Kooperativer Bibliotheksverbund

UID:

Format: 1 online resource (xviii, 309 pages) : , digital, PDF file(s).

ISBN: 1-108-56609-X , 1-108-55233-1

Content: This book will help readers understand fundamental and advanced statistical models and deep learning models for robust speaker recognition and domain adaptation. This useful toolkit enables readers to apply machine learning techniques to address practical issues, such as robustness under adverse acoustic environments and domain mismatch, when deploying speaker recognition systems. Presenting state-of-the-art machine learning techniques for speaker recognition and featuring a range of probabilistic models, learning algorithms, case studies, and new trends and directions for speaker recognition based on modern machine learning and deep learning, this is the perfect resource for graduates, researchers, practitioners and engineers in electrical engineering, computer science and applied mathematics.

Note: Title from publisher's bibliographic system (viewed on 29 Jun 2020).

Additional Edition: ISBN 1-108-42812-6

Language: English

URL: https://doi.org/10.1017/9781108552332

UID:

Format: 1 online resource (210 p.)

Edition: 1st ed.

ISBN: 1-5015-0150-X , 1-5015-0152-6

Content: Comprehensively covers protein subcellular localization from single-label prediction to multi-label prediction, and includes prediction strategies for virus, plant, and eukaryote species. Three machine learning tools are introduced to improve classification refinement, feature extraction, and dimensionality reduction.

Note: Description based upon print version of record. , Front matter -- , Preface -- , Contents -- , List of Abbreviations -- , 1. Introduction -- , 2. Overview of subcellular localization prediction -- , 3. Legitimacy of using gene ontology information -- , 4. Single-location protein subcellular localization -- , 5. From single- to multi-location -- , 6. Mining deeper on GO for protein subcellular localization -- , 7. Ensemble random projection for large-scale predictions -- , 8. Experimental setup -- , 9. Results and analysis -- , 10. Properties of the proposed predictors -- , 11. Conclusions and future directions -- , A. Webservers for protein subcellular localization -- , B. Support vector machines -- , C. Proof of no bias in LOOCV -- , D. Derivatives for penalized logistic regression -- , Bibliography -- , Index , English

Additional Edition: ISBN 1-5015-1048-7

Language: English

Subjects: Biology

Keywords: Electronic books.

DOI: 10.1515/9781501501500

URL: Click to View

UID:

Format: 1 Online-Ressource (1 online resource)

ISBN: 9781108552332 , 1108552331

Content: "In the last ten years, many methods have been developed and deployed for real-world biometric applications and multimedia information systems. Machine learning has been playing a crucial role in these applications where the model parameters could be learned and the system performance could be optimized. As for speaker recognition, researchers and engineers have been attempting to tackle the most di cult challenges: noise robustness and domain mismatch. These e orts have now been fruitful, leading to commercial products starting to emerge, e.g., voice authentication for e-banking and speaker identication in smart speakers. Research in speaker recognition has traditionally been focused on signal processing (for extracting the most relevant and robust features) and machine learning (for classifying the features). Recently, we have witnessed the shift in the focus from signal processing to machine learning. In particular, many studies have shown that model adaptation can address both robustness and domain mismatch. As for robust feature extraction, recent studies also demonstrate that deep learning and feature learning can be a great alternative to traditional signal processing algorithms. This book has two perspectives: Machine Learning and Speaker Recognition. The machine learning perspective gives readers insights on what make stateof-the-art systems perform so well. The speaker recognition perspective enables readers to apply machine learning techniques to address practical issues (e.g., robustness under adverse acoustic environments and domain mismatch) when deploying speaker recognition systems. The theories and practices of speaker recognition are tightly connected in the book"--

Note: Includes bibliographical references and index

Additional Edition: ISBN 9781108428125

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9781108428125

Language: English

DOI: 10.1017/9781108552332

URL: lizenzpflichtig