Conservationists are increasingly using autonomous acoustic recorders to determine the
presence/absence and the abundance of bird species. Unlike humans, these recorders can …

Despite surveillance systems becoming increasingly ubiquitous in our living environment,
automated surveillance, currently based on video sensory modality and machine …

Predicting all applicable labels for a given image is known as multi-label classification.
Compared to the standard multi-class case (where each image has only one label), it is …

This book is concerned with the classification of multilabeled data and other tasks related to
that subject. The goal of this chapter is to formally introduce the problem, as well as to give a …

Multi-label classification (MLC) has recently attracted increasing interest in the machine
learning community. Several studies provide surveys of methods and datasets for MLC, and …

For intelligent systems to make best use of the audio modality, it is important that they can
recognize not just speech and music, which have been researched as specific tasks, but …

Sound event detection (SED) methods are tasked with labeling segments of audio
recordings by the presence of active sound sources. SED is typically posed as a supervised …

Automatic species classification of birds from their sound is a computational tool of
increasing importance in ecology, conservation monitoring and vocal communication …

Partial label learning refers to the issue that each training sample corresponds to a
candidate label set containing only one valid label. Feature selection can be viewed as an …

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 …