90 citations · 194 across the 23 of their papers we have counts for
27 papers
Psychoacoustic Challenges Of Speech Enhancement On VoIP Platforms
Joseph Konan, Shikhar Agnihotri, Ojas Bhargave +4
Within the ambit of VoIP (Voice over Internet Protocol) telecommunications, the complexities introduced by acoustic transformations merit rigorous analysis. This research, rooted i…
Importance of negative sampling in weak label learning
Ankit Shah, Fuyu Tang, Zelin Ye +2
Weak-label learning is a challenging task that requires learning from data "bags" containing positive and negative instances, but only the bag labels are known. The pool of negativ…
Plug in the Safety Chip: Enforcing Constraints for LLM-driven Robot Agents
Ziyi Yang, Shreyas S. Raman, Ankit Shah +1
Recent advancements in large language models (LLMs) have enabled a new research domain, LLM agents, for solving robotics and planning tasks by leveraging the world knowledge and ge…
Improving Perceptual Quality, Intelligibility, and Acoustics on VoIP Platforms
Joseph Konan, Ojas Bhargave, Shikhar Agnihotri +12
In this paper, we present a method for fine-tuning models trained on the Deep Noise Suppression (DNS) 2020 Challenge to improve their performance on Voice over Internet Protocol (V…
Approach to Learning Generalized Audio Representation Through Batch Embedding Covariance Regularization and Constant-Q Transforms
Ankit Shah, Shuyi Chen, Kejun Zhou +2
General-purpose embedding is highly desirable for few-shot even zero-shot learning in many application scenarios, including audio tasks. In order to understand representations bett…
Grounding Complex Natural Language Commands for Temporal Tasks in Unseen Environments
Jason Xinyu Liu, Ziyi Yang, Ifrah Idrees +4
Grounding navigational commands to linear temporal logic (LTL) leverages its unambiguous semantics for reasoning about long-horizon tasks and verifying the satisfaction of temporal…
Conformers are All You Need for Visual Speech Recognition
Oscar Chang, Hank Liao, Dmitriy Serdyuk +2
Visual speech recognition models extract visual features in a hierarchical manner. At the lower level, there is a visual front-end with a limited temporal receptive field that proc…
Automated Audio Captioning and Language-Based Audio Retrieval
Clive Gomes, Hyejin Park, Patrick Kollman +3
This project involved participation in the DCASE 2022 Competition (Task 6) which had two subtasks: (1) Automated Audio Captioning and (2) Language-Based Audio Retrieval. The first…
On the pragmatism of using binary classifiers over data intensive neural network classifiers for detection of COVID-19 from voice
Ankit Shah, Hira Dhamyal, Yang Gao +4
Lately, there has been a global effort by multiple research groups to detect COVID-19 from voice. Different researchers use different kinds of information from the voice signal to…
Ontological Learning from Weak Labels
Larry Tang, Po Hao Chou, Yi Yu Zheng +3
Ontologies encompass a formal representation of knowledge through the definition of concepts or properties of a domain, and the relationships between those concepts. In this work,…
Triple Attention Network architecture for MovieQA
Ankit Shah, Tzu-Hsiang Lin, Shijie Wu
Movie question answering, or MovieQA is a multimedia related task wherein one is provided with a video, the subtitle information, a question and candidate answers for it. The task…
Audio-Visual Scene-Aware Dialog and Reasoning using Audio-Visual Transformers with Joint Student-Teacher Learning
Ankit P. Shah, Shijie Geng, Peng Gao +5
In previous work, we have proposed the Audio-Visual Scene-Aware Dialog (AVSD) task, collected an AVSD dataset, developed AVSD technologies, and hosted an AVSD challenge track at bo…
An Overview of Techniques for Biomarker Discovery in Voice Signal
Rita Singh, Ankit Shah, Hira Dhamyal
This paper reflects on the effect of several categories of medical conditions on human voice, focusing on those that may be hypothesized to have effects on voice, but for which the…
Feature extraction and evaluation for BioMedical Question Answering
Ankit Shah, Srishti Singh, Shih-Yen Tao
In this paper, we present our work on the BioASQ pipeline. The goal is to answer four types of questions: summary, yes/no, factoids, and list. Our goal is to empirically evaluate d…
Training image classifiers using Semi-Weak Label Data
Anxiang Zhang, Ankit Shah, Bhiksha Raj
In Multiple Instance learning (MIL), weak labels are provided at the bag level with only presence/absence information known. However, there is a considerable gap in performance in…
Tartan: A retrieval-based socialbot powered by a dynamic finite-state machine architecture
George Larionov, Zachary Kaden, Hima Varsha Dureddy +5
This paper describes the Tartan conversational agent built for the 2018 Alexa Prize Competition. Tartan is a non-goal-oriented socialbot focused around providing users with an enga…
Learning Sound Events From Webly Labeled Data
Anurag Kumar, Ankit Shah, Bhiksha Raj +1
In the last couple of years, weakly labeled learning has turned out to be an exciting approach for audio event detection. In this work, we introduce webly labeled learning for soun…
Activity Recognition on a Large Scale in Short Videos - Moments in Time Dataset
Ankit Shah, Harini Kesavamoorthy, Poorva Rane +3
Moments capture a huge part of our lives. Accurate recognition of these moments is challenging due to the diverse and complex interpretation of the moments. Action recognition refe…
Natural Language Person Search Using Deep Reinforcement Learning
Ankit Shah, Tyler Vuong
Recent success in deep reinforcement learning is having an agent learn how to play Go and beat the world champion without any prior knowledge of the game. In that task, the agent h…
CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis
Ankit Shah, Jean Baptiste Lamare, Tuan Nguyen Anh +1
This paper presents a novel dataset for traffic accidents analysis. Our goal is to resolve the lack of public data for research about automatic spatio-temporal annotations for traf…
Large-Scale Weakly Labeled Semi-Supervised Sound Event Detection in Domestic Environments
Romain Serizel, Nicolas Turpault, Hamid Eghbal-Zadeh +1
This paper presents DCASE 2018 task 4. The task evaluates systems for the large-scale detection of sound events using weakly labeled data (without time boundaries). The target of t…
A Closer Look at Weak Label Learning for Audio Events
Ankit Shah, Anurag Kumar, Alexander G. Hauptmann +1
Audio content analysis in terms of sound events is an important research problem for a variety of applications. Recently, the development of weak labeling approaches for audio or s…
NELS -- Never-Ending Learner of Sounds
Benjamin Elizalde, Rohan Badlani, Ankit Shah +2
Sounds are essential to how humans perceive and interact with the world and are captured in recordings and shared on the Internet on a minute-by-minute basis. These recordings, whi…
Framework for evaluation of sound event detection in web videos
Rohan Badlani, Ankit Shah, Benjamin Elizalde +2
The largest source of sound events is web videos. Most videos lack sound event labels at segment level, however, a significant number of them do respond to text queries, from a mat…