M Tech Dissertations

Permanent URI for this collectionhttp://ir.daiict.ac.in/handle/123456789/3

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Subject: search.filters.subject.Statistics

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    Analysis of nonlinearity in speech production mechanism for speaker verification: phase-based approach
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2015) Agrawal, Purvi; Patil, Hemant A.
    Many of the real-world signal processing problems can be described using linear models, and can be realized as analog or digital filter, time-invariant filters; finite or infinite impulse response (IIR or FIR) filters. In the recent past, a nonlinear operator called Teager Energy Operator (TEO) has been introduced and investigated as it has a small window in temporal-domain, making it ideal for local time analysis of signals. This thesis aims to explore the nonlinear nature of the speech production mechanism of a speaker. There has been significant advancement in exploring the source and system-based features for speaker recognition attributed to the characteristics of the excitation source and size and shape of the vocal tract. In this work, TEO phase features are derived from fullband speech signal and then on subband speech signal (due to the property of the TEO being a monocomponent operator). In addition, a feature set is derived from residual phase extracted from nonlinear filter designed using Volterra-Weiner (VW) series exploiting higher-order linear as well as nonlinear relationships hidden in the sequence of samples of speech signal. Experiments have been performed on the score-level fusion of the proposed feature sets with state-of-the-art MFCC features for text-independent Speaker Verification (SV) task, based on Gaussian Mixture Model-Universal Background Model (GMM-UBM) system, respectively. The performance of each feature set is evaluated and a comparative study of each of the features is presented. The results obtained provide an evaluation of the nature of the speech production mechanism and provides features to improve performance of SV system.
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    Wideband active mixer with high gain and high linearity
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2015) Pandey, Vijay Raghav; Gupta, Sanjeev
    An active mixer is presented with improved conversion gain and linearity over a wide range of frequency. The mixer is combined with a low noise amplifier which not only provides braodband input matching but also cancels out noise at the output. The LNA has two stages, input matching stage and noise cancellation stage. The former provides matching for a wide range of frequency and the latter cancels out the noise of the former stage at the output. A PMOS is used to cancel out the non-linear effects of the noise cancellation stage of the LNA thereby improving the linearity of the system. The non-linear effects of input matching stage is canceled by the noise cancellation stage itself. A current bleeding circuit is used to fulfill the large current requirement of the noise cancellation stage and helps in further improvement of the gain. Gilbert cell topology is used which has differential output thereby providing better immunity from noise and fluctuations. The circuit provides a conversion gain of 20 to 24 dB, noise figure of 8 to 13 dB and a linearity of 17.5 dBm for a frequency range of 1 to 6.5 GHz.
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    ItemOpen Access
    Learning to rank: using Bayesian networks
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2011) Gupta, Parth; Mjumder, Prasenjit; Mitra, Suman K.
    Ranking is one of the key components of an Information Retrieval system. Recently supervised learning is involved for learning the ranking function and is called 'Learning to Rank' collectively. In this study we present one approach to solve this problem. We intend to test this problem in di erent stochastic environment and hence we choose to use Bayesian Networks for machine learning. This work also involves experimentation results on standard learning to rank dataset `Letor4.0'[6]. We call our approach as BayesNetRank. We compare the performance of BayesNetRank with another Support Vector Machine(SVM) based approach called RankSVM [5]. Performance analysis is also involved in the study to identify for which kind of queries, proposed system gives results on either extremes. Evaluation results are shown using two rank based evaluation metrics, Mean Average Precision (MAP) and Normalized Discounted Cumulative Gain (NDCG).