Theses and Dissertations

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    Novel filtering based approach for epoch extraction
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2015) Bachhav, Pramod; Patil, Hemant A.
    Accurate estimation of source excitation features is important in many speech analysis-synthesis applications. According to source-filter theory, separating source features from speech signal is basically a deconvolution problem [1]. For voiced speech, the vocal tract is excited with a sequence of impulse-like glottal pulses. The extent of excitation is significant around these pulses. The work presented in this thesis is aimed at estimating the instants of significant excitation of vocal tract system which occur at glottal closure instants (GCIs), also known as epochs [2]. Unlike the conventional methods, we have proposed a method which does not require the modelling of vocal tract system for epoch estimation and thus, does not use the traditional linear prediction residual (LPR). The proposed epoch extraction method uses lowpass filter on the positively clipped and negated speech signal followed by peak detection. The method assumes the quasiperiodicity of speech signal. The lowpass filtering removes the vocal tract characteristics from the speech signal and a peak detection method is employed to detect the epoch candidates. The method has been evaluated over a phonetically balanced database and compared with the other state-of-the art methods, viz., Speech Event Detection using the Residual Excitation And a Mean-based Signal (SEDREAMS) and the Zero Frequency Resonator (ZFR)-based method. The proposed method gave comparable or better results on clean as well as noisy speech signals. In addition, using the estimated epoch locations, we proposed an event-based approach for pitch estimation. In this work, we have also presented an approach to evaluate the performance of a pitch estimation algorithm in the absence of ground truth. The proposed pitch estimation approach has been compared with other state-of-the-art pitch extraction methods, in the framework of voice conversion.
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    ItemOpen Access
    Road detection for intelligent transport systems
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2015) Shah, Falak; Shah, Pratik; Dubey, Rahul
    Road detection is an important machine vision problem with applications to driver assistance systems and autonomous vehicles. We carried out a literature survey of the state of the art road detection algorithms. Simulations of these algorithms were performed on road images taken from multiple datasets which revealed certain limitations such as failure under shadows or in the absence of lane markers. This is why the past few years saw the emergence of illuminant invariant based road detection techniques as the state of the art. As the name suggests, illuminant invariant is a feature which contains the colour information of the surface being captured independent of the illumination source. However, the derivation of illuminant invariant image from the RGB image makes use of the assumption that the surface being captured is lambertian. The smooth road surfaces that reflect sunlight are specular and they violate the lambertian assumption. Thus, the algorithms based on illuminant invariant feature fail to detect the road region containing specularities. The road detection algorithm functions by building a road model in the illuminant invariant feature space for each frame. The white markings that are painted over the roads in the form of zebra crossings, lane markers and arrows are not included into the road model. Hence, the algorithm fails to detect them as a part of road region. The first contribution of this thesis is to address the limitations of specularities and lane markers, thus improving the robustness of the state of the art road detection algorithm. We propose a novel specularity detection and removal method for road scenes which also removes the white markings present in the road image. The region of the image containing specularities/ markers is filled with same shade as its surrounding region. Any road detection algorithm has two aspects- the first is robustness and next is real time implementation. The second contribution of this thesis is implementation of the proposed algorithm on BeagleBone Black and Rapsberry pi-2, which are low cost, low-power single-board computers. This provides a proof-of-concept of real time computation. Thus, the thesis improves the accuracy of the state of the art path detection and provides means of real time implementation on mobile platforms.