國立虎尾科技大學 |

Filter Banks and Audio Coding = Compressing Audio Signals Using Python /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Filter Banks and Audio Coding/ by Gerald Schuller.
其他題名:	Compressing Audio Signals Using Python /
作者:	Schuller, Gerald.
面頁冊數:	XI, 197 p. 72 illus., 49 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Computer Appl. in Social and Behavioral Sciences. -
電子資源:	https://doi.org/10.1007/978-3-030-51249-1
ISBN:	9783030512491

Filter Banks and Audio Coding = Compressing Audio Signals Using Python /
Schuller, Gerald.

Filter Banks and Audio CodingCompressing Audio Signals Using Python /[electronic resource] :by Gerald Schuller. - 1st ed. 2020. - XI, 197 p. 72 illus., 49 illus. in color.online resource.

Introduction -- Filter Banks -- With a Changing Number of Subbands -- Predictive Coding -- Psychoacoustic Models -- Psychoacoustic Models and Quantization -- Entropy Coding -- The Python Perceptual Audio Coder -- Predictive Lossless Audio Coding -- Scalable Lossless Audio Coding -- Psycho-Acoustic Pre-Filter -- Conclusion.

This 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.

ISBN: 9783030512491

Standard No.: 10.1007/978-3-030-51249-1doiSubjects--Topical Terms:

669920
Computer Appl. in Social and Behavioral Sciences.

LC Class. No.: TK5102.9

Dewey Class. No.: 621.382

Filter Banks and Audio Coding = Compressing Audio Signals Using Python /
LDR:02917nam a22004215i 4500 001 1029588
003 DE-He213
005 20200923130151.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030512491 $9 978-3-030-51249-1
024 7 $a 10.1007/978-3-030-51249-1 $2 doi
035 $a 978-3-030-51249-1
050 4 $a TK5102.9
050 4 $a TA1637-1638
072 7 $a TTBM $2 bicssc
072 7 $a TEC008000 $2 bisacsh
072 7 $a TTBM $2 thema
072 7 $a UYS $2 thema
082 0 4 $a 621.382 $2 23
100 1 $a Schuller, Gerald. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1326353
245 1 0 $a Filter Banks and Audio Coding $h [electronic resource] : $b Compressing Audio Signals Using Python / $c by Gerald Schuller.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XI, 197 p. 72 illus., 49 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
505 0 $a Introduction -- Filter Banks -- With a Changing Number of Subbands -- Predictive Coding -- Psychoacoustic Models -- Psychoacoustic Models and Quantization -- Entropy Coding -- The Python Perceptual Audio Coder -- Predictive Lossless Audio Coding -- Scalable Lossless Audio Coding -- Psycho-Acoustic Pre-Filter -- Conclusion.
520 $a This 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 2 4 $a Computer Appl. in Social and Behavioral Sciences. $3 669920
650 2 4 $a User Interfaces and Human Computer Interaction. $3 669793
650 2 4 $a Engineering Acoustics. $3 785331
650 1 4 $a Signal, Image and Speech Processing. $3 670837
650 0 $a Application software. $3 528147
650 0 $a User interfaces (Computer systems). $3 1253526
650 0 $a Acoustics. $3 670692
650 0 $a Acoustical engineering. $3 563185
650 0 $a Speech processing systems. $3 564428
650 0 $a Image processing. $3 557495
650 0 $a Signal processing. $3 561459
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030512484
776 0 8 $i Printed edition: $z 9783030512507
776 0 8 $i Printed edition: $z 9783030512514
856 4 0 $u https://doi.org/10.1007/978-3-030-51249-1
912 $a ZDB-2-PHA
912 $a ZDB-2-SXP
950 $a Physics and Astronomy (SpringerNature-11651)
950 $a Physics and Astronomy (R0) (SpringerNature-43715)