Browsing by Author "Panahi, Issa M. S."
Now showing 1 - 9 of 9
- Results Per Page
- Sort Options
Item Fault Handling for Medium-voltage (MV) Grids(2022-12-01T06:00:00.000) Nourmohamadi, Hesam 1993-; Balsara, Poras T.; Moheimani, S.O. Reza; Gohil, Ghanshyamsinh; Zhang, Jie; Panahi, Issa M. S.This thesis provides an overview of the fault detection and protection methods for medium- voltage grids. First, it discusses and review the evolving direct-current medium-voltage (MVDC) grids and their application for various on-shore and off-shore cases. It then explores provided techniques and solutions in the literature to study challenges related to the short-circuit faults that a grid might be prone to them. Advantages and disadvantages of each technique are investigated. This is done with the ultimate goal to propose a new and fast fault detection, classification and location control method to be implemented for any given medium-voltage grid for prompt fault analysis. The so called proposed Grid Transient Classifier-Active Impedance Estimation (GTC-AGIE) provides a two-step fault detection, classification and location method based on the artificial neural network (ANN), wavelet transform (WT) and active high-frequency signal injection. The GTC part decomposes voltage and current signals using WT to extract feature vectors. Then, by the aid of two separate ANN, fault type and an estimation of its location (zone and side where fault has occurred) can be identified. The AGIE plays a complementary role to calculate fault resistance and its distance in a particular zone and side, which are identified by GTC. The AGIE performs its function by injecting a small duration high-frequency signal into the grid and then calculates corresponding impedance to retrieve fault distance and resistance. Shipboard MVDC system is considered as the case study to investigate applicability of the proposed method. Shipboard grid includes several power and voltage stages with various interconnections and load zones in a compact structure with small distances. Compared to alternative current (AC) system, fault current rises quickly in DC ones and a very fast fault analysis is required. Hence, shipboard MVDC is considered as a good case study to examine the effectiveness of the proposed GTC-AGIE. In the following, some solid-state fault current limiter (FCL) topologies for grid protection are reviewed and their advantages and disadvantages are assessed to identify potential areas for improvements. Finally, a novel intelligent multi-functional fault current limiter (IMFCL) topology is proposed to provide protection over short circuit faults and also address any voltage sag/swell by operating as dynamic voltage restorer (DVR) in a hybrid medium voltage alternative current (MVAC) and MVDC grid. A simple fault disturbance detector is proposed to quickly identify voltage sag/swell or fault current conditions. Furthermore, in case of any fault occurrence, control system in IMFCL injects a short duration high-frequency signal into the grid to quickly calculate system impedance in new condition. By knowing the impedance, it is possible to calculate fault resistance and estimate fault location. Both simulation and hardware-in-the-loop (HIL) results are presented throughout the thesis to evaluate performance of the proposed GTC-AGIE and IMFCL.Item Investigation of Improved Masking Noise for the Speech Privacy(2016-12) Cho, Allen; Panahi, Issa M. S.Many sound masking products for the purpose of achieving speech privacy have emerged on the market in recent years. Over the time, quality of the masking noise has been improved a lot and still is studied for a better method. This thesis introduces a new way of generating masking noise which outperforms the commonly used methods. Using Adaptive Linear Predictive Coding (LPC) we can derive the model for various target sound and create a new masking signal which has exactly the same spectral envelope of the target sound and different phase. Two important aspects in evaluating the performance quality of the masking noise are masking capability and pleasantness. By testing these criteria in objective and subjective ways, we show superior performance of the proposed adaptive method in comparison with several existing techniques.Item Multi-channel Acoustic Signal Processing on Edge Devices(May 2023) Kovalyov, Anton; Natarajan, Sriraam; Panahi, Issa M. S.; Busso-Recabarren, Carlos A.; Sisman, Berrak; Kiasaleh, KamranMicrophone arrays are useful in determining the space-time structure of an acoustic field. They are widely employed in many popular acoustic signal processing applications, including speech enhancement, speech separation, sound source localization, and sound source tracking. This dissertation introduces a set of practical and efficient multi-channel acoustic signal processing algorithms specifically targeted for improving people's spatial awareness and hearing towards sources of interest using edge devices featuring a microphone array. Such devices include smartphones, smart glasses, and hearing aids. As proof of feasibility, the majority of the developed algorithms have been in fact deployed as mobile applications for smartphones. First, Directional Signal Extraction Network (DSENet) is proposed. DSENet is a real-time, computationally-and-memory-efficient neural network which extracts a signal source located within a predefined directional region of interest. Experimental results show that DSENet is capable of outperforming oracle beamformers and state-of-the-art (SOTA) networks in low-latency causal speech separation while incurring a system latency of only 4 ms. Second, a complete method for highly accurate and efficient real-time estimation of 2-dimensional direction of arrival (2D-DOA) using a nonlinear 3-microphone array is presented. The proposed method provides the ability to estimate and track azimuth and elevation angles of one or more acoustic sources in real-time. Third, in an attempt to increase the number of microphones for improved acoustic processing performance, a distributed, real-time, low-latency audio input/output (I/O) framework for mobile devices is proposed. This framework can simulate an irregular and flexible microphone array by wirelessly synchronizing and processing multi-channel audio input of multiple mobile devices into real-time output. Fourth, a method for jointly calibrating and synchronizing two arrays of microphones and loudspeakers is described. This method allows finding the clock offset between two devices featuring an array of microphones and loudspeakers, as well as estimating their exact relative positions. Fifth, Delay-Filter-and-Sum Network (DFSNet) is proposed. DFSNet is a steerable neural beamformer invariant to microphone number and array geometry for real-time, low-latency speech enhancement. Apart from low latency, DFSNet is designed to incur controllable distortion and low memory and computational complexities, making it especially suitable for hearing aid applications. Comparison with SOTA reveals high performance approaching noncausal methods.Item Multi-Channel Dynamic-Range Compression Techniques for Hearing Devices(2018-08) Zou, Ziyan; Panahi, Issa M. S.Dynamic-range compression (DRC) is an important component in hearing aid devices (HADs). Research of multi-channel DRC design and real-time implementation has been carried out in the last few decades. The trade-offs of every DRC system include the frequency resolution, computational complexity and processing time delay. In this thesis, a crossover filter bank based nine-channel DRC with an optimized structure is proposed. A polyphase extension of this approach is then applied and a compensation filter is proposed to reduce the distortion. Then a subband filter bank is designed and implemented for multi-channel DRC to reduce the computational complexity. The final method to optimize a multi-channel DRC is to use Equalization technique in frequency domain, which further reduces the computational complexity. In this work, all the methods are implemented on a smartphone to work as an assistive device to hearing aids. Objective and subjective evaluation of the developed methods show the improvement in quality and intelligibility.Item Noise-Robust Speech Source Localization and Tracking Using Microphone Arrays for Smartphone-Assisted Hearing Aid Devices(2018-05) Ganguly, Anshuman; 0000-0003-4409-3088 (Ganguly, A); Panahi, Issa M. S.Speech Source Localization (SSL) (or Direction of Arrival estimation) is a powerful pre-processing tool that helps identify the direction of the talker of interest in a noisy environment using multiple fixed microphones (known as a Microphone Array). This information is very helpful to the speech-processing pipeline and can be utilized to improve the performance of the overall system. With recent advancements, smartphones now possess the requisite hardware and computational power to perform real-time SSL for different applications. In this work, we propose application-specific SSL algorithms for three types of microphone arrays and show their effectiveness for smartphone implementation under realistic background noise conditions. We evaluate our proposed approaches in several realistic noisy conditions and present object evaluations to demonstrate the effectiveness of the proposed methods. We also propose the real-time implementation of some of our methods on the latest smartphones and smartphone-assisted devices.Item Real Time Implementation of Direction of Arrival Estimation on Android Platforms for Hearing Aid Applications(2018-08) Kucuk, Abdullah; Panahi, Issa M. S.Sound Source Localization (SSL) is one of the vital areas in signal processing, especially in hearing aid applications. SSL (or Direction of Arrival) helps to determine the location of the speaker via multiple fixed microphones (also known as microphone array). Knowing of speaker Direction of Arrival (DOA) helps to improve the performance of the system. Another advantage of DOA is that it helps hearing-impaired people to locate talker because hearing aid users, especially those who are older than 60 years, have difficulties in determining the place of the speaker. Having requisite processing capabilities and at least two microphones makes smartphones a cost-effective solution for multi-channel audio signal processing. In this thesis, we propose a new stereo input/output framework for Android platforms for audio signal processing. This frame enables us to perform multi or single channel audio signal processing for real-time operations. We also propose a method for two microphones-based Direction of Arrival (DOA) estimation and real-time implementation of this method on the latest Android smartphones.Item Smartphone-Based Single and Dual Microphone Speech Enhancement Algorithms for Hearing Study(2018-05) Bhat, Gautam Shreedhar; 0000-0002-5510-6385 (Bhat, GS); Panahi, Issa M. S.Speech Enhancement (SE) is elemental in many real world applications. In the last two decades, extensive studies have been carried out on single and multi-channel SE techniques. In this thesis, three novel SE algorithms have been proposed that can be used for Hearing Aid Devices using a smartphone as their assistive device. The first SE method exploits the information of formant locations to improve the speech quality and intelligibility of the Super-Gaussian Joint Maximum aposterori (SGJMAP) SE method. The second method is the extension of this work on the Log Spectral Minimum Mean Square Error Amplitude Estimator (Log-MMSE) which is a well-known SE algorithm. The third method is a real time Blind Source Separation (BSS) method based on Independent Vector Analysis (IVA) for convolutive mixtures. Objective and subjective evaluation of the developed techniques show substantial improvements in speech quality and intelligibility.Item Speech Analysis and Single Channel Enhancement to Improve Speech Intelligibility for Cochlear Implant Recipients(2017-05) Wang, Dongmei; Hansen, John H. L.; Panahi, Issa M. S.; Busso, Carlos A.; Assmann, Peter F.Cochlear implant (CI) devices are able to help deaf individuals recover hearing ability by surgically inserting electrode arrays into the inner ear, to stimulate the auditory nerve and transmit the sound to the auditory cortex in the brain. CI listeners achieve high speech intelligibility in quiet environments, however their speech perception degrades dramatically when presented in noisy backgrounds. This is especially true in fluctuating noise, such as competing-speaker or babble noise, where CI users have difficulties in speech understanding. One of the reasons is that low spectral resolution provided by CI encoding strategies is insufficient to distinguish speech components from noise. In this dissertation, we propose a new speech enhancement solution to improve speech intelligibility for CI recipients in noise. Speech energy is primarily carried in the harmonic structure located at multiple integer harmonics of the fundamental frequency. In order to combat noise, we propose to use harmonic structure as the frequency domain cues to estimate the degraded noise. The proposed speech enhancement is based on harmonic structure estimation combined with a traditional statistical based leveraged solution. This dissertation has investigated robust fundamental frequency estimation in noise, along with integrating such novel in formulate to improve harmonic based speech enhancement in both stationary and non-stationary noise scenarios. Noise-robust pitch estimation is proposed based on temporal harmonic structure in local time-frequency (TF) segments. To reduce the noise affect, we take advantage of the sparsity of speech signal that only the high signal to noise ratio (SNR) TF segments are used for pitch estimation. Robust harmonic features are investigated for neural network classification based pitch estimation. The harmonic features map the pitch candidates into a more separable space for classification. Experimental results show that the proposed pitch estimation method improves global pitch error in noise. Next, harmonic structure estimation is combined with the traditional statistical based method to speech enhancement. Noise estimation is performed based on harmonic structures. The estimated noise variance is employed in a traditional MMSE framework for a prior and posterior SNR estimation to obtain a gain function for the target speech. Listening experiments with CI subjects demonstrated improved speech intelligibility for both stationary and non-stationary noise.Item User Customizable Real-Time Single and Dual Microphone Speech Enhancement and Blind Speech Separation for Smartphone Hearing Aid Applications(2018-05) Karadagur Ananda Reddy, Chandan; Panahi, Issa M. S.Speech Enhancement (SE) is a vital algorithmic component in the Hearing Aid pipeline. Over the years, several algorithms have been developed to work in real-time and to improve the quality and intelligibility of speech. However, noise suppression with minimal distortion to speech is still a prime challenge that needs to be addressed. In this work, a new single microphone SE is introduced that is implemented on a smartphone to work as an assistive device to Hearing Aids via wireless connectivity. The uniqueness of the developed method is in the introduction of varying parameters that allow the smartphone user to control the amount of noise suppression and speech distortion in real-time, which allows the user to customize the perceptual audio to their preference. A super-Gaussian extension of this approach is explored and analyzed. With the recent accessibility of the two microphones on the smartphones, doors were opened to use beamformer as a pre-filtering stage to the proposed single microphone SE. Real-time blind speech separation technique is also proposed to yield superior quality for speech. Objective and subjective results show that the developed methods outperform traditional SE techniques.