## 1 Introduction  
Music information retrieval (MIR) is an exciting and challenging area of research. Music not only connects people but also relates to many different research disciplines, including signal processing, information retrieval, machine learning, musicology, and psychoacoustics. In its beginnings, research in MIR has borrowed many ideas and concepts from more established disciplines such as speech processing or computer linguistics. After more than twenty years, the MIR field has matured to an independent research area, which has many things to offer to signal processing and other research disciplines. Using well-established music analysis and retrieval topics, the textbook on Fundamentals of Music Processing [1] (FMP) yields an example of how music may provide a rich and challenging application domain for introducing, teaching, and studying fundamental techniques and algorithms relevant for general courses in computer science, multimedia engineering, information science, and digital humanities. While providing profound technological knowledge as well as a comprehensive treatment of music processing applications, the book also includes numerous examples and illustrations to convey the main ideas in an intuitive fashion. In recent years, suitably designed software packages and freely accessible web-based frameworks have made education in computer science and signal processing more interactive. Such novel technology allows for designing courses that help students move from recalling and reciting theoretical concepts towards comprehension and application. These new developments are precisely the motivation for the development of the FMP Notebooks, which provide an interactive foundation for teaching and learning fundamentals of music processing (FMP). The FMP notebooks are built upon the Jupyter notebook framework, which has become a standard in industry as well as in educational settings [2]. This open-source web application allows users to create documents that contain live code, text-based information, mathematical formulas, plots, images, sound examples, and videos. By leveraging the Jupyter framework, the FMP notebooks bridge the gap between theory and practice, where technical concepts and mathematical details are interleaved with code examples, illustrations, and sound examples (see Figure 1). The FMP notebooks closely follow the eight chapters of the textbook [1], and as such, provide an explicit link between structured educational environments and current professional practices, in line with current curricular recommendations for computer science [3]. With this paper, we provide a comprehensive guide through the FMP notebooks. The parts of the FMP notebooks' main body, as shown in Figure 2, cover MIR topics starting with music representations and Fourier analysis through beat tracking and chord recognition to retrieval and audio decomposition. Each part, in turn, consists of about 10-15 notebooks that provide in-depth descriptions of techniques and algorithms, which are motivated, applied, and evaluated within the given MIR context. While giving a guide through the notebooks, this paper's main objective is to make concrete suggestions on using the FMP notebooks to create an enriching, interactive and interdisciplinary supplement in the form of experiments and advanced studies in a music processing curriculum. Furthermore, we show how the notebooks allow for generating appealing multimedia objects such as figures and sound examples, which may be useful for lectures and scientific publications. The FMP notebooks are publicly available under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License at [[https://www.audiolabs-erlangen.de/FMP](https://www.audiolabs-erlangen.de/FMP)]([https://www.audiolabs-erlangen.de/FMP](https://www.audiolabs-erlangen.de/FMP)) (accessed on 22 April 2021) in the form of Jupyter notebooks as well as HTML exports, which can be accessed through a conventional web browser.