I’m thrilled to announce the release of my new teaching book in the fields of music science and music psychology! The title is Music and Science: A Guide to Empirical Music Research, this book is the culmination of over ten years of teaching these subjects at Durham University. Over the past five years, both undergraduate and postgraduate students have accessed earlier drafts, and their invaluable feedback has shaped the book’s content and tone.

The book explores empirical research in music and psychology, as well as computational approaches to music research. These disciplines share strong synergies, overlapping interests, and similar methodologies. The chapters guide readers through historical foundations, key concepts, empirical methods, research design, data sources, and analysis techniques. Dedicated chapters focus on computational methods, including audio analysis, event-based and symbolic approaches, and corpus studies. A standout feature of the book is its emphasis on diversity. I’ve intentionally included a broad range of references, highlighting contributions from female scholars and researchers from outside the Anglo-American sphere. The music examples are equally diverse, drawing on Indian music, protest music, popular genres, and folk traditions.

The book also emphasizes Open Research practices, with extensive discussion on transparency, preregistration, replication, data sharing, open-access publishing, and preprints. Concrete examples illustrate these principles, many drawn from recent studies conducted at the Music and Science Lab, where we’ve actively embraced these initiatives. While it wasn’t feasible to release the book as an open-access monograph, I hope the softcover version remains affordable, offering 298 pages of insights and guidance.

To support readers further, I’ve created an electronic repository on GitHub, housing all the computer code used in the book. This repository includes R and Python scripts for relevant chapters, along with links to interactive notebooks in Google Colab. These resources enable readers to replicate the analyses and visualizations in the book and encourage the adoption of transparent practices in their own research.

Lastly, I’d like to extend heartfelt thanks to the many students and colleagues who contributed to this project. Alumni from our lab, including Scott Bannister, Lennie Thomas, and Annaliese Micallef Grimaud, are featured prominently, with their fascinating research illuminating key points. And a very special thanks also go to leading experts in the field—Mats Küssner, Laura Leante, Fabian Moss, Imre Lahdelma, and Brian McFee—for their detailed feedback on the initial drafts.