000			a
999			_c32439 _d32439
008			230831b xxu||||| |||| 00| 0 eng d
020			_a9783030512514
082			_a621.3893 _bSCH
100			_aSchuller, Gerald
245			_aFilter banks and audio coding : compressing audio signals using Python
260			_bSpringer, _c2020 _aCham :
300			_axi, 197 p. ; _bill., _c28 cm
365			_b49.99 _cEUR _d94.99
504			_aIncludes bibliographical references and index.
520			_aThis textbook presents the fundamentals of audio coding, used to compress audio and music signals, using Python programs both as examples to illustrate the principles and for experiments for the reader. Together, these programs then form complete audio coders. The author starts with basic knowledge of digital signal processing (sampling, filtering) to give a thorough introduction to filter banks as used in audio coding, and their design methods. He then continues with the next core component, which are psycho-acoustic models. The author finally shows how to design and implement them. Lastly, the author goes on to describe components for more specialized coders, like the Integer-to-Integer MDCT filter bank, and predictive coding for lossless and low delay coding. Included are Python program examples for each section, which illustrate the principles and provide the tools for experiments.- Comprehensively explains the fundamentals of filter banks and audio coding;- Provides Python examples for each principle so that completed audio coders are obtained in the language;- Includes a suite of classroom materials including exercises, experiments, and examples.
650			_aSound Recording and reproducing
650			_aDigital techniques
650			_aAcoustical engineering
650			_aBandpass
650			_aAudio signal processing
650			_aBark scale
650			_aDCT4
650			_aFolding matrix
650			_aMasking threshold
650			_aMDCT
650			_aPerfect reconstruction
650			_a Polyphase matrix
650			_aPrediction error
650			_aPsycho-acoustic model
650			_a Python
650			_aReconstructed samples
650			_aRice code
650			_aSynthesis filterbank
650			_a Z-transform
942			_2ddc _cBK