default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDChellu Chandra Sekhar
C. Chandra Sekhar
Person information
- affiliation: Indian Institute of Technology Madras, Department of Computer Science and Engineering, Chennai, India
SPARQL queries 
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2023
[j13]Hemalatha Munusamy
, C. Chandra Sekhar:
Multimodal attention-based transformer for video captioning. Appl. Intell. 53(20): 23349-23368 (2023)- [c64]Naveen Vakada, C. Chandra Sekhar:
Descriptive and Coherent Paragraph Generation for Image Paragraph Captioning Using Vision Transformer and Post-processing. ACIVS 2023: 40-52 - [c63]Hemalatha Munusamy, C. Chandra Sekhar:
Multi-Modal Hierarchical Attention-Based Dense Video Captioning. ICIP 2023: 475-479 - [i8]Vishwajit Kumar Vishnu, C. Chandra Sekhar:
Memory-efficient Stochastic methods for Memory-based Transformers. CoRR abs/2311.08123 (2023) - 2022
- [j12]Hemalatha Munusamy, C. Chandra Sekhar:
Video captioning using Semantically Contextual Generative Adversarial Network. Comput. Vis. Image Underst. 221: 103453 (2022) - 2021
- [j11]Nauman Dawalatabad, Srikanth R. Madikeri, C. Chandra Sekhar, Hema A. Murthy:
Novel Architectures for Unsupervised Information Bottleneck Based Speaker Diarization of Meetings. IEEE ACM Trans. Audio Speech Lang. Process. 29: 14-27 (2021) - [c62]Ujjal Kr Dutta, Mehrtash Harandi, Chellu Chandra Sekhar:
Semi-Supervised Metric Learning: A Deep Resurrection. AAAI 2021: 7279-7287 - [i7]Nauman Dawalatabad, Jilt Sebastian, Jom Kuriakose, C. Chandra Sekhar, Shrikanth Narayanan, Hema A. Murthy:
Front-end Diarization for Percussion Separation in Taniavartanam of Carnatic Music Concerts. CoRR abs/2103.03215 (2021) - [i6]Ujjal Kr Dutta, Mehrtash Harandi, Chellu Chandra Sekhar:
Semi-Supervised Metric Learning: A Deep Resurrection. CoRR abs/2105.05061 (2021) - [i5]Rupam Ojha, C. Chandra Sekhar:
Unsupervised Domain Adaptation in Speech Recognition using Phonetic Features. CoRR abs/2108.02850 (2021) - 2020
- [j10]Ujjal Kr Dutta, Mehrtash Harandi, Chellu Chandra Sekhar:
Unsupervised Deep Metric Learning via Orthogonality Based Probabilistic Loss. IEEE Trans. Artif. Intell. 1(1): 74-84 (2020) - [c61]Ujjal Kr Dutta, Mehrtash Harandi, C. Chandra Sekhar:
Unsupervised Metric Learning with Synthetic Examples. AAAI 2020: 3834-3841 - [c60]Ujjal Kr Dutta, C. Chandra Sekhar:
A Geometric Approach for Unsupervised Similarity Learning. ICASSP 2020: 4202-4206 - [c59]Hemalatha Munusamy, C. Chandra Sekhar:
Domain-Specific Semantics Guided Approach to Video Captioning. WACV 2020: 1576-1585 - [i4]Ujjal Kr Dutta, Mehrtash Harandi, Chellu Chandra Sekhar:
Affinity guided Geometric Semi-Supervised Metric Learning. CoRR abs/2002.12394 (2020) - [i3]Ujjal Kr Dutta, Mehrtash Harandi, Chellu Chandra Sekhar:
Unsupervised Deep Metric Learning via Orthogonality based Probabilistic Loss. CoRR abs/2008.09880 (2020)
2010 – 2019
- 2019
- [c58]Nauman Dawalatabad, Srikanth R. Madikeri, C. Chandra Sekhar, Hema A. Murthy:
Incremental Transfer Learning in Two-pass Information Bottleneck Based Speaker Diarization System for Meetings. ICASSP 2019: 6291-6295 - [c57]Rupam Ojha, C. Chandra Sekhar:
Multi-label Classification Models for Detection of Phonetic Features in building Acoustic Models. IJCNN 2019: 1-8 - [i2]Nauman Dawalatabad, Srikanth R. Madikeri, C. Chandra Sekhar, Hema A. Murthy:
Incremental Transfer Learning in Two-pass Information Bottleneck based Speaker Diarization System for Meetings. CoRR abs/1902.08051 (2019) - [i1]Ujjal Kr Dutta, Mehrtash Harandi, Chellu Chandra Sekhar:
A Probabilistic approach for Learning Embeddings without Supervision. CoRR abs/1912.08275 (2019) - 2018
- [j9]B. S. Shajee Mohan, C. Chandra Sekhar:
Distance metric learning-based kernel gram matrix learning for pattern analysis tasks in kernel feature space. Pattern Anal. Appl. 21(3): 847-867 (2018) - [c56]Ujjal Kr Dutta, C. Chandra Sekhar:
Affinity Propagation Based Closed-Form Semi-supervised Metric Learning Framework. ICANN (1) 2018: 556-565 - [c55]Ujjal Kr Dutta, C. Chandra Sekhar:
Subspace Segmentation Based Metric Learning. ICIP 2018: 1623-1627 - [c54]Nauman Dawalatabad, Jom Kuriakose, Chellu Chandra Sekhar, Hema A. Murthy:
Information Bottleneck Based Percussion Instrument Diarization System for Taniavartanam Segments of Carnatic Music Concerts. INTERSPEECH 2018: 1215-1219 - 2017
- [c53]B. S. Shajee Mohan, C. Chandra Sekhar:
Distance Metric Learnt Kernel based SVMs for Semi-Supervised Pattern Classification. ICAPR 2017: 1-6 - [c52]Aditya Mehta, C. Chandra Sekhar:
Kernel Entropy Discriminant Analysis for Dimension Reduction. PReMI 2017: 35-42 - [c51]Aritra Ghosh, C. Chandra Sekhar:
Label Correlation Propagation for Semi-supervised Multi-label Learning. PReMI 2017: 52-60 - [c50]Abhiram Kumar Singh, C. Chandra Sekhar:
A Two-Stage Conditional Random Field Model Based Framework for Multi-Label Classification. PReMI 2017: 69-76 - 2016
- [c49]Nauman Dawalatabad, Srikanth R. Madikeri, C. Chandra Sekhar, Hema A. Murthy:
Two-Pass IB Based Speaker Diarization System Using Meeting-Specific ANN Based Features. INTERSPEECH 2016: 2199-2203 - 2015
- [c48]Abhijeet Sachdev, Veena Thenkanidiyoor, Aroor Dinesh Dileep, C. Chandra Sekhar:
Example-Specific Density Based Matching Kernels for Scene Classification Using Support Vector Machines. ICMLA 2015: 122-127 - [c47]Niharjyoti Sarangi, C. Chandra Sekhar:
Automatic Image Annotation Using Convex Deep Learning Models. ICPRAM (2) 2015: 92-99 - [c46]Niharjyoti Sarangi, Chellu Chandra Sekhar:
Tensor Deep Stacking Networks and Kernel Deep Convex Networks for Annotating Natural Scene Images. ICPRAM (Selected Papers) 2015: 267-281 - 2014
- [j8]Aroor Dinesh Dileep, C. Chandra Sekhar:
Class-specific GMM based intermediate matching kernel for classification of varying length patterns of long duration speech using support vector machines. Speech Commun. 57: 126-143 (2014) - [j7]Aroor Dinesh Dileep, Chellu Chandra Sekhar:
GMM-Based Intermediate Matching Kernel for Classification of Varying Length Patterns of Long Duration Speech Using Support Vector Machines. IEEE Trans. Neural Networks Learn. Syst. 25(8): 1421-1432 (2014) - [c45]B. Balasanjeevi, C. Chandra Sekhar:
A Descriptor based on Intensity Binning for Image Matching. ICPRAM 2014: 96-103 - [c44]Shrikant Nayak, C. Chandra Sekhar:
Techniques for Improving the Performance of Image Retrieval using Relevance Feedback. ICVGIP 2014: 43:1-43:8 - [c43]Ramya K. Raman, C. Chandra Sekhar:
Concept Level Discriminant Analysis Techniques for Dimension Reduction in Image Classification Tasks. ICVGIP 2014: 44:1-44:8 - [e1]Manish Parashar, Umesh Bellur, S. D. Madhu Kumar, Priya Chandran, Murali Krishnan, Kamesh Madduri, Sushil K. Prasad, C. Chandra Sekhar, Nanjangud C. Narendra, Carlos Valera, Sanjay Chaudhary, Kavi Arya, Xiaolin Li:
Seventh International Conference on Contemporary Computing, IC3 2014, Noida, India, August 7-9, 2014. IEEE Computer Society 2014, ISBN 978-1-4799-5172-7 [contents] - 2013
- [j6]Venkataramana B. Kini, C. Chandra Sekhar:
Large margin mixture of AR models for time series classification. Appl. Soft Comput. 13(1): 361-371 (2013) - [j5]Venkataramana B. Kini, C. Chandra Sekhar:
Bayesian mixture of AR models for time series clustering. Pattern Anal. Appl. 16(2): 179-200 (2013) - [j4]Aroor Dinesh Dileep, C. Chandra Sekhar:
HMM Based Intermediate Matching Kernel for Classification of Sequential Patterns of Speech Using Support Vector Machines. IEEE ACM Trans. Audio Speech Lang. Process. 21(12): 2570-2582 (2013) - [c42]Aroor Dinesh Dileep, C. Chandra Sekhar:
HMM based pyramid match kernel for classification of sequential patterns of speech using support vector machines. ICASSP 2013: 3562-3566 - 2012
- [j3]Aroor Dinesh Dileep, C. Chandra Sekhar:
Speaker recognition using pyramid match kernel based support vector machines. Int. J. Speech Technol. 15(3): 365-379 (2012) - [c41]B. S. Shajee Mohan, C. Chandra Sekhar:
Class-Specific Mahalanobis Distance Metric Learning for Biological Image Classification. ICIAR (2) 2012: 240-248 - 2010
- [j2]S. Chandrakala, C. Chandra Sekhar:
Classification of varying length multivariate time series using Gaussian mixture models and support vector machines. Int. J. Data Min. Model. Manag. 2(3): 268-287 (2010) - [c40]T. Veena, Aroor Dinesh Dileep, Chellu Chandra Sekhar:
Scene Categorization Using Large Margin Gaussian Mixture Models. IPCV 2010: 395-401
2000 – 2009
- 2009
- [c39]Venkataramana B. Kini, C. Chandra Sekhar:
Bayesian Mixture of AR Models for Time Series Clustering. ICAPR 2009: 35-38 - [c38]S. Chandrakala, C. Chandra Sekhar:
Model Based Clustering of Audio Clips Using Gaussian Mixture Models. ICAPR 2009: 47-50 - [c37]Sheetal Reddy Pamudurthy, C. Chandra Sekhar:
Ordering and Elimination Based Component Learning Method. ICAPR 2009: 99-102 - [c36]Anupam Mandal, K. R. Prasanna Kumar, G. Athithan, C. Chandra Sekhar:
A Graphical Model Based Decoder for Recognition of Loss-concealed VoIP Speech. ICAPR 2009: 179-182 - [c35]Harendra Kumar Mishra, C. Chandra Sekhar:
Variational Gaussian Mixture Models for Speech Emotion Recognition. ICAPR 2009: 183-186 - [c34]Shanmuganathan Chandrakala, Chellu Chandra Sekhar:
Combination of generative models and SVM based classifier for speech emotion recognition. IJCNN 2009: 497-502 - [c33]Aroor Dinesh Dileep, Chellu Chandra Sekhar:
Representation and feature selection using multiple kernel learning. IJCNN 2009: 717-722 - [c32]S. Chandrakala, C. Chandra Sekhar:
Classification of Multi-variate Varying Length Time Series Using Descriptive Statistical Features. PReMI 2009: 13-18 - [c31]Ved Prakash Sahu, Harendra Kumar Mishra, C. Chandra Sekhar:
Variational Bayes Adapted GMM Based Models for Audio Clip Classification. PReMI 2009: 513-518 - 2008
- [c30]Sadhana Chevireddy, Hema A. Murthy, C. Chandra Sekhar:
Signal processing based segmentation and hmm based acoustic clustering of syllable segments for low bit rate segment vocoder at 1.4 Kbps. EUSIPCO 2008: 1-5 - [c29]A. Vijaya Rama Raju, C. Chandra Sekhar:
An SVM Based Approach to Cross-Language Adaptation for Indian Languages. ICONIP (2) 2008: 394-401 - [c28]Venkataramana B. Kini, C. Chandra Sekhar:
Large margin AR model for time series classification. ICPR 2008: 1-4 - [c27]S. Chandrakala, C. Chandra Sekhar:
A density based method for multivariate time series clustering in kernel feature space. IJCNN 2008: 1885-1890 - [c26]G. Haranadh, C. Chandra Sekhar:
Hyperparameters of Gaussian process as features for trajectory classification. IJCNN 2008: 2195-2199 - 2007
- [c25]H. Swethalakshmi, C. Chandra Sekhar, V. Srinivasa Chakravarthy:
Spatiostructural Features for Recognition of Online Handwritten Characters in Devanagari and Tamil Scripts. ICANN (2) 2007: 230-239 - [c24]Lakshmi Narayana Panuku, C. Chandra Sekhar:
Clustering of Nonlinearly Separable Data Using Spiking Neural Networks. ICANN (1) 2007: 390-399 - [c23]Venkataramana B. Kini, C. Chandra Sekhar:
Kernel Auto-Regressive Model with eXogenous Inputs for Nonlinear Time Series Prediction. ICCTA 2007: 355-360 - [c22]Venkataramana B. Kini, C. Chandra Sekhar:
Multi-Scale Kernel Latent Variable Models for Nonlinear Time Series Pattern Matching. ICONIP (2) 2007: 11-20 - [c21]Lakshmi Narayana Panuku, C. Chandra Sekhar:
Region-Based Encoding Method Using Multi-dimensional Gaussians for Networks of Spiking Neurons. ICONIP (1) 2007: 73-82 - [c20]Sheetal Reddy Pamudurthy, S. Chandrakala, C. Chandra Sekhar:
Local Density Estimation based Clustering. IJCNN 2007: 1249-1254 - [c19]R. Anitha, C. Chandra Sekhar:
Acoustic Modeling using Vector Quantization in Kernel Feature Space and Classification using String Kernel based Support Vector Machines. IJCNN 2007: 1512-1517 - [c18]R. Anitha, C. Chandra Sekhar:
Acoustic Modeling Using Continuous Density Hidden Markov Models in the Mercer Kernel Feature Space. ISNN (1) 2007: 546-552 - 2006
- [c17]D. Srikrishna Satish, C. Chandra Sekhar:
Kernel based Clustering and Vector Quantization for Speech Segmentation. IJCNN 2006: 1636-1641 - [c16]Aroor Dinesh Dileep, Chellu Chandra Sekhar:
Identification of Block Ciphers using Support Vector Machines. IJCNN 2006: 2696-2701 - 2005
- [c15]Suryakanth V. Gangashetty, C. Chandra Sekhar, B. Yegnanarayana:
Spotting Multilingual Consonant-Vowel Units of Speech Using Neural Network Models. NOLISP 2005: 303-317 - 2004
- [c14]R. Suguna, N. Sudha, C. Chandra Sekhar:
A Fast and Efficient Face Detection Technique Using Support Vector Machine. ICONIP 2004: 338-343 - [c13]D. Srikrishna Satish, C. Chandra Sekhar:
Kernel Based Clustering for Multiclass Data. ICONIP 2004: 1266-1272 - [c12]Suryakanth V. Gangashetty, Chellu Chandra Sekhar, B. Yegnanarayana:
Detection of vowel on set points in continuous speech using autoassociative neural network models. INTERSPEECH 2004: 1081-1084 - 2003
- [c11]Suryakanth V. Gangashetty, C. Chandra Sekhar, B. Yegnanarayana:
Constraint satisfaction model for enhancement of evidence in recognition of consonant-vowel utterances. ICASSP (2) 2003: 753-756 - [c10]Suryakanth V. Gangashetty, C. Chandra Sekhar, B. Yegnanarayana:
Constraint satisfaction model for enhancement of evidence in recognition of consonant-vowel utterances. ICME 2003: 201-204 - 2002
- [j1]C. Chandra Sekhar, B. Yegnanarayana:
A constraint satisfaction model for recognition of stop consonant-vowel (SCV) utterances. IEEE Trans. Speech Audio Process. 10(7): 472-480 (2002) - [c9]Weifeng Lee, C. Chandra Sekhar, Kazuya Takeda, Fumitada Itakura:
Recognition of continuous speech segments of monophone units using support vector machines. INTERSPEECH 2002: 2653-2656 - [c8]Chellu Chandra Sekhar, Kazuya Takeda, Fumitada Itakura:
Recognition of Consonant-Vowel (CV) Units of Speech in a Broadcast News Corpus Using Support Vector Machines. SVM 2002: 171-185 - 2001
- [c7]Chellu Chandra Sekhar, Kazuya Takeda, Fumitada Itakura:
Recognition of consonant-vowel utterances using Support Vector Machines. ESANN 2001: 7-12 - [c6]Chellu Chandra Sekhar, Kazuya Takeda, Fumitada Itakura:
Close-Class-Set Discrimination Method for Recognition of Stop_Consonant-Vowel Utterances Using Support Vector Machines. ICANN 2001: 399-404
1990 – 1999
- 1998
- [c5]Ansgar Drolshagen, Walter Anheier, C. Chandra Sekhar:
A Residue Number Arithmetic based Circuit for Pipelined Computation of Autocorrelation Coefficients of Speech Signal. VLSI Design 1998: 122-127 - 1996
- [c4]C. Chandra Sekhar, B. Yegnanarayana:
Neural network models for spotting stop consonant-vowel (SCV) segments in continuous speech. ICNN 1996: 2003-2008 - 1991
- [c3]A. S. Madhukumar, S. Rajendran, C. Chandra Sekhar, B. Yegnanarayana:
Synthesizing intonation for speech in hindi. EUROSPEECH 1991: 1153-1156
1980 – 1989
- 1989
- [c2]M. Prakash, G. V. Ramana Rao, C. Chandra Sekhar, B. Yegnanarayana:
Parsing spoken utterances in an inflectional language. EUROSPEECH 1989: 1546-1549 - 1987
- [c1]P. Eswar, S. K. Gupta, C. Chandra Sekhar, B. Yegnanarayana, K. Nagamma Reddy:
An acoustic-phonetic expert for analysis and processing of continuous speech in hindi. ECST 1987: 1369-1372
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-08-02 19:18 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
