Stadelmann Thilo (32 results)

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Paperback. Condition: Brand New. 400 pages. 9.25x6.10x1.42 inches. In Stock.

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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the refereed proceedings of the 9th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2020, held in Winterthur, Switzerland, in September 2020. The conference was held virtually due to the COVID-19 pandemic.The 22 revised full papers presented were carefully reviewed and selected from 34 submissions. The papers present and discuss the latest research in all areas of neural network-and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications.

Taschenbuch. Condition: Neu. Neuware -This book constitutes the refereed proceedings of the 9th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2020, held in Winterthur, Switzerland, in September 2020. The conference was held virtually due to the COVID-19 pandemic.The 22 revised full papers presented were carefully reviewed and selected from 34 submissions. The papers present and discuss the latest research in all areas of neural network-and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 320 pp. Englisch.

Taschenbuch. Condition: Neu. Artificial Neural Networks in Pattern Recognition | 9th IAPR TC3 Workshop, ANNPR 2020, Winterthur, Switzerland, September 2-4, 2020, Proceedings | Frank-Peter Schilling (u. a.) | Taschenbuch | xi | Englisch | 2020 | Springer | EAN 9783030583088 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Condition: Neu. Voice Modeling Methods | for Automatic Speaker Recognition | Thilo Stadelmann | Taschenbuch | 240 S. | Englisch | 2015 | Südwestdeutscher Verlag für Hochschulschriften AG Co. KG | EAN 9783838116327 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Taschenbuch. Condition: Neu. Neuware -Building a voice model means to capture the characteristics of a speaker's voice in a data structure. This data structure is then used by a computer for further processing, such as comparison with other voices. Voice modeling is a vital step in the process of automatic speaker recognition that itself is the foundation of several applied technologies: (a) biometric authentication, (b) speech recognition and (c) multimedia indexing. Current automatic speaker recognition works well under relatively constrained circumstances, such as studio recordings, or when prior knowledge on the number and identity of occurring speakers is available. Under more adverse conditions, such as in feature films or amateur material on the web, the achieved speaker recognition scores drop below a rate that is acceptable for an end user or for further processing. In this book, algorithmic and methodic improvements to the state of the art in automatic speaker recognition are presented. They are accompanied by a capacious software toolkit called 'sclib'. Additionally, the method of 'Eidetic Design' facilitates intuitive algorithm design, development and teaching.Books on Demand GmbH, Überseering 33, 22297 Hamburg 240 pp. Englisch.

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Condition: Hervorragend. Zustand: Hervorragend | Seiten: 480 | Sprache: Englisch | Produktart: Bücher | This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors ¿ some from academia and some from industry, ranging from financial to health and from manufacturing to e-commerce. Each of these chapters describes a fundamental principle, method or tool in data science by analyzing specific use cases and drawing concrete conclusions from them. The case studies presented, and the methods and tools applied, represent the nuts and bolts of data science. Finally, Part III was again written from the perspective of the editors and summarizes the lessons learned that have been distilled from the case studies in Part II. The section can be viewed as a meta-study on data science across a broad range of domains, viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are. The book targets professionals as well as students of data science:first, practicing data scientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors¿ combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, students of data science who want to understand both the theoretical and practical aspects of data science, vetted by real-world case studies at the intersection of academia and industry.

Hardback. Condition: New. 2019 ed.

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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors - some from academia and some from industry,ranging from financial to health and from manufacturing toe-commerce.Each of these chapters describes a fundamental principle, method or tool in data scienceby analyzing specific use cases and drawing concrete conclusions from them. The casestudies presented, and the methods and tools applied, represent the nuts and bolts ofdata science. Finally, Part III was again written from the perspective of the editors andsummarizes the lessons learned that have been distilled from the case studies in Part II.The section can be viewed as a meta-study on data science across a broad range of domains,viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are.The book targets professionals as well as students of data science:first, practicing datascientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors' combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, studentsof data science who want to understand both the theoretical and practical aspects of datascience, vetted by real-world case studies at the intersection of academia and industry.

Hardcover. Condition: New. New. book.

Hardcover. Condition: Brand New. 465 pages. 9.25x6.25x1.25 inches. In Stock.

Condition: New.

Hardback. Condition: New. 2019 ed.

Condition: new. Questo è un articolo print on demand.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book constitutes the refereed proceedings of the 9th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2020, held in Winterthur, Switzerland, in September 2020. The conference was held virtually due to the COVID-19 pandemic.The 22 revised full papers presented were carefully reviewed and selected from 34 submissions. The papers present and discuss the latest research in all areas of neural network-and machine learning-based pattern recognition. They are organized in two sections: learning algorithms and architectures, and applications. 320 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book constitutes the refereed proceedings of the 9th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2020, held in Winterthur, Switzerland, in September 2020. The conference was held virtually due to the C.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Building a voice model means to capture the characteristics of a speaker's voice in a data structure. This data structure is then used by a computer for further processing, such as comparison with other voices. Voice modeling is a vital step in the process of automatic speaker recognition that itself is the foundation of several applied technologies: (a) biometric authentication, (b) speech recognition and (c) multimedia indexing. Current automatic speaker recognition works well under relatively constrained circumstances, such as studio recordings, or when prior knowledge on the number and identity of occurring speakers is available. Under more adverse conditions, such as in feature films or amateur material on the web, the achieved speaker recognition scores drop below a rate that is acceptable for an end user or for further processing. In this book, algorithmic and methodic improvements to the state of the art in automatic speaker recognition are presented. They are accompanied by a capacious software toolkit called 'sclib'. Additionally, the method of 'Eidetic Design' facilitates intuitive algorithm design, development and teaching. 240 pp. Englisch.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Stadelmann ThiloThilo was born in Lemgo/Germany in the 1980 s and still loves themusic of this time. Maybe this is why he choose to analyzeacoustic data in his doctoral studies? When he s not playingmusic or doing research and develo.

Condition: new. Questo è un articolo print on demand.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Building a voice model means to capture the characteristics of a speaker's voice in a data structure. This data structure is then used by a computer for further processing, such as comparison with other voices. Voice modeling is a vital step in the process of automatic speaker recognition that itself is the foundation of several applied technologies: (a) biometric authentication, (b) speech recognition and (c) multimedia indexing. Current automatic speaker recognition works well under relatively constrained circumstances, such as studio recordings, or when prior knowledge on the number and identity of occurring speakers is available. Under more adverse conditions, such as in feature films or amateur material on the web, the achieved speaker recognition scores drop below a rate that is acceptable for an end user or for further processing. In this book, algorithmic and methodic improvements to the state of the art in automatic speaker recognition are presented. They are accompanied by a capacious software toolkit called 'sclib'. Additionally, the method of 'Eidetic Design' facilitates intuitive algorithm design, development and teaching.

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book has two main goals: to define data science through the work of data scientists and their results, namely data products, while simultaneously providing the reader with relevant lessons learned from applied data science projects at the intersection of academia and industry. As such, it is not a replacement for a classical textbook (i.e., it does not elaborate on fundamentals of methods and principles described elsewhere), but systematically highlights the connection between theory, on the one hand, and its application in specific use cases, on the other. With these goals in mind, the book is divided into three parts: Part I pays tribute to the interdisciplinary nature of data science and provides a common understanding of data science terminology for readers with different backgrounds. These six chapters are geared towards drawing a consistent picture of data science and were predominantly written by the editors themselves. Part II then broadens the spectrum by presenting views and insights from diverse authors - some from academia and some from industry,ranging from financial to health and from manufacturing toe-commerce.Each of these chapters describes a fundamental principle, method or tool in data scienceby analyzing specific use cases and drawing concrete conclusions from them. The casestudies presented, and the methods and tools applied, represent the nuts and bolts ofdata science. Finally, Part III was again written from the perspective of the editors andsummarizes the lessons learned that have been distilled from the case studies in Part II.The section can be viewed as a meta-study on data science across a broad range of domains,viewpoints and fields. Moreover, it provides answers to the question of what the mission-critical factors for success in different data science undertakings are.The book targets professionals as well as students of data science:first, practicing datascientists in industry and academia who want to broaden their scope and expand their knowledge by drawing on the authors' combined experience. Second, decision makers in businesses who face the challenge of creating or implementing a data-driven strategy and who want to learn from success stories spanning a range of industries. Third, studentsof data science who want to understand both the theoretical and practical aspects of datascience, vetted by real-world case studies at the intersection of academia and industry. 480 pp. Englisch.

Gebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Systematically highlights the connection between theory, on the one end, and its application in specific use cases, on the otherEach chapter describes a fundamental principle, method or tool in data science by analyzing specif.