Kunal Mukerjee

Windows Media Video 9: overview and applications

Abstract Microsoft ® Windows Media 9 Series is a set of technologies that enables rich digital media experiences across many types of networks and devices. These technologies are widely used in the industry for media delivery over the internet and other media, and are also applied to broadcast, high definition DVDs, and digital projection in theaters. At the core of these technologies is a state-of-the-art video codec called Windows Media Video 9 (WMV-9), which provides highly competitive video quality for reasonable computational complexity. WMV-9 is currently under standardization by the Society of Motion Picture and Television Engineers (SMPTE) and the spec is at the CD (Committee Draft) stage. This paper includes a brief introduction to Windows Media technologies and their applications, with a focus on the compression algorithms used in WMV-9. We present analysis, experimental results, and independent studies that demonstrate quality benefits of WMV-9 over a variety of codecs, including optimized implementations of MPEG-2, MPEG-4, and H.264/AVC. We also discuss the complexity advantages of WMV-9 over H.264/AVC.

Architecture, User Interface, and Enabling Technology in Windows Vista's Speech Systems

Existing speech recognition systems have claimed high accuracy for specific tasks such as dictation. What is new in Windows Speech recognition for Vista is a combination of high accuracy and high usability for the end-to-end speech experience. This paper describes the architecture, user interface, and key technologies that make up the speech system incorporated in Microsoft Windows Vista. It outlines some of the challenges encountered in providing a speech-based interface to a system as complex and extensible as the modern desktop PC, as well as the technology developments that have made this possible. In particular, the paper describes key elements of the speech user interface and how the users ability to control the system is maintained despite limitations in the underlying recognition technology. The paper also explains how feedback and adaptation systems are used to tailor the experience to each user and their particular style of speaking/use of language.

Linear scale semantic mining algorithms in microsoft SQL server's semantic platform

This paper describes three linear scale, incremental, and fully automatic semantic mining algorithms that are at the foundation of the new Semantic Platform being released in the next version of SQL Server. The target workload is large (10 -- 100 million) Enterprise document corpuses. At these scales, anything short of linear scale and incremental is costly to deploy. These three algorithms give rise to three weighted physical indexes: Tag Index (top keywords in each document); Document Similarity Index (top closely related documents given any document); and Semantic Phrase Similarity Index (top semantically related phrases, given any phrase), which are then query-able through the SQL interface. The need for specifically creating these three indexes was motivated by observing typical stages of document research, and gap analysis, given current tools and technology at the Enterprise. We describe the mining algorithms and architecture, and also outline some compelling user experiences that are enabled by the indexes.