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Titlebook: Advances for In-Vehicle and Mobile Systems; Challenges for Inter Héseyin Abut,John H. L. Hansen,Kazuya Takeda Book 2007 Springer-Verlag US

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發(fā)表于 2025-3-23 10:20:46 | 只看該作者
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發(fā)表于 2025-3-23 17:25:47 | 只看該作者
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發(fā)表于 2025-3-23 21:43:48 | 只看該作者
Bolustokolyse in Theorie und Praxis(DOA) estimation in noisy environments such as in-vehicle conditions. The particle filter is applied to track the movement of an active speaker while speaking, and the two-step filtering aims at combining the advantages of both traditional cross-correlation (CC) and generalized cross-correlation (GCC) methods.
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發(fā)表于 2025-3-23 23:19:17 | 只看該作者
Frege, Meaning and Communication,ve been annotated. Various research activities within the project’s scope are continuing using the annotated data such as speech intention understanding and speaker’s knowledge acquisition. In this chapter, we introduce these research activities and present the several findings from the in-car speech corpus.
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發(fā)表于 2025-3-24 06:03:53 | 只看該作者
Book 2007n the latest techniques, standards, and emerging deployment on "living in the age of wireless communications and smart vehicular systems." The objective is to incorporate speech, dialog, video, image, vehicular sensory data, and wireless communication modalities to model the total behavior of the dr
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發(fā)表于 2025-3-24 10:16:46 | 只看該作者
https://doi.org/10.1007/978-0-387-45976-9Abut; Digital Signal Processing; Hansen; In-Vehicle; Mobile; Mobile Systems; Processing; Signal Processing;
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發(fā)表于 2025-3-24 14:22:24 | 只看該作者
978-1-4419-4131-2Springer-Verlag US 2007
18#
發(fā)表于 2025-3-24 17:59:57 | 只看該作者
Héseyin Abut,John H. L. Hansen,Kazuya TakedaIncludes new standards being developed for smart vehicular systems.Incorporates models which have been tested for use in evaluating the behaviour of drivers.Includes supplementary material:
19#
發(fā)表于 2025-3-24 19:34:19 | 只看該作者
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發(fā)表于 2025-3-25 01:00:54 | 只看該作者
The Global as Local/Othello as Omkaracle. We investigate a number of classifier fusion techniques to combine multiple channel decisions. We observe that some driving signals carry more biometrie information than others. When we employ trainable combining methods, we can reduce identification error significantly using only driving behav
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