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X-WR-CALDESC:Events for EE
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TZID:Asia/Kolkata
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DTSTART:20220101T000000
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DTSTART;TZID=Asia/Kolkata:20220909T200000
DTEND;TZID=Asia/Kolkata:20220909T210000
DTSTAMP:20260618T043438
CREATED:20220905T052514Z
LAST-MODIFIED:20220905T052514Z
UID:239884-1662753600-1662757200@ee.iisc.ac.in
SUMMARY:Thesis Colloquium of Anurenjan P. R.
DESCRIPTION:Venue\, Date and Time: MMCR\, EE\, IISc. 9-9-2022\, 2.30-3.30pm.\n\n\nTeams link – https://tinyurl.com/3bpmjxkx . \nTitle: Dereverberation of speech using frequency domain linear prediction \nFaculty Advisor: Dr. Sriram Ganapathy \nAbstract : The speech-based technologies are radically changing the way we interact with systems and how we access information. In many of these applications\, the users prefer to interact with the system through a far-field microphone without the nuance of a handheld or body-worn device. Examples of such applications are automated meeting analysis\, speech-based dictation systems\, hands-free interfaces for controlling consumer-products\, IoT\, virtual assistants in mobile phones and smart speakers. The major challenge in capturing speech from the far-field is the degradation of the signal quality due to reverberation. Reverberation refers to the delayed and weighted summation of the direct component of the speech signal with the reflected versions. This talk is focused on developing methods for speech dereverberation\, i.e.\, restoring the functional quality of reverberated speech\, using the signal analysis technique of frequency domain linear prediction (FDLP). \nThe FDLP is the frequency domain dual of the conventional Time Domain Linear Prediction (TDLP). Just as the TDLP estimates the spectrum of a signal\, the FDLP estimates the temporal envelopes of the signal using an autoregressive model. We apply the FDLP approach to the sub-bands of speech signal that are distributed in the mel scale. \nThis talk will describe two broad directions for addressing issues in the far-field speech using the FDLP approach. In the first part of the talk\, we explore a front-end design for automatic speech recognition (ASR) applications that suppresses the reverberation artifacts in the FDLP envelope. In the second part of the thesis\, we develop a speech enhancement model using the envelope and carrier decomposition given by the FDLP technique. \nIn the design of the ASR front end\, I will discuss a novel approach for 3-D acoustic modeling framework\, where the spatio-spectral features from all the sub-band channels are extracted. The features that are input to the 3-D CNN are extracted by modeling the signal peaks in the spatio-spectral domain using a multi-variate autoregressive modeling approach. In the subsequent part of this section\, I will describe a neural model for speech dereverberation using the long-term sub-band envelopes of speech. The neural dereverberation model estimates the envelope gain\, which when applied to reverberant signals\, allows the suppression of the late reflection components. The de-reverberated envelopes are used for feature extraction in speech recognition. The key novelty in this model is the joint learning of the reverberation and the ASR system. In these ASR experiments using the proposed framework\, we illustrate significant performance gains over previously proposed front ends. \nThe second part of the thesis deals with the FDLP based speech dereverberation for enhancement applications\, where the goal is to restore the audible quality of the speech signal. For this task\, we decompose the sub-band speech signal into the constituent envelope and carrier part. A dereverberation neural model is designed that attempts to enhance the envelope and carrier signals jointly. Further\, joint learning of the speech enhancement model with the end-to-end ASR model is proposed with a single neural framework. The proposed model therefore can generate improved audio quality and provide robust representations for far-field ASR. Finally\, I will illustrate the subjective quality improvement of the audio signal as well as the improvement in ASR performance obtained by the proposed envelope-carrier model. \nAcknowledgement \nThis work was partly supported by project grants from Samsung Research India\, Bangalore and the College of Engineering\, Trivandrum\, Kerala.  \nBio: Mr. Anurenjan is a PhD student at the LEAP lab\, Electrical Engineering\, IISc. He is also currently working as Assistant Professor in College of Engineering\, Trivandrum. Mr. Anurenjan completed his Bachelors in Technology from Government Engineering College\, Barton Hill\, Trivandrum\, Kerala in 2006 and his Masters in Technology from College of Engineering\, Trivandrum\, Kerala in 2008. He joined the LEAP lab as a PhD candidate under AICTE-QIP program in the year 2017. He hails from Trivandrum district of Kerala. He is interested in signal processing\, machine learning and speech processing. Mr. Anurenjan is a student member of IEEE SPS and the ISCA. During his free hours\, Mr. Anurenjan likes to play badminton and swimming.  \n——- \nAll are invited. Coffee/Tea will be served before the talk. 
URL:https://ee.iisc.ac.in/event/thesis-colloquium-of-anurenjan-p-r/
LOCATION:EE\, MMCR
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DTSTART;TZID=Asia/Kolkata:20220921T223000
DTEND;TZID=Asia/Kolkata:20220921T233000
DTSTAMP:20260618T043438
CREATED:20220920T230706Z
LAST-MODIFIED:20220920T231050Z
UID:239893-1663799400-1663803000@ee.iisc.ac.in
SUMMARY:Online Lecture by Dr. Mohammad Hedayati @5pm
DESCRIPTION:Title: High Voltage DC Circuit Breakers (HVDC CB) \nSpeaker: Dr. Mohammad Hedayati\, Dyson Technology\, UK. \nTime and Venue: 5 pm\, MS Teams/Online mode (Link provided below) \nAbstract: The main challenge of the DC meshed grid is the lack of suitable fault protection devices. The AC circuit breaker cannot be used in the DC system as in DC there is no zero crossing to extinguish the arc. Hence the DC circuit breakers need to create a virtual current zero crossing. There are different types of DC CB\, and three of them (solid state\, mechanical\, hybrid circuit breaker) are explained in this talk. The advantages and disadvantages of each technology are then pointed out. \nSpeaker Bio: Dr. Mohammad Hedayati did his Master and PhD in department of Electrical Engineering\, IISc between 2008 to 2016. Then he moved to University of Aberdeen as a postdoc\, where he was working on DC CB for DC meshed grids in 2016. In 2018 he joined University of Bristol working on GaN Devices Reliability and health monitoring and switching characteristics. In 2021 he joined Dyson technology\, during this time he was designing high speed (150kRPM) motor drive inverters for vacuum cleaner and personal care applications. In October 2022 he will be joining Jaguar Land Rover (owned by Tata group) to design EV motor drive inverters. \nAll are welcome. \nMicrosoft Teams meeting \nJoin on your computer or mobile app \nClick here to join the meeting
URL:https://ee.iisc.ac.in/event/online-lecture-by-dr-mohammad-hedayati-5pm/
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DTSTART;TZID=Asia/Kolkata:20220926T163000
DTEND;TZID=Asia/Kolkata:20220926T173000
DTSTAMP:20260618T043438
CREATED:20220926T010719Z
LAST-MODIFIED:20220926T010758Z
UID:240014-1664209800-1664213400@ee.iisc.ac.in
SUMMARY:Thesis Defence of Mr. Ahmad Arfeen @ 11am
DESCRIPTION:Title:  Data Efficient Domain Generalization \nFaculty Advisor: Prof. Soma Biswas \nExaminer: Prof. Venkatesh Babu R. (CDS\, IISc) \nDate: Monday\, September 26\, 2022 \nTime: 11:00 AM \nVenue:  MMCR (EE dept) \nAbstract: For the task of image classification\, in general\, the test data is assumed to come from the same distribution as the training data. But this may not always hold in real-life scenarios. For example\, in night-time surveillance\, we may need to classify images captured using NIR cameras\, but the available model has been trained on RGB images. Domain generalization (DG) addresses the problem of generalizing classification performance across any unknown domain\, by leveraging training samples from multiple source domains. In this thesis\, we address two challenging scenarios for the DG task\, with focus on data efficiency. Currently\, the training process of majority of the state-of-the-art DG-methods is dependent on a large amount of labeled data. This restricts the application of the models in many real-world scenarios\, where collecting and annotating a large dataset is an expensive and difficult task. \nAs the first contribution\, we address the problem of Semi-supervised Domain Generalization (SSDG)\, where the training set contains only a few labeled data\, in addition to a large number of unlabeled data from multiple domains. This is relatively unexplored in literature and poses a considerable challenge to the state-of-the-art DG models\, since their performance degrades under such condition. To address this scenario\, we propose a novel Selective Mixing and Voting Network (SMV-Net)\, which effectively extracts useful knowledge from the set of unlabeled training data\, available to the model. Specifically\, we propose a mixing strategy on selected unlabeled samples on which the model is confident about their predicted class labels to achieve a domain-invariant representation of the data\, which generalizes effectively across any unseen domain. Extensive experiments on two popular DG-datasets demonstrate the usefulness of the proposed framework. \nThe second contribution of this thesis is a novel approach for the task of Zero-Shot Domain Generalization (ZSDG). This is very challenging since the query data can belong to an unseen class as well as unseen domain. For this task\, we address the challenge of class imbalance by learning class specific classifier margins\, which not only maintains the semantic relationship of the classes in the embedding space\, but is also discriminative\, and thus improves the classification performance on the test data. Extensive experiments on multiple datasets justify the effectiveness of the proposed approach. \n*************************************************************************************************** \nALL ARE WELCOME
URL:https://ee.iisc.ac.in/event/thesis-defence-of-mr-%e2%80%afahmad-arfeen-11am/
LOCATION:EE\, MMCR
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