Search Results for author: Arijit Biswas

Found 16 papers, 2 papers with code

Multi-User MultiWOZ: Task-Oriented Dialogues among Multiple Users

1 code implementation31 Oct 2023 Yohan Jo, Xinyan Zhao, Arijit Biswas, Nikoletta Basiou, Vincent Auvray, Nikolaos Malandrakis, Angeliki Metallinou, Alexandros Potamianos

While most task-oriented dialogues assume conversations between the agent and one user at a time, dialogue systems are increasingly expected to communicate with multiple users simultaneously who make decisions collaboratively.

Decision Making Dialogue State Tracking

Generative Machine Listener

no code implementations18 Aug 2023 Guanxin Jiang, Lars Villemoes, Arijit Biswas

We show how a neural network can be trained on individual intrusive listening test scores to predict a distribution of scores for each pair of reference and coded input stereo or binaural signals.

Data Augmentation

AudioVMAF: Audio Quality Prediction with VMAF

no code implementations7 Aug 2023 Arijit Biswas, Harald Mundt

In this study, we propose an auditory-inspired frontend in existing VMAF for creating videos of reference and coded spectrograms, and extended VMAF for measuring coded audio quality.

Stereo InSE-NET: Stereo Audio Quality Predictor Transfer Learned from Mono InSE-NET

no code implementations23 Sep 2022 Arijit Biswas, Guanxin Jiang

Automatic coded audio quality predictors are typically designed for evaluating single channels without considering any spatial aspects.

InSE-NET: A Perceptually Coded Audio Quality Model based on CNN

no code implementations30 Aug 2021 Guanxin Jiang, Arijit Biswas, Christian Bergler, Andreas Maier

Automatic coded audio quality assessment is an important task whose progress is hampered by the scarcity of human annotations, poor generalization to unseen codecs, bitrates, content-types, and a lack of flexibility of existing approaches.

Data Augmentation

Dialog Simulation with Realistic Variations for Training Goal-Oriented Conversational Systems

no code implementations16 Nov 2020 Chien-Wei Lin, Vincent Auvray, Daniel Elkind, Arijit Biswas, Maryam Fazel-Zarandi, Nehal Belgamwar, Shubhra Chandra, Matt Zhao, Angeliki Metallinou, Tagyoung Chung, Charlie Shucheng Zhu, Suranjit Adhikari, Dilek Hakkani-Tur

Our approach includes a novel goal-sampling technique for sampling plausible user goals and a dialog simulation technique that uses heuristic interplay between the user and the system (Alexa), where the user tries to achieve the sampled goal.

Goal-Oriented Dialog Natural Language Understanding

Analysis by Adversarial Synthesis -- A Novel Approach for Speech Vocoding

no code implementations1 Jul 2019 Ahmed Mustafa, Arijit Biswas, Christian Bergler, Julia Schottenhamml, Andreas Maier

Recently, autoregressive deep generative models such as WaveNet and SampleRNN have been used as speech vocoders to scale up the perceptual quality of the reconstructed signals without increasing the coding rate.

eCommerceGAN : A Generative Adversarial Network for E-commerce

no code implementations10 Jan 2018 Ashutosh Kumar, Arijit Biswas, Subhajit Sanyal

Exploring the space of all plausible orders could help us better understand the relationships between the various entities in an e-commerce ecosystem, namely the customers and the products they purchase.

Generative Adversarial Network

MRNet-Product2Vec: A Multi-task Recurrent Neural Network for Product Embeddings

no code implementations21 Sep 2017 Arijit Biswas, Mukul Bhutani, Subhajit Sanyal

E-commerce websites such as Amazon, Alibaba, Flipkart, and Walmart sell billions of products.

Active learning with version spaces for object detection

no code implementations22 Nov 2016 Soumya Roy, Vinay P. Namboodiri, Arijit Biswas

Previous works on object detection model the problem as a structured regression problem which ranks the correct bounding boxes more than the background ones.

Active Learning Object +3

Weakly Supervised Learning of Heterogeneous Concepts in Videos

no code implementations12 Jul 2016 Sohil Shah, Kuldeep Kulkarni, Arijit Biswas, Ankit Gandhi, Om Deshmukh, Larry Davis

Typical textual descriptions that accompany online videos are 'weak': i. e., they mention the main concepts in the video but not their corresponding spatio-temporal locations.

General Classification Weakly-supervised Learning

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