Combining the evidences of temporal and spectral enhancement techniques for improving the performance of Indian language identification system in the presence of background noise

Language Identification has gained significant importance in recent years, both in research and commercial market place, demanding an improvement in the ability of machines to distinguish between languages. Although methods like Gaussian mixture models, hidden Markov models and neural networks are used for identifying languages the problem of language identification in noisy environments could not be addressed so far. This paper addresses the performance of automatic language identification system in noisy environments. A comparative performance analysis of speech enhancement techniques like minimum mean squared estimation, spectral subtraction and temporal processing, with different types of noise at different SNRs, is presented here. Though these individual enhancement techniques may not yield good performance with different types of noise at different SNRs, it is proposed to combine the evidences of all these techniques to improve the overall performance of the system significantly. The language identification studies are performed using IITKGP-MLILSC (IIT Kharagpur-Multilingual Indian Language Speech Corpus) databases which consists of 27 languages.     

Parallel programming for multimedia applications

Computing capabilities are continuing to increase with the availability of multi core and many core processors. The wide availability of multi core processors has made parallel programming possible for end user applications running on desktops, workstations, and mobile devices. While parallel hardware has become common, software that exploits parallel capabilities is just beginning to take hold. Multimedia applications, with their data parallel nature and large computing requirements will benefit significantly from parallel programming. In this paper an overview of parallel programming is presented and languages and tools for parallel programming such as OpenMP and CUDA are introduced within the scope of multimedia applications.     

Distributed Video Coding using Turbo Trellis Coded Modulation

Distributed Video Coding (DVC) has been proposed for increasingly new application domains. This rise is apparently motivated by the very attractive features of its flexibility for building very low cost video encoders and the very high built-in error resilience when applied over noisy communication channels. Yet, the compression efficiency of DVC is notably lagging behind the state-of-the-art in video coding and compression, H.264/AVC in particular. In this context, a novel coding solution for DVC is presented in this paper, which promises to improve its rate-distortion (RD) performance towards the state-of-the-art. Here, Turbo Trellis Coded Modulation (TTCM), with its attractive coding gain in channel coding, is utilized and its resultant impact in both pixel domain and transform domain DVC framework is discussed herein. Simulations have shown a significant gain in the RD performance when compared with the state-of-the-art Turbo coding based DVC implementations.

Error resilient video over multimedia broadcast multicast services (MBMS)

With available data rates for mobile devices constantly increasing, services such as video broadcast and multicast are becoming feasible. A new standard called Multimedia Broadcast Multicast Services (MBMS) is being developed by 3GPP to enable new class of spectrum-efficient multimedia services. Multicast services are expected to serve a diverse user base with varying connectivity and capabilities. We analyze the problem of video error resilience in MBMS services that is critical to maintain consistent quality for end users. The existing error resilience techniques for IP multicasting are not applicable in the MBMS environment. In this paper, we present error resilience techniques that are applicable within the context of the MBMS standard. We propose an Intra Block Refresh method for MBMS services and the results show improved performance. We develop a methodology that can be applied to adapting traditional error resilience tools for the MBMS environment.     

Neural network approach to background modeling for video object segmentation.

This paper presents a novel background modeling and subtraction approach for video object segmentation. A neural network (NN) architecture is proposed to form an unsupervised Bayesian classifier for this application domain. The constructed classifier efficiently handles the segmentation in natural-scene sequences with complex background motion and changes in illumination. The weights of the proposed NN serve as a model of the background and are temporally updated to reflect the observed statistics of background. The segmentation performance of the proposed NN is qualitatively and quantitatively examined and compared to two extant probabilistic object segmentation algorithms, based on a previously published test pool containing diverse surveillance-related sequences. The proposed algorithm is parallelized on a subpixel level and designed to enable efficient hardware implementation.     

The VC-1 Video Coding Standard

VC-1 is a new video coding standard developed by Microsoft and standardized by the SMPTE. VC-1 is one of the three video compression algorithms standardized for high definition DVD. With high definition DVD players expecting to support MPEG-2, H.264, and VC-1, end users do not have to be concerned about the coding formats. The VC-1 standard offers a competitive quality-complexity tradeoff compared to H.264, especially for high-definition services. With a diverse digital video market, we can expect to see VC-1 co-existing with H.264 in the next generation of broadband and broadcast video services.     

Hydrocephalus and cognitive decline in myotonic dystrophy: case report and literature review

Myotonic dystrophy is a rare genetic neuromuscular disorder with multisystem involvement. Hydrocephalus and cognitive deterioration are not commonly considered part of adult-onset myotonic dystrophy. This report describes a case of progressive distal muscle weakness, cognitive decline, and longstanding ventricular enlargement. Review of the literature suggests that hydrocephalus may be associated with myotonic dystrophy while progressive cognitive decline is rare in the adult form of the disease.