What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of study are Artificial intelligence, Pattern recognition, Speech recognition, Speech processing and Feature extraction.
As a part of the same scientific family, Kuldip K. Paliwal mostly works in the field of Artificial intelligence, focusing on Machine learning and, on occasion, Identification.
Kuldip K. Paliwal has researched Pattern recognition in several fields, including Quantization and Data mining.
His Speech recognition study combines topics from a wide range of disciplines, such as Speech perception, Filter and Mel-frequency cepstrum.
Kuldip K. Paliwal interconnects Recurrent neural nets, Speech coding, Symbol and Signal processing in the investigation of issues within Speech processing.
His Feature extraction research is multidisciplinary, relying on both Classifier, Principal component analysis, Support vector machine and Feature vector.
His most cited work include:
- Bidirectional recurrent neural networks (3869 citations)
- Efficient vector quantization of LPC parameters at 24 bits/frame (603 citations)
- Speech Coding and Synthesis (530 citations)
What are the main themes of his work throughout his whole career to date?
Kuldip K. Paliwal mainly investigates Speech recognition, Artificial intelligence, Pattern recognition, Algorithm and Speech enhancement.
His biological study spans a wide range of topics, including Mel-frequency cepstrum, Noise and Signal processing.
His Artificial intelligence study frequently links to other fields, such as Machine learning.
His Pattern recognition research integrates issues from Facial recognition system and Discrete cosine transform.
His study looks at the intersection of Speech enhancement and topics like Intelligibility with Short-time Fourier transform.
His Speech processing study which covers Speech coding that intersects with Coding.
He most often published in these fields:
- Speech recognition (57.18%)
- Artificial intelligence (46.61%)
- Pattern recognition (37.13%)
What were the highlights of his more recent work (between 2016-2021)?
- Speech enhancement (18.43%)
- Artificial intelligence (46.61%)
- Speech recognition (57.18%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Speech enhancement, Artificial intelligence, Speech recognition, Deep learning and Kalman filter.
His work deals with themes such as Algorithm, Noise measurement and Estimator, which intersect with Speech enhancement.
Artificial intelligence is closely attributed to Pattern recognition in his work.
His study in Speech recognition is interdisciplinary in nature, drawing from both Self attention, Robustness, Coloured noise and Invariant extended Kalman filter.
In his study, which falls under the umbrella issue of Deep learning, Topology and Feature is strongly linked to Residual.
His Kalman filter study combines topics in areas such as Linear prediction, Estimation theory and Residual noise.
Between 2016 and 2021, his most popular works were:
- Capturing non-local interactions by long short-term memory bidirectional recurrent neural networks for improving prediction of protein secondary structure, backbone angles, contact numbers and solvent accessibility. (164 citations)
- SPIDER2: A Package to Predict Secondary Structure, Accessible Surface Area, and Main-Chain Torsional Angles by Deep Neural Networks (97 citations)
- Accurate prediction of protein contact maps by coupling residual two-dimensional bidirectional long short-term memory with convolutional neural networks. (69 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Artificial neural network, Artificial intelligence, Algorithm, Deep learning and Recurrent neural network are his primary areas of study.
His research in Artificial neural network intersects with topics in Protein structure, Correlation coefficient, Speaker identification and Pattern recognition.
His Pattern recognition research is multidisciplinary, incorporating elements of Additive white Gaussian noise and Sequence analysis.
Kuldip K. Paliwal connects Artificial intelligence with Fold in his research.
His biological study deals with issues like Speech enhancement, which deal with fields such as Sampling, Speech processing, Computational complexity theory, Reduction and Discrete Fourier transform.
His studies in Recurrent neural network integrate themes in fields like Accessible surface area, Protein structure prediction and Protein secondary structure.
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