Prasanta Ghosh

Ramakrishna Mission Vidyamandira

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Suvendu Maity

Ramakrishna Mission Vidyamandira

234

Thomas Weyhermüller

Max Planck Institute for Chemical Energy Conversion

127

Sachinath Bera

Vidyasagar University

Pinaki Saha

Ramakrishna Mission Vidyamandira

Chhanda Mukhopadhyay

University of Calcutta

Suman Kundu

University of Delhi

Chiranjeevi Yarra

Indian Institute of Technology Hyderabad

Sandip Mondal

Jadavpur University

Amit Saha Roy

Aliah University

Shrikanth Narayanan

University of Southern California

Cooperative Institutions

Indian Institute of Science Bangalore

108

Jadavpur University

University of Calcutta

Indian Association for the Cultivation of Science

Indian Institute of Chemical Biology

Council of Scientific and Industrial Research

University of Southern California

Centre National de la Recherche Scientifique

University of Delhi

Indian Institute of Technology Kharagpur

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Metal promoted conversion of aromatic amines toortho-phenylenediimine derivatives by a radical coupling path

Dalton Transactions (2020)

Debarpan Dutta Suman Kundu Thomas Weyhermüller Prasanta Ghosh

Transition metal promoted conversion ofo-substituted arylamines too-phenylenediimine derivatives by a radical C–N coupling path is reported.

Coupling reaction

10.1039/d0dt00089b

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Citations (3)

SegNet-Based Deep Representation Learning for Dysphagia Classification

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2022)

Siddharth Subramani Achuth Rao M V Anwesha Roy Prasanna Suresh Hegde Prasanta Ghosh

Swallowing disorders, broadly known as Dysphagia, are difficulties in the process of swallowing food. Many currently available methods for classifying healthy and dysphagic swallows typically use hand-picked acoustic features. This article presents a SegNet-based method for classifying healthy and dysphagic swallow signals by learning mel-spectrogram features. Swallow sounds were recorded from a total of 24 subjects using a microphone based cervical auscultation (CA) system. Each subject swallowed multiple samples of water of volumes 5ml, 10ml and 15ml, and also performed multiple dry swallows. The experiments investigated the significance of temporal structures in the SegNet-learnt representations. The classification performance was evaluated at different model depths in order to identify the optimum feature time-scale that maximized the classification performance. The proposed method was found to be more robust to variations in the signatures of swallow signals across multiple volumes of water, against a baseline method across a single volume of water. The best performing model yielded a mean test F1-score of 80.13% (±4.62%) in a 5-fold cross validation setup.

Spectrogram

Auscultation

Feature (linguistics)

10.1109/icassp43922.2022.9747732

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Citations (4)

CCDC 2225124: Experimental Crystal Structure Determination

The Cambridge Structural Database (2023)

Debasish Samanta Pinaki Saha Suvendu Maity Sudipto Mondal Prasanta Ghosh

An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.

10.5517/ccdc.csd.cc2dpf6r

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Bark Frequency Transform Using an Arbitrary Order Allpass Filter

IEEE Signal Processing Letters (2010)

Prasanta Ghosh Shrikanth Narayanan

We propose an arbitrary order stable allpass filter structure for frequency transformation from Hertz to Bark scale. According to the proposed filter structure, the first order allpass filter is causal, but the second and higher order allpass filters are non-causal. We find that the accuracy of the transformation significantly improves when a second or higher order allpass filter is designed compared to a first order allpass filter. We also find that the RMS error of the transformation monotonically decreases by increasing the order of the allpass filter.

All-pass filter

10.1109/lsp.2010.2046192

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Citations (3)

Speech rate estimation using representations learned from speech with convolutional neural network

Renuka Mannem H Jyothi Aravind Illa Prasanta Ghosh

With advancement in machine learning techniques, several speech related applications deploy end-to-end models to learn relevant features from the raw speech signal. In this work, we focus on the speech rate estimation task using an end-to-end model to learn representation from raw speech in a data driven manner. We propose an end-to-end model that comprises of 1-d convolutional layer to extract representations from raw speech and a convolutional dense neural network (CDNN) to predict speech rate from these representations. The primary aim of the work is to understand the nature of representations learned by end-to-end model for the speech rate estimation task. Experiments are performed using TIMIT corpus, in seen and unseen subject conditions. Experimental results reveal that, the frequency response of the learned 1-d CNN filters are low-pass in nature, and center frequencies of majority of the filters lie below 1000Hz. While comparing the performance of the proposed end-to-end system with the baseline MFCC based approach, we find that the performance of the learned features with CNN are on par with MFCC.

Mel-frequency cepstrum

TIMIT

Word error rate

10.1109/spcom50965.2020.9179502

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Citations (1)

CCDC 2170560: Experimental Crystal Structure Determination

The Cambridge Structural Database (2023)

Sayan Pramanik Pinaki Saha Prasanta Ghosh Chhanda Mukhopadhyay

10.5517/ccdc.csd.cc2bvn2y

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