Anantha Krishna Chintanpalli

@vit.ac.in

Professor Grade I, School of Electronics
Vellore Institute of Technology

Anantha Krishna Chintanpalli


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https://researchid.co/anantha.krishna

RESEARCH INTERESTS

Biomedical Signal Processing, Deep learning for speech recognition systems, Artificial Neural Networks, Deep Neural Networks, Speech Processing, and Digital Signal Processing.
27

Scopus Publications

284

Scholar Citations

8

Scholar h-index

7

Scholar i10-index

Scopus Publications

  • Predicting the age effects on concurrent vowel scores using a temporal jitter computational model
    Harshavardhan Settibhaktini, Rithik Rathi, Ananthakrishna Chintanpalli
    Applied Acoustics, 2026
  • Recent advancements in feature extraction and classification based bone cancer detection - a systematic review
    Kanimozhi S, Sivakumar Rajagopal, Ananthakrishna Chintanpalli
    Biomedical Physics and Engineering Express, 2025
    Cancer is a deadly disease that occurs due to the overgrowth of abnormal cells. Bone cancer is the third most occurring disease; approximately 10,000 patients suffer from bone cancer in India annually. It can lead to death if not diagnosed in the earlier stage. The bone cancer occurs in four stages as follows: In stage 1, cancer does not spread to other bone parts; in stage 2, cancer looks similar to stage 1, but it becomes dangerous; in stage 3, cancer spreads to one or two bone parts; and in stage 4, cancer spreads to other body parts. Timely diagnosis of bone cancer is challenging due to the unspecific indications that are similar to common musculoskeletal injuries, late visits of patients to the hospital, and low intuition by the physician. The texture of diseased bone differs from healthy bone. Mostly in the dataset, the healthy and cancerous bone images have similar characteristics. Therefore, development of automated systems is necessary to classify the normal and abnormal scan images. The objective of this paper is to identify the studies on classification techniques in detecting bone cancer with five criteria: feature extraction methods, machine learning (ML) and deep learning (DL) techniques, advantages, disadvantages, and classifier accuracy. The current study performed the systematic literature review of 129 studies selected based on the use of different feature extractions to extract the textural characteristics of the images that are fed into the ML and DL algorithms to classify the normal and subtypes of bone cancer images for better analysis. The review concludes that the convolutional neural network classifier, along with different textural feature extraction techniques like gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP), detected the bone cancer with high accuracy compared to DL classification without feature extraction techniques in diagnosing the bone cancer. In this respect, this paper proposes the systematic review of types of bone cancer and recent advancements in feature extraction methods and classification involving deep learning and machine learning models to detect bone cancer with a higher accuracy rate.
  • A systematic review on feature extraction methods and deep learning models for detection of cancerous lung nodules at an early stage -the recent trends and challenges
    Mathumetha Palani, Sivakumar Rajagopal, Anantha Krishna Chintanpalli
    Biomedical Physics and Engineering Express, 2025
    Lung cancer is one of the most common life-threatening worldwide cancers affecting both the male and the female populations. The appearance of nodules in the scan image is an early indication of the development of cancer cells in the lung. The Low Dose Computed Tomography screening technique is used for the early detection of cancer nodules. Therefore, with more Computed Tomography (CT) lung profiles, an automated lung nodule analysis system can be utilized through image processing techniques and neural network algorithms. A CT image of the lung consists of many elements such as blood vessels, ribs, nodules, sternum, bronchi and nodules. These nodules can be both benign and malignant, where the latter leads to lung cancer. Detecting them at an earlier stage can increase life expectancy by up to 5 to 10 years. To analyse only the nodules from the profile, the respected features are extracted using image processing techniques. Based on the review, textural features were the promising ones in medical image analysis and for solving computer vision problems. The importance of uncovering the hidden features allows Deep Learning algorithms (DL) to function better, especially in medical imaging, where accuracy has improved. The earlier detection of cancerous lung nodules is possible through the combination of multi-featured extraction and classification techniques using image data. This technique can be a breakthrough in the deep learning area by providing the appropriate features. One of the greatest challenges is the incorrect identification of malignant nodules results in a higher false positive rate during the prediction. The suitable features make the system more precise in prognosis. In this paper, the overview of lung cancer along with the publicly available datasets is discussed for the research purposes. They are mainly focused on the recent research that combines feature extraction and deep learning algorithms used to reduce the false positive rate in the automated detection of lung nodules. The primary objective of the paper is to provide the importance of textural features when combined with different deep-learning models. It gives insights into their advantages, disadvantages and limitations regarding possible research gaps. These papers compare the recent studies of deep learning models with and without feature extraction and conclude that DL models that include feature extraction are better than the others.
  • Machine Learning Framework of EEG for binary classification of Early on-set Alzheimer's Disease
    Shyam Sundar S, Ananthakrishna Chintanpalli, Rajesh Kumar M
    2025 International Conference on Biomedical Engineering and Sustainable Healthcare Icbmesh 2025 Proceedings, 2025
    Dementia is the one type of brain degenerative disease. It affects the memory, thinking and ability to perform daily task of a person who has dementia. Out of all dementia cases at least two-thirds of all dementia cases in people are above the age of 65. The most common cause of dementia is Alzheimer’s disease(AD) and this tells the importance of AD in the case of Dementia. In this paper Electroencephalogram (EEG) signal Dataset, is used to classify AD from Normal control(NC). Early-onset Alzheimer’s disease (EOAD) which occurs during age less than 65 is given less importance to study than the lateonset form (LOAD). But, EOAD mostly show, disease progression more aggressively than LOAD. So, this paper discusses about the binary classification of AD in EOAD age group whose age is less than 65 and LOAD age group whose is age greater than or equal to 65 and compare the classification accuracy of both. For binary classification, Support Vector Machine(SVM) is used which is a machine learning technique. Maximum alpha frequency band(812 Hz) power between Different Electrode Location of brain is used as a feature input to Support Vector Machine. Along with EEG dataset Mini Mental state Examination (MMSE) score of patients is given. Based on MMSE score, the label is generated and given as an input to SVM classifier. To ensure robust model performance K-fold stratified cross validation is used in SVM based binary classification. To measure the performance of the model, average folded accuracy is used. After studying the binary classification Accuracy results of EOAD group, it is founded that electrode location O1 and O2 which is in occipital brain lobe has more average folded accuracy which is 92.86% whereas LOAD age group has less average folded accuracy of 73.33% for same electrode Locations. This is evidently shows that maximum alpha frequency band between O1 and O2 electrode Location is act as a best feature to distinguish Alzheimer’s from Normal Control in EOAD age group than LOAD group.
  • Effect of Duration on Concurrent Vowel Identification Using the 2D-CNN Models
    Lakshmi Brunda Dogiparthy, Anup George Koshy, Ananthakrishna Chintanpalli, Sujan Yenuganti
    Proceedings of IEEE International Conference on Modelling Simulation and Intelligent Computing Mosicom 2025, 2025
    In a real-world listening environment, listeners are able to segregate the target speech from other competing speech signals. Nonetheless, this segregation ability, based on the fundamental frequency (F0) difference, can be hampered if the duration of the speech is reduced, resulting in poor identification scores. Traditionally, concurrent vowel identification has been utilized to comprehend the impact of the F0 difference prior to identification. Behavioral studies and previous modeling work on concurrent vowels have demonstrated that the identification scores are reduced for shorter durations due to limited availability to utilize F0-based segregation prior to its identification. The goal of this current study is to develop a deep neural network framework that can capture the effect of shorter durations on concurrent vowel scores across F0 differences. It utilized the previous existing 2D-convolutional neural network (CNN) models to capture the duration effects on concurrent vowel identification scores. The identification scores of both vowels for 2D-CNN models across F0 differences performed better than the scores from the previous F0-segregation model for longer and shorter durations. These scores also closely matched with the behavioral data. This study highlights the importance of incorporating auditory neural mechanisms related to duration effects into speech recognition models, eventually contributing to the development of a robust automatic speech recognition systems.
  • A Deep Neural Network Model for Understanding the Effects of Age and Hearing Loss on Identification of Two Overlapping Vowels
    Ashish G. Hallur, Harshavardhan Settibhaktini, Ananthakrishna Chintanpalli, Karra Shivam Sharma, Rejona Susan Julius
    IEEE Access, 2025
    Fundamental frequency (F0) is the perceived pitch of a voice sound. It determines how high or low is a speaker’s voice. When multiple speakers talk at the same time, human ear uses differences in F0 to segregate and identify voices. Behavioral studies explored how F0 differences affect listeners’ ability to identify two overlapping vowels, known as concurrent vowels. Younger adults with normal hearing (YNH) showed improved identification scores as the F0 differences increased, with performance saturating at higher F0 differences. However, scores were lower for older adults with normal hearing (ONH), and poorest for older adults with hearing impairment (OHI). The current study developed a unified deep neural network (DNN) model to predict how people of different ages and hearing abilities identify concurrent vowel scores across F0 differences. The DNN model included a U-Net architecture with dilated convolution layer and multi-task learning. The inputs were the normal neural responses for the YNH model and degraded neural responses were used for the ONH and OHI models to represent the effects of age and hearing loss. The model was trained on YNH neural responses at one specific F0 difference (6-Hz) and later tested across F0 differences and subject groups. The model scores closely matched the behavioral data for each subject group. Statistically, the model’s accuracy surpassed the previous existing models, thus effectively linking DNN modeling and the human behavioral data. This model gives a new perception on how age and hearing loss influence the identification of concurrent vowels.
  • A comparative analysis of CNN-based deep learning architectures for early diagnosis of bone cancer using CT images
    Kanimozhi Sampath, Sivakumar Rajagopal, Ananthakrishna Chintanpalli
    Scientific Reports, 2024
    Bone cancer is a rare in which cells in the bone grow out of control, resulting in destroying the normal bone tissue. A benign type of bone cancer is harmless and does not spread to other body parts, whereas a malignant type can spread to other body parts and might be harmful. According to Cancer Research UK (2021), the survival rate for patients with bone cancer is 40% and early detection can increase the chances of survival by providing treatment at the initial stages. Prior detection of these lumps or masses can reduce the risk of death and treat bone cancer early. The goal of this current study is to utilize image processing techniques and deep learning-based Convolution neural network (CNN) to classify normal and cancerous bone images. Medical image processing techniques, like pre-processing (e.g., median filter), K-means clustering segmentation, and, canny edge detection were used to detect the cancer region in Computer Tomography (CT) images for parosteal osteosarcoma, enchondroma and osteochondroma types of bone cancer. After segmentation, the normal and cancerous affected images were classified using various existing CNN-based models. The results revealed that AlexNet model showed a better performance with a training accuracy of 98%, validation accuracy of 98%, and testing accuracy of 100%.
  • Hybrid and Efficient Neural Network Design for LiDAR Point Cloud Data Processing
    Abhith Krishna, Sainath Bitragunta, Ananthakrishna Chintanpalli
    2023 IEEE 20th India Council International Conference Indicon 2023, 2023
    This work presents a hybrid neural network (NN) model for efficient and accurate semantic segmentation for 3-D point cloud data. The proposed model uses a combination of spatial point cloud features and Multilayer perceptrons (MLPs), is lightweight, and has lesser trainable parameters than many existing models with similar or worse accuracy and performance. Specifically, a processing module of moderate complexity was introduced for effectively extracting and aggregating features from point clouds. The proposed model is highly scalable as it can process many point clouds because of its lightweight nature. Additionally, the model’s performance outperforms more accurate models in processing time. Due to its lightweight feature, the proposed model is useful for next-generation deep learning-enabled devices with low computational power. The proposed model offers good performance while being efficient and fast, balancing accuracy and efficiency.
  • Predicting Level-Dependent Changes in Concurrent Vowel Scores Using the 2D-CNN Models
    Arsalan Malik, Nipun Agarwal, Harshavardhan Settibhaktini, Ananthakrishna Chintanpalli
    IEEE ACM Transactions on Audio Speech and Language Processing, 2023
    Differences in fundamental frequencies (F0s) are an important cue for segregating multiple speakers. However, the ability to avail this cue for identification varies with sound levels. For different-and same-F0 conditions, the identification scores of both vowels increased from low- to mid-levels and then reduced at higher levels for younger adults with normal hearing (YNH). These subjects benefited from the F0 difference; however, this benefit varied across the levels. The current study aims to develop a deep-neural-network (DNN) model that can predict the level-dependent changes in concurrent-vowel scores for YNH subjects. This DNN-model includes two-dimensional convolutional neural networks (2D-CNNs) in parallel, with the same architecture, to predict the concurrent-vowel scores. The input layer was neural responses of the auditory-nerve model to concurrent vowels. The 2D-CNN models were trained and validated using the subsets of the concurrent vowels from 50, 65, and 75 dB SPL for both F0 conditions, using the batch gradient descent algorithm. The trained model was then fine-tuned with a single epoch. The 2D-CNN models were evaluated against vowel levels (25 to 85 dB SPL) and F0 conditions. Compared with previous models, the current model accurately predicts the level-dependent changes in concurrent vowel scores for different-and same-F0 conditions.
  • Advancing Remote Healthcare Using Humanoid and Affective Systems
    Utkarsh Tripathi, Ritvik Saran J, Vinay Chamola, Alireza Jolfaei, Ananthakrishna Chintanpalli
    IEEE Sensors Journal, 2022
    Social distancing and remote work are becoming more prevalent in the post-covid world At the same time, there is a huge demand for remote healthcare sessions as well Although a growing number of such sessions are now utilizing online platforms as a medium of communication, other critical parameters such as the affective state and other feedback opportunities are lost during the transmission of this digital information This paper presents a solution that leverages a brain-computer interface system for this affective feedback and a humanoid robot for teaching effectively during remote sessions The solution uses Kinect as a sensing mechanism for the trainer It utilizes state-of-the-art deep learning algorithms at the back-end to understand the emotional state of the trainee The training poses (from humanoid’s camera feed and kinect) are calculated using AlphaPose compared using inverse kinematics To ascertain the trainees’state (high valence and arousal vs low valence and arousal), a Capsule Network was used that gives an average accuracy of 90 4% for this classification with a low average inference time of 14 3ms on the publicly available DREAMER and AMIGOS datasets The system also allows real-time communication through the humanoid, making this experience even more distinct for the trainee IEEE
  • Concurrent Vowel Identification Using the Deep Neural Network
    Vandana Prasad, Anantha Krishna Chintanpalli
    Lecture Notes in Networks and Systems, 2022
  • Modeling Concurrent Vowel Scores Using the Time Delay Neural Network and Multitask Learning
    Atharva Anand Joshi, Harshavardhan Settibhaktini, Ananthakrishna Chintanpalli
    IEEE ACM Transactions on Audio Speech and Language Processing, 2022
  • Modeling the effects of age and hearing loss on concurrent vowel scores
    Harshavardhan Settibhaktini, Michael G. Heinz, Ananthakrishna Chintanpalli
    Journal of the Acoustical Society of America, 2021
  • IoMT and DNN-Enabled Drone-Assisted Covid-19 Screening and Detection Framework for Rural Areas
    N. Naren, Vinay Chamola, Sainath Baitragunta, Ananthakrishna Chintanpalli, Puneet Mishra, Sujan Yenuganti, Mohsen Guizani
    IEEE Internet of Things Magazine, 2021
  • Comparative Performance Investigation of MIMO-OTFS and MIMO-OFDM using Deep Neural Network Modeling
    Manoj Joshi, Gaurav Punjabi, B. Sainath, Ananthakrishna Chintanpalli
    Proceedings of the 2021 IEEE 18th India Council International Conference Indicon 2021, 2021
  • Modeling concurrent vowel identification for shorter durations
    Harshavardhan Settibhaktini, Ananthakrishna Chintanpalli
    Speech Communication, 2020
  • A Real Time Wavelet Filtering for ECG Baseline Wandering Removal
    Akul Malhotra, Ananthakrishna Chintanpalli
    2020 International Conference on Artificial Intelligence and Signal Processing Aisp 2020, 2020
  • Level-dependent changes in concurrent vowel scores using the multi-layer perceptron
    Akshay Joshi, Anantha Krishna Chintanpalli
    Lecture Notes in Electrical Engineering, 2020
  • Revisiting models of concurrent vowel identification: The critical case of no pitch differences
    Samuel S. Smith, Ananthakrishna Chintanpalli, Michael G. Heinz, Christian J. Sumner
    Acta Acustica United with Acustica, 2018
  • Modeling the level-dependent changes of concurrent vowel scores
    Harshavardhan Settibhaktini, Ananthakrishna Chintanpalli
    Journal of the Acoustical Society of America, 2018
  • Computational model predictions of level dependent changes in vowel identification with addition of rate-place cue
    Pranav Misra, Ananthakrishna Chintanpalli
    4th IEEE International Conference on Signal Processing Computing and Control Ispcc 2017, 2017
  • Effects of age and hearing loss on concurrent vowel identification
    Ananthakrishna Chintanpalli, Jayne B. Ahlstrom, Judy R. Dubno
    Journal of the Acoustical Society of America, 2016
  • Computational model predictions of cues for concurrent vowel identification
    Ananthakrishna Chintanpalli, Jayne B. Ahlstrom, Judy R. Dubno
    JARO Journal of the Association for Research in Otolaryngology, 2014
  • The use of confusion patterns to evaluate the neural basis for concurrent vowel identification
    Ananthakrishna Chintanpalli, Michael G. Heinz
    Journal of the Acoustical Society of America, 2013
  • Modeling the anti-masking effects of the olivocochlear reflex in auditory nerve responses to tones in sustained noise
    Ananthakrishna Chintanpalli, Skyler G. Jennings, Michael G. Heinz, Elizabeth A. Strickland
    JARO Journal of the Association for Research in Otolaryngology, 2012
  • Effect of auditory-nerve response variability on estimates of tuning curves
    Ananthakrishna Chintanpalli, Michael G. Heinz
    Journal of the Acoustical Society of America, 2007
  • Optical time-frequency scaling for signal processing applications
    C. K. Madsen, A. Chintanpalli
    Proceedings of SPIE the International Society for Optical Engineering, 2006

RECENT SCHOLAR PUBLICATIONS

  • Predicting the age effects on concurrent vowel scores using a temporal jitter computational model
    H Settibhaktini, R Rathi, A Chintanpalli
    Applied Acoustics 245, 111215 , 2026
    2026
  • Effect of Duration on Concurrent Vowel Identification Using the 2D-CNN Models
    LB Dogiparthy, AG Koshy, A Chintanpalli, S Yenuganti
    2025 International Conference on Modeling, Simulation & Intelligent … , 2025
    2025
  • A Deep Neural Network Model for Understanding the Effects of Age and Hearing Loss on Identification of Two Overlapping Vowels
    AG Hallur, H Settibhaktini, A Chintanpalli, KS Sharma, RS Julius
    IEEE Access 13, 207749-207761 , 2025
    2025
  • Machine Learning Framework of EEG for binary classification of Early on-set Alzheimer’s Disease
    A Chintanpalli, R Kumar
    2025 International Conference on Biomedical Engineering and Sustainable … , 2025
    2025
  • Predicting level-dependent changes in concurrent vowel scores using the 2D-CNN models
    A Malik, N Agarwal, H Settibhaktini, A Chintanpalli
    IEEE/ACM Transactions on Audio, Speech, and Language Processing 31, 2558-2566 , 2023
    2023
    Citations: 3
  • Modeling concurrent vowel scores using the time delay neural network and multitask learning
    AA Joshi, H Settibhaktini, A Chintanpalli
    IEEE/ACM Transactions on Audio, Speech, and Language Processing 30, 2452-2459 , 2022
    2022
    Citations: 4
  • Modeling the effects of age and hearing loss on concurrent vowel scores
    H Settibhaktini, MG Heinz, A Chintanpalli
    The Journal of the Acoustical Society of America 150 (5), 3581-3592 , 2021
    2021
    Citations: 5
  • IoMT and DNN-enabled drone-assisted COVID-19 screening and detection framework for rural areas
    N Naren, V Chamola, S Baitragunta, A Chintanpalli, P Mishra, ...
    IEEE Internet of Things Magazine 4 (2), 4-9 , 2021
    2021
    Citations: 49
  • Concurrent vowel identification using the deep neural network
    V Prasad, AK Chintanpalli
    International Conference on Machine Learning and Big Data Analytics, 78-84 , 2021
    2021
    Citations: 4
  • Advancing remote healthcare using humanoid and affective systems
    U Tripathi, V Chamola, A Jolfaei, A Chintanpalli
    IEEE Sensors Journal 22 (18), 17606-17614 , 2021
    2021
    Citations: 26
  • Modeling concurrent vowel identification for shorter durations
    H Settibhaktini, A Chintanpalli
    Speech Communication 125, 1-6 , 2020
    2020
    Citations: 9
  • Level-dependent changes in concurrent vowel scores using the multi-layer perceptron
    A Joshi, AK Chintanpalli
    International conference on Modelling, Simulation and Intelligent Computing … , 2020
    2020
    Citations: 4
  • A real time wavelet filtering for ECG baseline wandering removal
    A Malhotra, A Chintanpalli
    2020 International Conference on Artificial Intelligence and Signal … , 2020
    2020
    Citations: 6
  • Revisiting models of concurrent vowel identification: the critical case of no pitch differences
    SS Smith, A Chintanpalli, MG Heinz, CJ Sumner
    Acta acustica united with acustica: the journal of the European Acoustics … , 2018
    2018
    Citations: 4
  • Modeling the level-dependent changes of concurrent vowel scores
    H Settibhaktini, A Chintanpalli
    The Journal of the Acoustical Society of America 143 (1), 440-449 , 2018
    2018
    Citations: 8
  • Computational model predictions of level dependent changes in vowel identification with addition of rate-place cue
    P Misra, A Chintanpalli
    2017 4th International Conference on Signal Processing, Computing and … , 2017
    2017
  • Effects of age and hearing loss on concurrent vowel identification
    A Chintanpalli, JB Ahlstrom, JR Dubno
    The Journal of the Acoustical Society of America 140 (6), 4142-4153 , 2016
    2016
    Citations: 32
  • Computational model predictions of level dependent changes in vowel identification
    A Chintanpalli, S Raghotham
    ​ International Conference on Signal Processing​​(ICSP 2016), 1-4 , 2016
    2016
    Citations: 1
  • Computational model predictions of cues for concurrent vowel identification
    A Chintanpalli, JB Ahlstrom, JR Dubno
    Journal of the Association for Research in Otolaryngology 15 (5), 823-837 , 2014
    2014
    Citations: 24
  • The use of confusion patterns to evaluate the neural basis for concurrent vowel identification
    A Chintanpalli, MG Heinz
    The Journal of the Acoustical Society of America 134 (4), 2988-3000 , 2013
    2013
    Citations: 27

MOST CITED SCHOLAR PUBLICATIONS

  • IoMT and DNN-enabled drone-assisted COVID-19 screening and detection framework for rural areas
    N Naren, V Chamola, S Baitragunta, A Chintanpalli, P Mishra, ...
    IEEE Internet of Things Magazine 4 (2), 4-9 , 2021
    2021
    Citations: 49
  • Modeling the anti-masking effects of the olivocochlear reflex in auditory nerve responses to tones in sustained noise
    A Chintanpalli, SG Jennings, MG Heinz, EA Strickland
    Journal of the Association for Research in Otolaryngology 13 (2), 219-235 , 2012
    2012
    Citations: 46
  • Effects of age and hearing loss on concurrent vowel identification
    A Chintanpalli, JB Ahlstrom, JR Dubno
    The Journal of the Acoustical Society of America 140 (6), 4142-4153 , 2016
    2016
    Citations: 32
  • Effect of auditory-nerve response variability on estimates of tuning curves
    A Chintanpalli, MG Heinz
    The Journal of the Acoustical Society of America 122 (6), EL203-EL209 , 2007
    2007
    Citations: 30
  • The use of confusion patterns to evaluate the neural basis for concurrent vowel identification
    A Chintanpalli, MG Heinz
    The Journal of the Acoustical Society of America 134 (4), 2988-3000 , 2013
    2013
    Citations: 27
  • Advancing remote healthcare using humanoid and affective systems
    U Tripathi, V Chamola, A Jolfaei, A Chintanpalli
    IEEE Sensors Journal 22 (18), 17606-17614 , 2021
    2021
    Citations: 26
  • Computational model predictions of cues for concurrent vowel identification
    A Chintanpalli, JB Ahlstrom, JR Dubno
    Journal of the Association for Research in Otolaryngology 15 (5), 823-837 , 2014
    2014
    Citations: 24
  • Modeling concurrent vowel identification for shorter durations
    H Settibhaktini, A Chintanpalli
    Speech Communication 125, 1-6 , 2020
    2020
    Citations: 9
  • Modeling the level-dependent changes of concurrent vowel scores
    H Settibhaktini, A Chintanpalli
    The Journal of the Acoustical Society of America 143 (1), 440-449 , 2018
    2018
    Citations: 8
  • A real time wavelet filtering for ECG baseline wandering removal
    A Malhotra, A Chintanpalli
    2020 International Conference on Artificial Intelligence and Signal … , 2020
    2020
    Citations: 6
  • Modeling the effects of age and hearing loss on concurrent vowel scores
    H Settibhaktini, MG Heinz, A Chintanpalli
    The Journal of the Acoustical Society of America 150 (5), 3581-3592 , 2021
    2021
    Citations: 5
  • Modeling concurrent vowel scores using the time delay neural network and multitask learning
    AA Joshi, H Settibhaktini, A Chintanpalli
    IEEE/ACM Transactions on Audio, Speech, and Language Processing 30, 2452-2459 , 2022
    2022
    Citations: 4
  • Concurrent vowel identification using the deep neural network
    V Prasad, AK Chintanpalli
    International Conference on Machine Learning and Big Data Analytics, 78-84 , 2021
    2021
    Citations: 4
  • Level-dependent changes in concurrent vowel scores using the multi-layer perceptron
    A Joshi, AK Chintanpalli
    International conference on Modelling, Simulation and Intelligent Computing … , 2020
    2020
    Citations: 4
  • Revisiting models of concurrent vowel identification: the critical case of no pitch differences
    SS Smith, A Chintanpalli, MG Heinz, CJ Sumner
    Acta acustica united with acustica: the journal of the European Acoustics … , 2018
    2018
    Citations: 4
  • Predicting level-dependent changes in concurrent vowel scores using the 2D-CNN models
    A Malik, N Agarwal, H Settibhaktini, A Chintanpalli
    IEEE/ACM Transactions on Audio, Speech, and Language Processing 31, 2558-2566 , 2023
    2023
    Citations: 3
  • Computational model predictions of level dependent changes in vowel identification
    A Chintanpalli, S Raghotham
    ​ International Conference on Signal Processing​​(ICSP 2016), 1-4 , 2016
    2016
    Citations: 1
  • Evaluating the neural basis for concurrent vowel identification in dry and reverberant conditions
    A Chintanpalli
    2011
    Citations: 1
  • The use of confusion patterns to evaluate the neural basis for concurrent vowel identification.
    A Chintanpalli, MG Heinz
    The Journal of the Acoustical Society of America 127 (3_Supplement), 1991-1991 , 2010
    2010
    Citations: 1
  • Predicting the age effects on concurrent vowel scores using a temporal jitter computational model
    H Settibhaktini, R Rathi, A Chintanpalli
    Applied Acoustics 245, 111215 , 2026
    2026