TY  - GEN
N2  - The textbook provides both profound technological knowledge and a comprehensive treatment of essential topics in music processing and music information retrieval (MIR). Including numerous examples, figures, and exercises, this book is suited for students, lecturers, and researchers working in audio engineering, signal processing, computer science, digital humanities, and musicology. The book consists of eight chapters. The first two cover foundations of music representations and the Fourier transform concepts used throughout the book. Each of the subsequent chapters starts with a general description of a concrete music processing task and then discusses in a mathematically rigorous way essential techniques and algorithms applicable to a wide range of analysis, classification, and retrieval problems. By mixing theory and practice, the book's goal is to offer detailed technological insights and a deep understanding of music processing applications. As a substantial extension, the textbook's second edition introduces the FMP (fundamentals of music processing) notebooks, which provide additional audio-visual material and Python 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. The FMP notebooks are available from the author's institutional web page at the International Audio Laboratories Erlangen.
DO  - 10.1007/978-3-030-69808-9
DO  - doi
AB  - The textbook provides both profound technological knowledge and a comprehensive treatment of essential topics in music processing and music information retrieval (MIR). Including numerous examples, figures, and exercises, this book is suited for students, lecturers, and researchers working in audio engineering, signal processing, computer science, digital humanities, and musicology. The book consists of eight chapters. The first two cover foundations of music representations and the Fourier transform concepts used throughout the book. Each of the subsequent chapters starts with a general description of a concrete music processing task and then discusses in a mathematically rigorous way essential techniques and algorithms applicable to a wide range of analysis, classification, and retrieval problems. By mixing theory and practice, the book's goal is to offer detailed technological insights and a deep understanding of music processing applications. As a substantial extension, the textbook's second edition introduces the FMP (fundamentals of music processing) notebooks, which provide additional audio-visual material and Python 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. The FMP notebooks are available from the author's institutional web page at the International Audio Laboratories Erlangen.
T1  - Fundamentals of music processing :using Python and Jupyter notebooks /
AU  - Müller, Meinard,
ET  - Second edition.
CN  - TK7882.P3
ID  - 1435781
KW  - Computer sound processing
KW  - Sound
KW  - Electronic music
KW  - Son
SN  - 9783030698089
SN  - 3030698084
TI  - Fundamentals of music processing :using Python and Jupyter notebooks /
LK  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-69808-9
UR  - https://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-69808-9
ER  -