The lack of strongly labeled data can limit the potential of a Sound Event Detection (SED) system trained using deep learning approaches. To address this issue, this paper proposes a novel method to approximate strong labels for the weakly labeled data using Nonnegative Matrix Factorization (NMF) in a supervised manner. Using a combinative transfer learning and semi-supervised learning framework, two different Convolutional Neural Networks (CNN) are trained using synthetic data, approximated strongly labeled data, and unlabeled data where one model will produce the audio tags. In contrast, the other will produce the frame-level prediction. The proposed methodology is then evaluated on three different subsets of the Detection and Classificat...
The objective of this thesis is to develop novel classification and feature learning techniques for t...
We applied various architectures of deep neural networks for sound event detection and compared thei...
Everyday environments are overflowed with a wide variety of acoustic events, either produced by huma...
Sound event detection (SED) is a task to detect sound events in an audio recording. One challenge of...
In this paper, we present a method called HODGEPODGE\footnotemark[1] for large-scale detection of so...
In this technique report, we present a bunch of methods for the task 4 of Detection and Classificati...
Weakly labeled sound event detection (WSED) is an important task as it can facilitate the data colle...
This paper details our approach to Task 3 of the DCASE’19 Challenge, namely sound event localization...
In this paper, we present a gated convolutional neural network and a temporal attention-based local...
In this paper, we describe our system for the Task 2 of Detection and Classification of Acoustic Sce...
Sound event detection (SED) aims to detect when and recognize what sound events happen in an audio c...
Each edition of the challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) ...
Sound event detection (SED) is a problem to detect the onset and offset times of sound events in an ...
International audienceEach edition of the challenge on Detection and Classification of Acoustic Scen...
Sound event detection in real-world environments suffers from the interference of non-stationary and...
The objective of this thesis is to develop novel classification and feature learning techniques for t...
We applied various architectures of deep neural networks for sound event detection and compared thei...
Everyday environments are overflowed with a wide variety of acoustic events, either produced by huma...
Sound event detection (SED) is a task to detect sound events in an audio recording. One challenge of...
In this paper, we present a method called HODGEPODGE\footnotemark[1] for large-scale detection of so...
In this technique report, we present a bunch of methods for the task 4 of Detection and Classificati...
Weakly labeled sound event detection (WSED) is an important task as it can facilitate the data colle...
This paper details our approach to Task 3 of the DCASE’19 Challenge, namely sound event localization...
In this paper, we present a gated convolutional neural network and a temporal attention-based local...
In this paper, we describe our system for the Task 2 of Detection and Classification of Acoustic Sce...
Sound event detection (SED) aims to detect when and recognize what sound events happen in an audio c...
Each edition of the challenge on Detection and Classification of Acoustic Scenes and Events (DCASE) ...
Sound event detection (SED) is a problem to detect the onset and offset times of sound events in an ...
International audienceEach edition of the challenge on Detection and Classification of Acoustic Scen...
Sound event detection in real-world environments suffers from the interference of non-stationary and...
The objective of this thesis is to develop novel classification and feature learning techniques for t...
We applied various architectures of deep neural networks for sound event detection and compared thei...
Everyday environments are overflowed with a wide variety of acoustic events, either produced by huma...