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Audio Event Detection using Weakly Labeled Data

• Anurag Kumar, Bhiksha Raj
• ACM Multimedia
• 2016

Acoustic event detection is essential for content analysis and description of multimedia recordings. The majority of current literature on the topic learns the detectors through fully-supervised… (More)

Audio event detection from acoustic unit occurrence patterns

• Anurag Kumar, Pranay Dighe, Rita Singh, Sourish Chaudhuri, Bhiksha Raj
• 2012 IEEE International Conference on Acoustics…
• 2012

In most real-world audio recordings, we encounter several types of audio events. In this paper, we develop a technique for detecting signature audio events, that is based on identifying patterns of… (More)

Unsupervised Fusion Weight Learning in Multiple Classifier Systems

• Anurag Kumar, Bhiksha Raj
• ArXiv
• 2015

In this paper we present an unsupervised method to learn the weights with which the scores of multiple classifiers must be combined in classifier fusion settings. We also introduce a novel metric for… (More)

Experiments on the DCASE Challenge 2016: Acoustic Scene Classification and Sound Event Detection in Real Life Recording

• Benjamin Elizalde, Anurag Kumar, +4 authors Ian Lane
• ArXiv
• 2016

In this paper we present our work on Task 1 Acoustic Scene Classification and Task 3 Sound Event Detection in Real Life Recordings. Among our experiments we have low-level and high-level features,… (More)

A novel ranking method for multiple classifier systems

• Anurag Kumar, Bhiksha Raj
• 2015 IEEE International Conference on Acoustics…
• 2015

We introduce an unsupervised optimization method for optimal fusion of multiple classifiers in retrieval problems. The method is based on a ranking loss called the “clarity” index, which does not… (More)

Weakly supervised scalable audio content analysis

• Anurag Kumar, Bhiksha Raj
• 2016 IEEE International Conference on Multimedia…
• 2016

Audio Event Detection is an important task for content analysis of multimedia data. Most of the current works on detection of audio events is driven through supervised learning approaches. We propose… (More)

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

• Anurag Kumar, Bhiksha Raj
• ArXiv
• 2017

The development of audio event recognition models requires labeled training data, which are generally hard to obtain. One promising source of recordings of audio events is the large amount of… (More)

Audio event and scene recognition: A unified approach using strongly and weakly labeled data

• Anurag Kumar, Bhiksha Raj
• 2017 International Joint Conference on Neural…
• 2017

In this paper we propose a novel learning framework called Supervised and Weakly Supervised Learning where the goal is to learn simultaneously from weakly and strongly labeled data. Strongly labeled… (More)

Event detection in short duration audio using Gaussian Mixture Model and Random Forest Classifier

• Anurag Kumar, Rajesh Mahanand Hegde, Rita Singh, Bhiksha Raj
• 21st European Signal Processing Conference…
• 2013

The amount of online multimedia files is increasing day by day with the ever increasing popularity of video sharing websites. This has led to a huge interest in content analysis of multimedia files.… (More)

Features and Kernels for Audio Event Recognition

• Anurag Kumar, Bhiksha Raj
• ArXiv
• 2016

One of the most important problems in audio event detection research is absence of benchmark results for comparison wit h any proposed method. Different works consider different se ts of events and… (More)

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