Automatic environmental sound classification (ESC) is one of the upcoming areas of
research as most of the traditional studies are focused on speech and music signals …

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

Audio pattern recognition is an important research topic in the machine learning area, and
includes several tasks such as audio tagging, acoustic scene classification, music …

Environmental sound classification (ESC) is an important and challenging problem. In
contrast to speech, sound events have noise-like nature and may be produced by a wide …

In this paper, we propose the use of spatial and harmonic features in combination with long
short term memory (LSTM) recurrent neural network (RNN) for automatic sound event …

Audio tagging is the task of predicting the presence or absence of sound classes within an
audio clip. Previous work in audio tagging focused on relatively small datasets limited to …

Underwater noise targets classification has variable applications in many fields. During the
long range detection, inevitable environmental noise will decrease the recognition accuracy …

Complexity of environmental sounds impose numerous challenges for their classification.
The performance of Environmental Sound Classification (ESC) depends greatly on how …

The last decade has seen increased interest in environmental sound classification (ESC)
due to the increased complexity and rich information of ambient sounds. The state-of-the-art …

The Detection and Classification of Acoustic Scenes and Events (DCASE) consists of five
audio classification and sound event detection tasks: 1) Acoustic scene classification, 2) …