Computational ecoacoustics has seen significant growth in recent decades, facilitated by the
reduced costs of digital sound recording devices and data storage. This progress has …

The popularity of bioacoustics for threatened species monitoring has surged. Large volumes
of acoustic data can be collected autonomously and remotely with minimal human effort. The …

Sound event detection (SED) in environmental recordings is a key topic of research in
machine listening, with applications in noise monitoring for smart cities, self-driving cars …

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 bird sound classification plays an important role in monitoring and further
protecting biodiversity. Recent advances in acoustic sensor networks and deep learning …

Large bioacoustic archives of wild animals are an important source to identify reappearing
communication patterns, which can then be related to recurring behavioral patterns to …

Supervised learning for audio classification typically imposes a fixed class vocabulary,
which can be limiting for real-world applications where the target class vocabulary is not …

The use of passive acoustic monitoring in wildlife ecology has increased dramatically in
recent years as researchers take advantage of improvements in autonomous recording units …

In the context of automatic speech recognition and acoustic event detection, an adaptive
procedure named per-channel energy normalization (PCEN) has recently shown to …

Audio segmentation and sound event detection are crucial topics in machine listening that
aim to detect acoustic classes and their respective boundaries. It is useful for audio-content …