led by Atser Damsma.

We continue to meet at Bushuis, with a Zoom link to be sent out via the Reading Group mailing list the morning of the meeting. The core article is available via the link above.

MusicLM: Generating Music From Test

14 February 2023. 11.00. In the last few months, there have been several noteworthy advancements in the field of automatic music generation. For our February meeting, we plan to read MusicLM: Generating Music From Text, a text to audio model recently published by a team at Google. In addition to this “core” article, we will also suggest a handful of recent, related articles. The conversation is planned to more generally address the advancements, technologies, and consequences of advancements in AI generated music.

As this topic concerns many research group at the ILLC, we will be inviting several related research groups at the University of Amsterdam to join this meeting. As a result of a larger in-person attendance, online participation may be limited to only listening and participation via messaging on Zoom in order to facilitate smoother in-person discussion.

We continue to meet at Bushuis, with a Zoom link to be sent out via the Reading Group mailing list the morning of the meeting. The core article is available via the link above. Please also acquaint yourself with the accompanying audio examples provided by the authors.

Relative pitch representations and invariance to timbre

17 January 2023. 11.00. Our first article of 2023 investigates the relationship between relative pitch representations and timbre. Again, we are meeting at our new location in Bushuis with a Zoom link to be sent out via the Reading Group list-serv the morning of the meeting. A copy of the article has been sent out via the list-serv if you are unable to obtain it via the linked heading above.

New music system reveals spectral contribution to statistical learning

6 December 2022. 11.00. Following up on our last review chapter discussing memory and timbre, a new article investigating the role of spectral properties in statistical learning will be the subject of our next discussion. We will be meeting in person AT THE HUMANITIES LAB with a Zoom link to be provided the morning of the session. Those attending in person will get lunch after! NOTE! This is a new location. An address and map will be sent out via the mailing list.

Memory for Timbre

8 November 2022. 11.00. Our second reading for this academic year will be an online discussion reviewing the chapter Memory for Timbre from the textbook Timbre: Acoustics, Perception, and Cognition. We will be discussing the paper in reference to the NWO-OC project currently being investigated by the lab. This meeting will be held via Zoom and will not include the normal lunch option after at Science Park as several members of the lab will be away from Science Park that day. A copy of the article and access to the Zoom link will be sent out via the reading list. Note: We will not be using the usual MCG Zoom link, please check the reading list email!!

A Causal Framework for Cross-Cultural Generalizability

11 October, 2022, 11.00. The music cognition group starts of the academic year with a discussion of a paper concerning cross cultural generalizability. Join us online to discuss.

Upcoming meetings

6 December 2022. New music system reveals spectral contribution to statistical learning