Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Thomas, M.Has files: No

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    Multimedia Streaming Using Cloud-Based P2P Systems
    (Elsevier, 2015) Thomas, N.; Thomas, M.; Chandrasekaran, K.
    The past one and a half decades have seen great strides in the field of commercially available distributed computing implementations. The two most popular architectures in the modern world are Peer-to-Peer Systems and Cloud Systems. Peer-to-Peer Systems (P2P) have become very popular in recent times, mainly being used to facilitate file sharing among disparate systems. Another recent trend in modern computing has been the wide scale utilization of Cloud Computing architectures. These systems are used to allow multiple systems to pool their resources and allow other tertiary systems to use these shared resources in bulk for tasks such as data storage, complex calculations, and file sharing. This entails the conceptual outsourcing of various data processing tasks to an external cloud system. Given the two nearly independent functionalities of P2P and Cloud architectures, it is interesting to consider the possibility of fusing these two concepts and researching the applications of the resultant amalgamation. In this research paper, we discuss the theory and application of Cloud Based Peer-to-Peer Systems and their potential application in multimedia streaming services. While the value of P2P Systems and Cloud Computing Systems have been extolled individually, the hybrid of both concepts shows great promise. In this paper we provide an introduction to Cloud Computing Systems, P2P Systems, and the advantages as well as the limitations of both configurations. We then describe the concept of a Cloud-Based P2P System, its basic architecture, and its possible implementations. We also describe the possible application of a Cloud-Based P2P System as a platform for a multimedia streaming service. A proposed algorithm to facilitate streaming in such an application is also described, along with a proposed system model and its advantages. © 2015 The Authors. Published by Elsevier B.V.
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    Detection of largest possible repeated patterns in Indian audio songs using spectral features
    (Institute of Electrical and Electronics Engineers Inc., 2016) Thomas, M.; Vishnu Srinivasa Murthy, Y.V.S.; Koolagudi, S.G.
    In the field of Content Based Music Information Retrieval (CB-MIR), researchers are always looking for better ways to classify songs aside from the existing classifiers such as genre, mood, scale, tempo, etc. By determining a way to isolate and extract maximum length repeating patterns (MLRPs) in a music file, we can analyze them in order to describe another potential classifier: complexity. Extraction of repeating patterns would also allow users to easily extract ringtones from their favorite songs. In this paper, an effort has been made to describe a method to extract repeating patterns from a given music file through direct signal level as well as feature level comparison. These extracted patterns can be used as ringtones, or for analysis to determine complexity. Features such as mel-frequency cepstral coefficients (MFCCs), modulation spectral features (MSFs) and jitter are computed to reduce the computational time observed in signal level comparison. © 2016 IEEE.
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    Detection of similarity in music files using signal level analysis
    (Institute of Electrical and Electronics Engineers Inc., 2017) Thomas, M.; Jothish, M.; Thomas, N.; Koolagudi, S.G.; Vishnu Srinivasa Murthy, Y.V.
    In today's age of digital media, the collection of music files available to the general public is extremely diverse. As with any such set of data, efforts must be made to classify and categorize these files in order to facilitate easy access and searching. Songs can be classified based on attributes available in the music file's metadata such as artist, album, year of release, length, etc. However, if the similarity between two songs is to be determined, a simple comparison of metadata is not only unsatisfactory, the metadata itself might not be available. Therefore, a method of comparison independent of the availability of metadata is required. In this work, a comparison method has been proposed involving the use of musical parameters such as tempo, key and signal envelope, which are extracted from the music file through signal level analysis. Genre is also computed using a support vector machine (SVM) classifier and used to estimate the similarity between two songs. © 2016 IEEE.