no code implementations • 3 Oct 2022 • Swapnil Bhosale, Rupayan Chakraborty, Sunil Kumar Kopparapu
Automatic Audio Captioning (AAC) refers to the task of translating an audio sample into a natural language (NL) text that describes the audio events, source of the events and their relationships.
no code implementations • 2 Feb 2022 • Upasana Tiwari, Rupayan Chakraborty, Sunil Kumar Kopparapu
The usefulness of these features for EEG emotion classification is investigated through extensive experiments using state-of-the-art classifiers.
no code implementations • 28 Jan 2022 • Swapnil Bhosale, Rupayan Chakraborty, Sunil Kumar Kopparapu
Automatic Audio Captioning (AAC) refers to the task of translating audio into a natural language that describes the audio events, source of the events and their relationships.
1 code implementation • 24 Mar 2021 • Ayush Tripathi, Rupayan Chakraborty, Sunil Kumar Kopparapu
In this paper, we propose a novel three step technique to address imbalanced data.
no code implementations • 16 Feb 2021 • Swapnil Bhosale, Rupayan Chakraborty, Sunil Kumar Kopparapu
In this paper, we propose to replace the typical prototypical loss function with an Episodic Triplet Mining (ETM) technique.
no code implementations • 27 Feb 2020 • Rupayan Chakraborty, Meghna Pandharipande, Chitralekha Bhat, Sunil Kumar Kopparapu
The objective of this work is to use speech processing and machine learning techniques to automatically identify the stage of dementia such as mild cognitive impairment (MCI) or Alzheimers disease (AD).
no code implementations • 18 Dec 2019 • Sri Harsha Dumpala, Imran Sheikh, Rupayan Chakraborty, Sunil Kumar Kopparapu
Naturally introduced perturbations in audio signal, caused by emotional and physical states of the speaker, can significantly degrade the performance of Automatic Speech Recognition (ASR) systems.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +2
no code implementations • WS 2018 • Imran Sheikh, Sri Harsha Dumpala, Rupayan Chakraborty, Sunil Kumar Kopparapu
Multimodal sentiment classification in practical applications may have to rely on erroneous and imperfect views, namely (a) language transcription from a speech recognizer and (b) under-performing acoustic views.
Automatic Speech Recognition (ASR) General Classification +2
no code implementations • 15 Dec 2017 • Sri Harsha Dumpala, Rupayan Chakraborty, Sunil Kumar Kopparapu
Deep learning based discriminative methods, being the state-of-the-art machine learning techniques, are ill-suited for learning from lower amounts of data.
no code implementations • 24 Apr 2017 • Sri Harsha Dumpala, Rupayan Chakraborty, Sunil Kumar Kopparapu
It is not immediately clear (a) how a priori temporal knowledge can be used in a FFNN architecture (b) how a FFNN performs when provided with this knowledge about temporal correlations (assuming available) during training.