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 large …

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 …

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 …

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 …