In this paper, we introduce a novel collection of educational material for teaching and learning fundamentals of music processing (FMP) with a particular focus on the audio domain. This collection, referred to as FMP notebooks, discusses well-established topics in Music Information Retrieval (MIR) as motivating application scenarios. The FMP notebooks provide detailed textbook-like explanations of central techniques and algorithms in combination with Python code examples that illustrate how to implement the theory. All components including the introductions of MIR scenarios, illustrations, sound examples, technical concepts, mathematical details, and code examples are integrated into a consistent and comprehensive framework based on Jupyter notebooks. The FMP notebooks are suited for studying the theory and practice, for generating educational material for lectures, as well as for providing baseline implementations for many MIR tasks, thus addressing students, teachers, and researchers.