N Takahashi, M Gygli… - IEEE Transactions on …, 2017 - ieeexplore.ieee.org
We propose a new deep network for audio event recognition, called AENet. In contrast to speech, sounds coming from audio events may be produced by a wide variety of sources …
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 …
Audio event recognition (AER) has been an important research problem with a wide range of applications. However, it is very challenging to develop large scale audio event …
A Kumar, M Khadkevich… - 2018 IEEE International …, 2018 - ieeexplore.ieee.org
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 …
Deep neural networks are a promising tool for Audio Event Classification. In contrast to other data like natural images, there are many sensible and non-obvious representations for …
H Phan, L Hertel, M Maass, A Mertins - arXiv preprint arXiv:1604.06338, 2016 - arxiv.org
We present in this paper a simple, yet efficient convolutional neural network (CNN) architecture for robust audio event recognition. Opposing to deep CNN architectures with …
The Sound Event Classification (SEC) task involves recognizing the set of active sound events in an audio recording. The Sound Event Detection (SED) task involves, in addition to …
As sound event classification moves towards larger datasets, issues of label noise become inevitable. Web sites can supply large volumes of user-contributed audio and metadata, but …
To reveal the importance of temporal precision in ground truth audio event labels, we collected precise (∼ 0.1 sec resolution)" strong" labels for a portion of the AudioSet dataset …