Fundamentals of Music Processing : Using Python and Jupyter Notebooks

Springer Nature Switzerland AG

Dive into the world of music processing with "Fundamentals of Music Processing: Using Python and Jupyter Notebooks" by Meinard Müller. This comprehensive textbook offers profound technological knowledge and a thorough treatment of essential topics in music information retrieval (MIR). Ideal for students, lecturers, and researchers in audio engineering, signal processing, computer science, digital humanities, and musicology, the book is packed with examples, figures, and exercises. The second edition features FMP notebooks, providing interactive Python code examples that implement computational approaches step-by-step. Experiment with music examples, explore parameter settings, and understand results through visualizations and sonifications. Key Features: * Covers music representations and Fourier transform foundations. * Explores essential techniques and algorithms for analysis, classification, and retrieval. * Offers detailed technological insights and deep understanding of music processing applications. * Includes interactive Jupyter notebooks with Python code examples. * Suited for a wide range of academic disciplines and research areas. This hardcover edition is written in English and comprises 495 pages, offering a deep dive into the computational aspects of music. A substantial extension to the textbook, the FMP notebooks provide additional audio-visual material and code examples that implement all computational approaches step by step. Using Jupyter notebooks and open-source web applications, the FMP notebooks yield an interactive framework that allows students to experiment with their music examples, explore the effect of parameter settings, and understand the computed results by suitable visualizations and sonifications. Product Summary: Fundamentals of Music Processing, Meinard Müller, Springer Nature Switzerland AG, Hardcover, English, First edition, 495 pages, ISBN 9783030698072, Python, Jupyter Notebooks, Music Information Retrieval.