Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Pulikala, A.

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    Cochlear Acoustic Model that Improves the Speech Perception in Noise by Encoding TFS
    (Springer Science and Business Media Deutschland GmbH, 2022) Poluboina, V.; Pulikala, A.; Pitchaimuthu, A.
    People with cochlear implants accomplish good speech recognition scores in quiet. Temporal envelope (ENV) is encoded primarily in cochlear implant (CI), and it is sufficient for recognizing speech in quiet. However, temporal fine structures (TFS) are needed for better recognition of speech in noise. Some fine structure coding strategies tried to modulate temporal envelope with TFS. In such coding strategies, FS4 is one that tried to encode fine structures up 950 Hz. In this study, the performance of FS4 with speech recognition in noise was investigated by using acoustic simulation. The speech intelligibility of this study was conducted on five normal-hearing (NH) persons. This performance was compared with 16 channel sinewave vocoder and with the Full band TFS condition. The variance of these three conditions was analyzed using the SNR 50. These results indicate that the fine structure (FS4) coding (up to 1078 Hz Hz) has improved speech recognition in noise compared to the sinewave vocoder. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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    Hardware-Optimized Deep Learning Model for FPGA-Based Character Recognition
    (Institute of Electrical and Electronics Engineers Inc., 2023) Rao, P.S.; Pulikala, A.
    Deep neural networks (DNNs) are widely used algorithms in machine learning. Even though most of the deep learning applications are driven by software solutions, there has been significant research and development aimed at optimizing these algorithms over the years. However, when considering hardware implementation applications, it becomes essential to optimize the design not only in software but also in hardware. In this paper, we present a straightforward yet effective Convolutional Neural Network architecture that is meticulously optimized both in hardware and software for char-acter recognition applications. The implemented accelerator was realized on a Xilinx Zynq XC7Z020CLG484 FPGA using a high-level synthesis tool. To enhance performance, the accelerator employs an optimized fixed-point data type and applies loop parallelization techniques combining 2D convolution and 2D max pooling operations. The hardware efficiency of the proposed DNN is compared with some of the existing architectures in terms of hardware utilization. © 2023 IEEE.
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    Robustness Analysis of EV Charging System using Random Forest Algorithm
    (Institute of Electrical and Electronics Engineers Inc., 2023) Barre, U.P.V.; Satyanarayan, S.; Reddy, H.; Pulikala, A.; Bajaj, A.
    Electric cars offer numerous benefits and are considered the future of the automobile industry. However, their worldwide adoption still needs to grow. One of the primary reasons for this delay in electrification is charge anxiety, which refers to the uncertainty customers feel when connecting the charging cable to the car. To address this issue, this study analyses the performance of the charging system using a machine learning model to identify sensitive signals that influence the charging process and can cause successful charging or charge termination. The analysis will also help to define robust operating regions where the charging component can reliably function, regardless of external conditions. This study's findings will provide insights into electric vehicle charging behavior with the supply station. © 2023 IEEE.
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    Speech Intelligibility Enhancement for Cochlear Implant using Multi-Objective Deep Denoising Autoencoder
    (Institute of Electrical and Electronics Engineers Inc., 2023) Vishnu, B.U.P.; Poluboina, V.; Sushma, B.; Pulikala, A.
    This study introduces a novel technique for enhancing the performance of deep denoising autoencoders (DDAE) in speech processing for cochlear implants (CIs). For individuals with hearing loss, cochlear implants are electronic devices that help to restore their ability to hear. However, the performance of CIs speech intelligibility in the noisy environment is limited. One of the most commonly used methods for reducing noise in CIs is through a preprocessing technique called deep denoising autoencoder. DDAE models have shown potential in learning various noise patterns, but their performance in enhancing speech intelligibility is relatively low due to a ineffective objective function. To address this limitation, this study proposes a multi-objective technique to fine-tune the DDAE model. When multiple objectives are optimized simultaneously, the model becomes more robust and better at handling real-time noise. Based on the experimental findings, it has been confirmed that the proposed multi-objective learning technique performs better than other models when it comes to speech intelligibility. Furthermore, the enhanced signal is presented to the acoustic cochlear implant simulator to evaluate the improvement of speech intelligibility in CIs. © 2023 IEEE.