Automatic classification of environmental sounds, such as dog barking and glass breaking,
is becoming increasingly interesting, especially for mobile devices. Most mobile devices …

This paper presents DCASE 2018 task 4. The task evaluates systems for the large-scale
detection of sound events using weakly labeled data (without time boundaries). The target of …

With the development of multi-modal man-machine interaction, audio signal analysis is
gaining importance in a field traditionally dominated by video. In particular, anomalous …

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 …

Persons with hearing loss use visual signals such as gestures and lip movement to interpret
speech. While hearing aids and cochlear implants can improve sound recognition, they …

A typical semi-supervised learning-based scheme is based on training a single model for
labeled data. For unlabeled data, it uses the pseudo-labeling method to obtain labels …

This paper proposes an overview of the latest advances and challenges in sound event
detection and classification with systems trained on partially annotated data. The paper fo …

Coping with scarcity of labeled data is a common problem in sound classification tasks.
Approaches for classifying sounds are commonly based on supervised learning algorithms …

We present in this paper our work on audio event classification for outdoor events. As the
main classification method we employ a deep neural network (DNN) and compare this to …

We propose a simple but efficient method termed Guided Learning for weakly-labeled semi-
supervised sound event detection (SED). There are two sub-targets implied in weakly …