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paperback. Zustand: Good. Cover shows minor shelf wear, some light indentations. Pages are clean, text and pictures are intact and unmarred.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. In English.

Soft cover. Zustand: As New. This book has been in storage since publication and is unread. Hence the description as new .

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New. In English.

Paperback. Zustand: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.

Paperback. Zustand: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less.

Zustand: New. In English.

Zustand: New. In English.

Zustand: New.

Paperback. Zustand: Brand New. 122 pages. 9.25x7.51x0.26 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book describes several modules of the Code Excited Linear Prediction (CELP) algorithm. The authors use the Federal Standard-1016 CELP MATLAB® software to describe in detail several functions and parameter computations associated with analysis-by-synthesis linear prediction. The book begins with a description of the basics of linear prediction followed by an overview of the FS-1016 CELP algorithm. Subsequent chapters describe the various modules of the CELP algorithm in detail. In each chapter, an overall functional description of CELP modules is provided along with detailed illustrations of their MATLAB® implementation. Several code examples and plots are provided to highlight some of the key CELP concepts. Link to MATLAB® code found within the book Table of Contents: Introduction to Linear Predictive Coding / Autocorrelation Analysis and Linear Prediction / Line Spectral Frequency Computation / Spectral Distortion / The Codebook Search / The FS-1016 Decoder.

Hardcover. Zustand: Brand New. 57 pages. 9.50x7.75x0.50 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces basic machine learning concepts and applications for a broad audience that includes students, faculty, and industry practitioners. We begin by describing how machine learning provides capabilities to computers and embedded systems to learn from data. A typical machine learning algorithm involves training, and generally the performance of a machine learning model improves with more training data. Deep learning is a sub-area of machine learning that involves extensive use of layers of artificial neural networks typically trained on massive amounts of data. Machine and deep learning methods are often used in contemporary data science tasks to address the growing data sets and detect, cluster, and classify data patterns. Although machine learning commercial interest has grown relatively recently, the roots of machine learning go back to decades ago. We note that nearly all organizations, including industry, government, defense, and health, are using machine learning toaddress a variety of needs and applications. The machine learning paradigms presented can be broadly divided into the following three categories: supervised learning, unsupervised learning, and semi-supervised learning. Supervised learning algorithms focus on learning a mapping function, and they are trained with supervision on labeled data. Supervised learning is further sub-divided into classification and regression algorithms. Unsupervised learning typically does not have access to ground truth, and often the goal is to learn or uncover the hidden pattern in the data. Through semi-supervised learning, one can effectively utilize a large volume of unlabeled data and a limited amount of labeled data to improve machine learning model performances. Deep learning and neural networks are also covered in this book. Deep neural networks have attracted a lot of interest during the last ten years due to the availability of graphics processing units (GPU) computational power, big data, and new software platforms. They have strong capabilities in terms of learning complex mapping functions for different types of data. We organize the book as follows. The book starts by introducing concepts in supervised, unsupervised, and semi-supervised learning. Several algorithms and their inner workings are presented within these three categories. We then continue with a brief introduction to artificial neural network algorithms and their properties. In addition, we cover an array of applications and provide extensive bibliography. The book ends with a summary of the key machine learning concepts.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Machine learning and artificial intelligence (AI) are powerful tools that create predictive models, extract information, and help make complex decisions. They do this by examining an enormous quantity of labeled training data to find patterns too complex for human observation. However, in many real-world applications, well-labeled data can be difficult, expensive, or even impossible to obtain. In some cases, such as when identifying rare objects like new archeological sites or secret enemy military facilities in satellite images, acquiring labels could require months of trained human observers at incredible expense. Other times, as when attempting to predict disease infection during a pandemic such as COVID-19, reliable true labels may be nearly impossible to obtain early on due to lack of testing equipment or other factors. In that scenario, identifying even a small amount of truly negative data may be impossible due to the high false negative rate of available tests. In such problems, it is possible to label a small subset of data as belonging to the class of interest though it is impractical to manually label all data not of interest. We are left with a small set of positive labeled data and a large set of unknown and unlabeled data. Readers will explore this Positive and Unlabeled learning (PU learning) problem in depth. The book rigorously defines the PU learning problem, discusses several common assumptions that are frequently made about the problem and their implications, and considers how to evaluate solutions for this problem before describing several of the most popular algorithms to solve this problem. It explores several uses for PU learning including applications in biological/medical, business, security, and signal processing. This book also provides high-level summaries of several related learning problems such as one-class classification, anomaly detection, and noisy learning and their relation to PU learning.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. In.

Zustand: New. pp. xx + 464 Illus.

Gebunden. Zustand: New. ANDREAS SPANIAS, PhD, is Professor in Electrical Engineering at Arizona State University (ASU). He has led the development of the award-winning online software Java-DSP. His research interests include adaptive filters, speech/audio processing, multimedia, a.

Hardcover. Zustand: Brand New. illustrated edition. 464 pages. 9.50x6.25x1.25 inches. In Stock.

Zustand: New. An in-depth treatment of algorithms and standards for perceptual coding of high-fidelity audio, this self-contained reference surveys and addresses all aspects of the field. Num Pages: 464 pages, Illustrations. BIC Classification: TJK. Category: (P) Professional & Vocational. Dimension: 241 x 162 x 29. Weight in Grams: 824. . 2007. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

Buch. Zustand: Neu. Neuware - Master algorithms and standards for transparent coding of high-fidelity audioHere is an in-depth treatment of algorithms and standards for transparent coding of high-fidelity audio. Readers learn how algorithms for high-quality digital audio deliver transparent signal reproduction with a minimum number of bits. The unique features of the book include detailed coverage of topics such as filter banks, transform coding, sinusoidal analysis, linear prediction, hybrid algorithms, perceptual evaluation methods, scalable algorithms, Internet applications, MP3 and MP4 stereo systems, and current international and commercial audio standards.Following a general introduction, the authors present fundamental signal processing concepts relevant to audio coding and then introduce waveform and entropy quantization schemes. Next are thorough treatments of the following topics:\* Linear prediction, ADPCM, and CELP algorithms for narrowband and wideband coding\* Cellular telephony vocoders versus CD-quality audio coders\* Perceptual modules including the ISO/IEC 11172-3 (MPEG-1) psychoacoustic analysis model\* Detailed descriptions of the MPEG-1 Layer III (MP3) and the AAC (MP4) algorithms\* Descriptions of the algorithms behind successful products such as the Apple iPod\* Filter bank design and algorithms and the Modified Discrete Cosine Transform (MDCT)\* Established and emerging standards for transparent coding of CD-quality stereo audio signals\* Standardization activities in high-fidelity audio coding, including DVD-Audio, Super Audio CD (SACD), Dolby AC3, Digital, Digital Theater Systems (DTS), and Sony SDDS surround sound\* Lossless audio coding and digital audio watermarking techniques, including SHORTEN, DVD-algorithm, MUSICompress, AudioPaK, C-LPAC, LTAC, and IntMDCT lossless coding schemes\* Surround sound compression algorithms for cinema and super audio CD applications\* Digital audio watermarking, content protection, and copyright issues\* Complexity, delay, error control, and subjective quality in perceptual audio codingComputer exercises and MATLAB(r) hands-on projects complement the algorithm theory and reinforce concept learning. A comprehensive bibliography with more than 600 references to additional sources of information to explore individual topics in greater depth.This textbook includes all the right elements and topics for a senior and/or graduate-level course in speech/audio processing and multimedia. Moreover, it is highly recommended for practitioners, scientists, and audio engineers who want to master coding algorithms for high-fidelity audio.