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Anurag Kumar

Facebook Research

49 papers hereh-index 233.7k citations68 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author15
  • middle author24
  • last author10

Across the 49 of 49 papers where every author was matched, so the position is known.

fields
  • cs.SD32
  • eess.AS9
  • cs.CV4
  • cs.LG3
  • cs.CL1
affiliations
  • Facebook Research
Homepage
same name
  • Anurag Kumar — 42 papers, h 33
  • Anurag Kumar — 9 papers, h 5
  • Anurag Kumar — 7 papers, h 5
  • Anurag Kumar — 5 papers, h 2
  • Anurag Kumar — 4 papers, h 5
  • Anurag Kumar — 3 papers, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20152023
most citedAudio Event Detection using Weakly Labeled Data

182 citations · 435 across the 38 of their papers we have counts for

collaborators
Showing 2017Show all

4 papers · 1 filter

cs.SD2017

Knowledge Transfer from Weakly Labeled Audio using Convolutional Neural Network for Sound Events and Scenes

Anurag Kumar, Maksim Khadkevich, Christian Fugen

In this work we propose approaches to effectively transfer knowledge from weakly labeled web audio data. We first describe a convolutional neural network (CNN) based framework for…

cs.SD2017

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…

cs.SD2017

Content-based Representations of audio using Siamese neural networks

Pranay Manocha, Rohan Badlani, Anurag Kumar +3

In this paper, we focus on the problem of content-based retrieval for audio, which aims to retrieve all semantically similar audio recordings for a given audio clip query. This pro…

cs.SD2017★ 25 cited

Deep CNN Framework for Audio Event Recognition using Weakly Labeled Web Data

Anurag Kumar, Bhiksha Raj

The development of audio event recognition systems require labeled training data, which are generally hard to obtain. One promising source of recordings of audio events is the larg…

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