Browsing by Author "Hegde, P."

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A deep learning approach to detect drowsy drivers in real time
(Institute of Electrical and Electronics Engineers Inc., 2019) Pinto, A.; Bhasi, M.; Bhalekar, D.; Hegde, P.; Koolagudi, S.G.
Fatigue and microsleep are the reasons behind many severe road accidents. These can be avoided if the symptoms of fatigue are detected on time. This paper describes a real-time system for monitoring driver vigilance. Driver drowsiness detection algorithms in the past have proven to work in controlled environments but have not been implemented on a wide scale as of yet. Algorithms in the past suggest calculating a scalar value known as Eye Aspect Ratio (EAR) and detect drowsiness by comparing its instantaneous value with a previously configured value. We propose a generalised approach using Convolution Neural Networks (CNN) in this paper. Our algorithm tracks the driver's eyes and feeds it into a pre-trained that predicts the state of the eye. Once the prediction is obtained, we would be able to detect if the driver is drowsy or not. The main components of our system include a camera, for real time image acquisition, a processor for running algorithms to process the acquired image and an alarm system to warn the driver when the symptoms are detected in order to avoid potential accidents. Â© 2019 IEEE.
An Improved Method for Speech Enhancement Using Convolutional Neural Network Approach
(Institute of Electrical and Electronics Engineers Inc., 2022) Mahesh Kumar, T.N.; Hegde, P.; Deepak, K.T.; Narasimhadhan, A.V.
In the speech processing domain Speech enhancement is one of the most widely used techniques. With the development of deep neural networks and the availability of powerful hardware, multiple deep learning-based speech enhancement models have come up in recent years. In this work, the speech enhancement technique using a Convolutional Neural Network(CNN) as Denoising Autoencoders (DAEs) is investigated and compared with the conventional feed-forward topology. Further, The proposed model is analyzed at various SNR levels to process the corrupted english speech and also tested on unseen speech data which includes additional SNR levels. It is observed from simulation results that the proposed model outperforms the existing model in terms of Perceptual Evaluation of Speech Quality (PESQ) and Log Spectral Distance (LSD). The network achieved 3% higher scores than feed-forward neural networks, and it is found that the convolutional DAEs perform better than feed-forward counterparts. Â© 2022 IEEE.
Crack Density and Length Detection using Machine Learning
(Avestia Publishing, 2024) Koushik, M.; Hegde, P.; Rudra, B.
This study presents a comprehensive approach for detecting and analyzing microscopic cracks in rock samples using computer vision techniques and machine learning algorithms. The proposed methodology involves image segmentation, crack detection, length, and density prediction, utilizing a combination of image processing techniques and linear regression modeling. Microscopic rock images captured at various temperatures were analyzed to detect and measure cracks accurately. The developed system demonstrated effective crack detection and length measurement capabilities, aided by image segmentation, edge detection, and feature extraction methods. Moreover, the application of linear regression facilitated the prediction of crack parameters, exhibiting a clear relationship between crack characteristics and temperature variations. The findings contribute to a deeper understanding of crack formation mechanisms in rocks under different temperature conditions, offering valuable insights for geological studies and infrastructure integrity assessments. Â© 2024, Avestia Publishing. All rights reserved.
Efficient Lubricity Improvers Derived from Methyl Oleate for Ultra Low Sulphur Diesel (ULSD)
(Pleiades Publishing, 2022) Sruthi, H.; Udayakumar, D.U.; Hegde, P.; Manjunatha, M.G.; Nandakumar, V.
Abstract: A new series of lubricity improvers for ultra-low sulphur diesel (ULSD) was synthesized starting from methyl oleate through simple chemical reactions. In the first step methyl oleate was epoxidized using formic acid and hydrogen peroxide mixture. Then the epoxide was subjected to esterification using different long chain (C4–C18 alkyl groups) organic acids to get the final diesters. The lubricating property of the newly synthesized diesters was studied by dosing them to ULSD at 300 ppm (wt/vol) concentration. Amongst them, diester LAMOSA derived from stearic acid showed the best lubrication enhancing property at 300 ppm dosage level. The SEM and EDS spectra of the HFRR (high frequency reciprocating rig) specimen also confirms the formation of metal-oxygen interaction supporting the friction reducing properties of LAMOSA. The study reveals that the newly synthesized methyl oleate derived diesters are promising materials as lubricity additives for ULSD. [Figure not available: see fulltext.] © 2022, Pleiades Publishing, Ltd.
Evaluation of implant properties, safety profile and clinical efficacy of patient-specific acrylic prosthesis in cranioplasty using 3D binderjet printed cranium model: A pilot study
(Churchill Livingstone, 2021) Basu, B.; Bhaskar, N.; Barui, S.; Sharma, V.; Das, S.; Govindarajan, N.; Hegde, P.; Perikal, P.J.; Antharasanahalli Shivakumar, M.; Khanapure, K.; Jagannatha, A.
There exists a significant demand to develop patient-specific prosthesis in reconstruction of cranial vaults after decompressive craniectomy. we report here, the outcomes of an unicentric pilot study on acrylic cranial prosthesis fabricated using a 3D printed cranium model with its clinically relevant mechanical properties. Methods: The semi-crystalline polymethyl methacrylate (PMMA) implants, shaped to the cranial defects of 3D printed cranium model, were implanted in 10 patients (mean age, 40.8 ± 14.8 years). A binderjet 3D printer was used to create patient-specific mould and PMMA was casted to fabricate prosthesis which was analyzed for microstructure and properties. Patients were followed up for allergy, infection and cosmesis for a period of 6 months. Results: As-cast PMMA flap exhibited hardness of 15.8 ± 0.24Hv, tensile strength of 30.7 ± 3.9 MPa and elastic modulus of 1.5 ± 0.1 GPa. 3D microstructure of the semi-crystalline acrylic implant revealed 2.5–15 µm spherical isolated pores. The mean area of the calvarial defect in craniectomy patients was 94.7 ± 17.4 cm2. We achieved a cranial index of symmetry (CIS -%) of 94.5 ± 3.9, while the average post-operative Glasgow outcome scale (GOS) score recorded was 4.2 ± 0.9. Conclusions: 3D printing based patient-specific design and fabrication of acrylic cranioplasty implant is safe and achieves acceptable cosmetic and clinical outcomes in patients with decompressive craniectomy. Our study ensured clinically acceptable structural and mechanical properties of implanted PMMA, suggesting that a low cost 3D printer based PMMA flap is an affordable option for cranioplasty in resource constrained settings. © 2021 Elsevier Ltd
Fatty acid, fatty alcohol and acrylate derivatives as friction depressive additives for ultra-low sulphur diesel
(Elsevier Ltd, 2023) Sruthi, H.; Udayakumar, D.; Hegde, P.; Manjunatha, M.G.
Herein we report the synthesis of some fatty acid, fatty alcohol and acrylate derivatives as friction depressive additives for ultra-low sulphur diesel (ULSD). The high frequency reciprocating rig (HFRR) was employed to measure the wear scar diameter (WSD) of the samples. The lubricating property of the newly synthesized samples [2a, (4a-c) and (5a-c)] was studied by dosing them to ULSD at 200 ppm (wt/vol) concentrations. Amongst them, ester derived from OLA/Polyol (4c) showed the best lubrication enhancing property (WSD 328 µm) at 200 ppm(wt/vol) dosage level. Interestingly, it maintains lubricity characteristics even at a lower blending concentration of 100 ppm with a WSD value (446 µm) lower than the than the accepted value (460 µm). Notably, additives containing polar functional groups and long non-polar carbon backbone exhibited significant lubricity properties with low WSD values. Moreover, it possesses long term antiwear stability when blended with the diesel fuel and do not alter or negatively influence the physical and chemical parameters of the diesel. The FESEM and EDS analysis revealed the formation of thin defensive layer of the additive between the moving metal surfaces supporting the friction reducing properties of the additives. © 2023
Kannada Dialect Classification using Artificial Neural Networks
(Institute of Electrical and Electronics Engineers Inc., 2020) Mothukuri, S.K.P.; Hegde, P.; Chittaragi, N.B.; Koolagudi, S.G.
In this paper, Automatic Dialect Classification (ADC) system is proposed for dialects of Kannada language (the Dravidian language spoken in Southern Karnataka). ADC system is proposed by extracting spectral Mel Frequency Cepstral Coefficients (MFCCs), and log filter bank features along with Linear predictive coefficients. In addition, prosodic pitch and energy features are extracted to capture dialect specific cues. A Kannada dialect speech corpus consisting of five prominent dialects of Kannada language is used for designing the ADC system. An attempt is made by using Artificial Neural Networks (ANNs) technique for classification of Kannada dialects. As, recently, ANNs and its variants are gaining more popularity in the area of speech processing application. Hyperparameter tuning of ANN has resulted with an increase in performance. Â© 2020 IEEE.
Kannada Dialect Classification UsingÂ CNN
(Springer Science and Business Media Deutschland GmbH, 2020) Hegde, P.; Chittaragi, N.B.; Mothukuri, S.K.P.; Koolagudi, S.G.
Kannada is one of the prominent languages spoken in southern India. Since the Kannada is a lingua franca and spoken by more than 70 million people, it is evident to have dialects. In this paper, we identified five major dialectal regions in Karnataka state. An attempt is made to classify these five dialects from sentence-level utterances. Sentences are segmented from continuous speech automatically by using spectral centroid and short term energy features. Mel frequency cepstral coefficient (MFCC) features are extracted from these sentence units. These features are used to train the convolutional neural networks (CNN). Along with MFCCs, shifted delta and double delta coefficients are also attempted to train the CNN model. The proposed CNN based dialect recognition system is also tested with internationally known standard Intonation Variation in English (IViE) dataset. The CNN model has resulted in better performance. It is observed that the use of one convolution layer and three fully connected layers balances computational complexity and results in better accuracy with both Kannada and English datasets. Â© 2020, Springer Nature Switzerland AG.
Nitk Kids' speech corpus
(2019) Ramteke, P.B.; Supanekar, S.; Hegde, P.; Nelson, H.; Aithal, V.; Koolagudi, S.G.
This paper introduces speech database for analyzing children's speech. The proposed database of children is recorded in Kannada language (one of the South Indian languages) from children between age 2 12 to 6 12 years. The database is named as National Institute of Technology Karnataka Kids' Speech Corpus (NITK Kids' Speech Corpus). The relevant design considerations for the database collection are discussed in detail. It is divided into four age groups with an interval of 1 year between each age group. The speech corpus includes nearly 10 hours of speech recordings from 160 children. For each age range, the data is recorded from 40 children (20 male and 20 female). Further, the effect of developmental changes on the speech from 2 12 to 6 12 years are analyzed using pitch and formant analysis. Some of the potential applications, of the NITK Kids' Speech Corpus, such as, systematic study on the language learning ability of children, phonological process analysis and children speech recognition are discussed. Copyright � 2019 ISCA
Nitk Kids' speech corpus
(International Speech Communication Association publication@isca-speech.org 4 Rue des Fauvettes - Lous Tourils Baixas 66390, 2019) Ramteke, P.B.; Supanekar, S.; Hegde, P.; Nelson, H.; Aithal, V.; Koolagudi, S.G.
This paper introduces speech database for analyzing children's speech. The proposed database of children is recorded in Kannada language (one of the South Indian languages) from children between age 2 12 to 6 12 years. The database is named as National Institute of Technology Karnataka Kids' Speech Corpus (NITK Kids' Speech Corpus). The relevant design considerations for the database collection are discussed in detail. It is divided into four age groups with an interval of 1 year between each age group. The speech corpus includes nearly 10 hours of speech recordings from 160 children. For each age range, the data is recorded from 40 children (20 male and 20 female). Further, the effect of developmental changes on the speech from 2 12 to 6 12 years are analyzed using pitch and formant analysis. Some of the potential applications, of the NITK Kids' Speech Corpus, such as, systematic study on the language learning ability of children, phonological process analysis and children speech recognition are discussed. Â© Â© 2019 ISCA
Perspective analysis of assistive robots for elderly in India
(Taylor and Francis Ltd., 2024) Hegde, P.; Gadag, A.; Sontakke, S.; Kumar, M.; Kholia, A.; Patel, J.; Khan, A.; Jahnavi, E.; Nabala, R.; Thotappa, D.
Purpose: Assistive technology for elderly are advancing, and this study aimed to analyse the Indian perspective on utilising assistive robot technology for aiding elderly individuals. Materials and Methods: A population-based survey was undertaken to collect data from three perspectives: Relatives of the elderly, Healthcare professionals and Elderly individuals. The survey gathered 389 responses. The responses are statistically analysed, and data is visualised with different plots for better understanding. Results: It is observed that the older people rate with less conviction on the use of technology when compared to the relatives and healthcare professionals. Out of the three target groups, the elderly individuals had the most correlating attributes to purchasing the robot. Also, healthcare personnel, relatives, and older people gave 82%, 63% and 55% affirmatives to the question on purchasing the robot, respectively. And the cost of the robot is preferred to be under 6 lakh rupees. Conclusions: Though the younger generation has more orientation towards technology, older people are skeptical about handling computer gadgets or robots. However, there are significant expectations and concerns expressed by three target groups such as conversational, navigational, reminder features, security and malfunction concerns. © 2024 Informa UK Limited, trading as Taylor & Francis Group.
Robust Dialect Identification System using Spectro-Temporal Gabor Features
(2019) Chittaragi, N.B.; Krishna, Mothukuri, S.P.; Hegde, P.; Koolagudi, S.G.
Automatic identification of dialects of a language is gaining popularity in the field of automatic speech recognition (ASR) systems. The present work proposes an automatic dialect identification (ADI) system using 2D Gabor and spectral features. A comprehensive study of the five dialects of a Dravidian Kannada language has been taken up. Gabor filters representing spectro-temporal modulations attempt in emulation of the human auditory system concerning signal processing strategies. Hence, they are able to well perceive human voices in tern recognize dialectal variations effectively. Also, spectral features Mel frequency cepstral coefficients (MFCC) are derived. A single classifier based support vector machine (SVM) and ensemble based extreme random forest (ERF) classification methods are employed for recognition. The effectiveness of the Gabor features for ADI system is demonstrated with proposed Kannada dialect dataset along with a standard intonation variation in English (IViE) dataset for British English dialects. The Gabor features have shown better performance over MFCC features with both datasets. Better recognition performance of 88.75% and 99.16% is achieved with Kannada and IViE dialect datasets respectively. Proposed Gabor features have demonstrated better performances even under noisy conditions. � 2018 IEEE.
Robust Dialect Identification System using Spectro-Temporal Gabor Features
(Institute of Electrical and Electronics Engineers Inc., 2018) Chittaragi, N.B.; Mothukuri, S.P.; Hegde, P.; Koolagudi, S.G.
Automatic identification of dialects of a language is gaining popularity in the field of automatic speech recognition (ASR) systems. The present work proposes an automatic dialect identification (ADI) system using 2D Gabor and spectral features. A comprehensive study of the five dialects of a Dravidian Kannada language has been taken up. Gabor filters representing spectro-temporal modulations attempt in emulation of the human auditory system concerning signal processing strategies. Hence, they are able to well perceive human voices in tern recognize dialectal variations effectively. Also, spectral features Mel frequency cepstral coefficients (MFCC) are derived. A single classifier based support vector machine (SVM) and ensemble based extreme random forest (ERF) classification methods are employed for recognition. The effectiveness of the Gabor features for ADI system is demonstrated with proposed Kannada dialect dataset along with a standard intonation variation in English (IViE) dataset for British English dialects. The Gabor features have shown better performance over MFCC features with both datasets. Better recognition performance of 88.75% and 99.16% is achieved with Kannada and IViE dialect datasets respectively. Proposed Gabor features have demonstrated better performances even under noisy conditions. Â© 2018 IEEE.
Spectral Feature Based Kannada Dialect Classification from Stop Consonants
(2019) Chittaragi, N.B.; Hegde, P.; Mothukuri, S.K.P.; Koolagudi, S.G.
This study focuses on the investigation of the significance of stop consonants in view of the classification of Kannada dialects. Majority of the studies proposed have shown the existence of evidential differences in the pronunciation of vowels across dialects. However, consonant based studies on dialect processing are found to be comparatively lesser. In this work, eight stop consonants are used for characterization of five Kannada dialects. Acoustic characteristics such as cepstral coefficients, formant frequencies, spectral flux, and rolloff features are explored from spectral analysis of stops. The consonant dataset is derived from standard Kannada dialect dataset consisting of 2417 consonants obtained from 16 native speakers from each dialect. Support vector machine (SVM) and decision tree-based extreme gradient boosting (XGB) ensemble classification methods are employed for automatic recognition of Kannada dialects. The research findings show that the stops existing for shorter duration also convey dialectal linguistic cues. Combination of spectral properties has contributed to the identification of distinct dialect-specific information across Kannada dialects. � 2019, Springer Nature Switzerland AG.
Spectral Feature Based Kannada Dialect Classification from Stop Consonants
(Springer, 2019) Chittaragi, N.B.; Hegde, P.; Mothukuri, S.K.P.; Koolagudi, G.K.
This study focuses on the investigation of the significance of stop consonants in view of the classification of Kannada dialects. Majority of the studies proposed have shown the existence of evidential differences in the pronunciation of vowels across dialects. However, consonant based studies on dialect processing are found to be comparatively lesser. In this work, eight stop consonants are used for characterization of five Kannada dialects. Acoustic characteristics such as cepstral coefficients, formant frequencies, spectral flux, and rolloff features are explored from spectral analysis of stops. The consonant dataset is derived from standard Kannada dialect dataset consisting of 2417 consonants obtained from 16 native speakers from each dialect. Support vector machine (SVM) and decision tree-based extreme gradient boosting (XGB) ensemble classification methods are employed for automatic recognition of Kannada dialects. The research findings show that the stops existing for shorter duration also convey dialectal linguistic cues. Combination of spectral properties has contributed to the identification of distinct dialect-specific information across Kannada dialects. Â© 2019, Springer Nature Switzerland AG.
Study on the strength parameters of high volume fly ash concrete and geopolymer concrete
(2011) Shetty, A.; Anand, V.R.; Hegde, P.
Concrete has been the most preferred construction material. It is being increasingly used day by day all over the world due to its versatility, mould ability and high compressive strength. But the large-scale production of cement is causing environmental problems on one hand and the unrestricted depletion of natural resources on the other. So the issue of sustainable development in concrete construction is addressed in this paper through development of concrete mixes by replacing certain percentage of cement with fly ash. Trials on concrete mixes with replacement of 40%, 50%, 60% and 70% of cement with fly ash are carried out and the results depict that at the replacement level of 40% cement by fly ash, the required strength is achieved. It is also observed that the rate of early strength gain is retarded as the percentage replacement of cement increases. But in case of Geopolymer concrete (100% replacement of cement by fly ash) under a curing temperature around 600C and above, the strength gain rate is very high in initial stages. It is observed that design strength is achieved within 28 hours of oven curing. 2011 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.
Study on the strength parameters of high volume fly ash concrete and geopolymer concrete
(2011) Shetty, A.; Anand, V.R.; Hegde, P.
Concrete has been the most preferred construction material. It is being increasingly used day by day all over the world due to its versatility, mould ability and high compressive strength. But the large-scale production of cement is causing environmental problems on one hand and the unrestricted depletion of natural resources on the other. So the issue of sustainable development in concrete construction is addressed in this paper through development of concrete mixes by replacing certain percentage of cement with fly ash. Trials on concrete mixes with replacement of 40%, 50%, 60% and 70% of cement with fly ash are carried out and the results depict that at the replacement level of 40% cement by fly ash, the required strength is achieved. It is also observed that the rate of early strength gain is retarded as the percentage replacement of cement increases. But in case of Geopolymer concrete (100% replacement of cement by fly ash) under a curing temperature around 600C and above, the strength gain rate is very high in initial stages. It is observed that design strength is achieved within 28 hours of oven curing. © 2011 CAFET-INNOVA TECHNICAL SOCIETY. All rights reserved.
Synthesis, Characterization, Thermal and Antimicrobial studies of N-substituted Sulfanilamide derivatives
(2014) Lahtinen, M.; Kudva, J.; Hegde, P.; Bhat, K.; Kolehmainen, E.; Nonappa; Venkatesh; Naral, D.
Four sulfanilamide derivatives N-[4-(phenylsulfamoyl)phenyl]acetamide (1), 4-amino-N-phenylbenzenesulfonamide (2), N-[4-(phenylsulfamoyl)phenyl]benzamide (3) and N-{4-[(3-chlorophenyl)sulfamoyl]phenylbenzamide (4) were synthesized and characterized by Infra-Red (IR), Nuclear Magnetic Resonance (NMR) and UV-visible (UV-Vis) spectra. Also Liquid Chromatographic (LCMS) and High Resolution Mass Spectrometric (HRMS) methods were used. Crystal structures of 1-4 were determined by single crystal X-ray diffraction (XRD) and their conformational and hydrogen bond (HB) network properties were examined with survey of the literature data. Compounds 1 and 2 crystallize in the same orthorhombic Pbca symmetry with equivalent molecular conformation (tilted V-shape) but showed distinct packing and hydrogen bonding models. Compounds 3 and 4 crystallize in monoclinic and triclinic crystal systems, albeit exhibiting identical molecular conformation (L-shaped). Same donor acceptor pairs both on 3 and 4 result to different kind of HB network. Thermogravimetric (TG) and differential scanning calorimetric (DSC) methods were used to evaluate thermal properties of the substances. All sulfanilamide derivatives have melting points between195-227 C, initiation of thermal decomposition between 259-271 C and enthalpies of fusion ?HfusT = 38.96, 36.60, 46.23 and 44.81 kJ mol -1 were determined for 1-4, respectively. The derivatives were screened for their antibacterial and antifungal activities against various bacterial and fungal strains. It is observed that there is no significant antibacterial activity with the introduction of the benzene ring to CO-NH group or SO2-NH moiety, and none of the compounds exhibited antifungal activity. 2013 Elsevier B.V. All rights reserved.
Synthesis, Characterization, Thermal and Antimicrobial studies of N-substituted Sulfanilamide derivatives
(2014) Lahtinen, M.; Kudva, J.; Hegde, P.; Bhat, K.; Kolehmainen, E.; Nonappa, N.; Venkatesh; Naral, D.
Four sulfanilamide derivatives N-[4-(phenylsulfamoyl)phenyl]acetamide (1), 4-amino-N-phenylbenzenesulfonamide (2), N-[4-(phenylsulfamoyl)phenyl]benzamide (3) and N-{4-[(3-chlorophenyl)sulfamoyl]phenylbenzamide (4) were synthesized and characterized by Infra-Red (IR), Nuclear Magnetic Resonance (NMR) and UV-visible (UV-Vis) spectra. Also Liquid Chromatographic (LCMS) and High Resolution Mass Spectrometric (HRMS) methods were used. Crystal structures of 1-4 were determined by single crystal X-ray diffraction (XRD) and their conformational and hydrogen bond (HB) network properties were examined with survey of the literature data. Compounds 1 and 2 crystallize in the same orthorhombic Pbca symmetry with equivalent molecular conformation (tilted V-shape) but showed distinct packing and hydrogen bonding models. Compounds 3 and 4 crystallize in monoclinic and triclinic crystal systems, albeit exhibiting identical molecular conformation (L-shaped). Same donor acceptor pairs both on 3 and 4 result to different kind of HB network. Thermogravimetric (TG) and differential scanning calorimetric (DSC) methods were used to evaluate thermal properties of the substances. All sulfanilamide derivatives have melting points between195-227 C, initiation of thermal decomposition between 259-271 C and enthalpies of fusion ?HfusT = 38.96, 36.60, 46.23 and 44.81 kJ mol -1 were determined for 1-4, respectively. The derivatives were screened for their antibacterial and antifungal activities against various bacterial and fungal strains. It is observed that there is no significant antibacterial activity with the introduction of the benzene ring to CO-NH group or SO2-NH moiety, and none of the compounds exhibited antifungal activity. © 2013 Elsevier B.V. All rights reserved.
The Second DISPLACE Challenge: DIarization of SPeaker and LAnguage in Conversational Environments
(International Speech Communication Association, 2024) Kalluri, S.B.; Singh, P.; Roy Chowdhuri, P.; Kulkarni, A.; Baghel, S.; Hegde, P.; Sontakke, S.; Deepak, K.T.; Mahadeva Prasanna, S.R.; Vijayasenan, D.; Ganapathy, S.
The DIarization of SPeaker and LAnguage in Conversational Environments (DISPLACE) 2024 challenge is the second in the series of DISPLACE challenges, which involves tasks of speaker diarization (SD) and language diarization (LD) on a challenging multilingual conversational speech dataset. In the DISPLACE 2024 challenge, we also introduced the task of automatic speech recognition (ASR) on this dataset. The dataset containing 158 hours of speech, consisting of both supervised and unsupervised mono-channel far-field recordings, was released for LD and SD tracks. Further, 12 hours of close-field mono-channel recordings were provided for the ASR track conducted on 5 Indian languages. The details of the dataset, baseline systems and the leader board results are highlighted in this paper. We have also compared our baseline models and the team's performances on evaluation data of DISPLACE-2023 to emphasize the advancements made in this second version of the challenge. Â© 2024 International Speech Communication Association. All rights reserved.