Theses and Dissertations

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    Development of Countermeasures for Voice Liveness and Spoofed Speech Detection
    (Dhirubhai Ambani Institute of Information and Communication Technology, 2022) Chodingala, Piyushkumar Kiritbhai; Patil, Hemant A.
    An Automatic Speaker Verification (ASV) or voice biometric system performs machine based authentication of speakers using voice signals. ASV is a voice biometric system which has applications, such as banking transactions using mobile phones. Personal information, and banking details, demand more robust security of ASV systems. Furthermore, the Voice Assistants (VAs) are also known for the convenience of controlling most of the surrounding devices, such as user�s personal device, door locks, electric appliances, etc. However, these ASV and VA systems are also vulnerable to various spoofing attacks, such as details, twins, Voice Conversion (VC), Speech Synthesis (SS), and replay. In particular, the user�s voice command can be conveniently recorded and played back by the imposter (attacker) with negligible cost. Hence, the most harmful attack (replay attack) of morphing user�s voice command can be performed easily. Hence, this thesis aims to develop countermeasure to protect these ASV and VA systems from replay attacks. In addition, this thesis is also an attempt to develop Voice Liveness Detection (VLD) task as countermeasure for replay attack. In this thesis, the novel Cochlear Filter Cepstral Coefficients based Instanta neous Frequency using Quadrature Energy Separation Algorithm (CFCCIF-QESA) feature set is proposed for replay Spoofed Speech Detection (SSD) on ASV systems. Performance of the proposed feature set is evaluated using publicly avail- able datasets such as, ASVSpoof 2017 v2.0 and BTAS 2016. Furthermore, the significance of Delay and Sum (DAS) beamformer over state of the art Minimum Variance Distortionless Response (MVDR) for replay SSD on VAs. Finally, the wavelet based features are proposed for VLD task. The performance of proposed wavelet-based approaches are evaluated using recently released POp noise COr pus (POCO).
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    ItemOpen Access
    Beamforming for mitigation of interference for different antenna array geometry
    (2021) Kumari, Komal; Vasavada, Yash; Gupta, Sanjeev
    DOA estimation and Beamforming is one of the most crucial requirements of the Communication system design of RADAR, Satellite communication, Wireless communication. This Estimation performance differs for different antenna-array geometry. Remarkable progress in algorithm development has been made over the last decade. Using a sensor array of a particular configuration, we can improve the parameter estimation accuracy from the observation data in the presence of interference and noise. This Thesis is interested in how different algorithms for estimation perform for different antenna-array geometry and their comparative performance study. This motivates us to study interference and jamming attacks and comparative study on their performance. We will focus on conventional matched filter-based Beamforming, MVDR, MUSIC, and Expectation Maximization. We will review the version and limitations of these methods. A simulation model in MATLAB is used for the performance analysis. This thesis provides an overview of the conventional beamforming method, matched filtering method, MVDR, Expectation-Maximization, and the MUSIC method for DOA estimation and a study of their estimation error for ULA. We analyzed the planar array and circular array and MUSIC algorithm for both. We researched different interference cancellation techniques and performed their MATLAB comparative simulation study.
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    Beamforming using learning based algorithms
    (2020) Parekh, Naitik; Vasavada, Yash
    With an increasing number of subscribers to the terrestrial cellular satellite-based services, there is a resultant rise in the demand for the data rate, and there is a growing need for advanced antenna and signal processing schemes that improve the power and the spectral efficiencies. Adaptive beamforming using antenna arrays is one such technique. When multiple signals are impinging on the antenna array, beamforming can be used for increasing the signal to noise ratio (SNR) (achieved by increasing the Directivity of the formed beam along the direction of interest) and thereby for source separation/interference mitigation. In this thesis, we propose several new algorithms to improve the practical effectiveness of beamforming. These algorithms range from computationally complex closed-form solution to iterative estimation and optimization techniques. Out of all these algorithms, some require precise knowledge of the channel model, or some are based on prior assumptions, which, when violated, will deteriorate the performance of the system. The Neural Network (NN) based solutions are gaining popularity in communication system design. The NN operates in the blind mode, and it does not require a detailed a-priori mathematical model of the channel. It has shown some promising results in terms of accurately approximating some known algorithms with reduced complexity. The NN can effectively trade the performance with the complexity. Most of the applications of the NN aim at reducing the computational complexity of the existing approaches; little or no efforts have been spent to come up with an indigenous approach to do beamforming using NN.We have proposed a few beamforming schemes using NN. Our results show that the learned models can provide improvements in the suppression of interference and the number of pilot symbols required.