Search Word(s) in Title, Keywords, Authors, and Abstract: Kuldeep Gurjar

A Comparative Analysis of Music Similarity Measures in Music Information Retrieval Systems
Kuldeep Gurjar and Yang-Sae Moon
Page: 32~55, Vol. 14, No.1, 2018
10.3745/JIPS.04.0054

Keywords: Content-Based Music Retrieval, MIR System, Music Information Retrieval Survey, Music Similarity Measures
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The digitization of music has seen a considerable increase in audience size from a few localized listeners to a wider range of global listeners. At the same time, the digitization brings the challenge of smoothly retrieving music from large databases. To deal with this challenge, many systems which support the smooth retrieval of musical data have been developed. At the computational level, a query music piece is compared with the rest of the music pieces in the database. These systems, music information retrieval (MIR systems), work for various applications such as general music retrieval, plagiarism detection, music recommendation, and musicology. This paper mainly addresses two parts of the MIR research area. First, it presents a general overview of MIR, which will examine the history of MIR, the functionality of MIR, application areas of MIR, and the components of MIR. Second, we will investigate music similarity measurement methods, where we provide a comparative analysis of state of the art methods. The scope of this paper focuses on comparative analysis of the accuracy and efficiency of a few key MIR systems. These analyses help in understanding the current and future challenges associated with the field of MIR systems and music similarity measures


Comparative Study of Evaluating the Trustworthiness of Data Based on Data Provenance
Kuldeep Gurjar and Yang-Sae Moon
Page: 234~248, Vol. 12, No.2, 2016
10.3745/JIPS.04.0024

Keywords: Data Provenance, Trustworthiness of Data, Data Quality, Trustworthiness Evaluation, Trust Score
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Due to the proliferation of data being exchanged and the increase of dependency on this data for critical decision-making, it has become imperative to ensure the trustworthiness of the data at the receiving end in order to obtain reliable results. Data provenance, the derivation history of data, is a useful tool for evaluating the trustworthiness of data. Various frameworks have been proposed to evaluate the trustworthiness of data based on data provenance. In this paper, we briefly review a history of these frameworks for evaluating the trustworthiness of data and present an overview of some prominent state-of-the-art evaluation frameworks. Moreover, we provide a comparative analysis of two key frameworks by evaluating various aspects in an executional environment. Our analysis points to various open research issues and provides an understanding of the functionalities of the frameworks that are used to evaluate the trustworthiness of data.