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

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 …

Recent successful applications of convolutional neural networks (CNNs) to audio
classification and speech recognition have motivated the search for better input …

In the context of non-speech audio recognition and classification for multimedia applications,
it becomes essential to have a set of features able to accurately represent and discriminate …

Presents our entry to the Detection and Classification of Acoustic Scenes challenge. The
approach we propose for classifying acoustic scenes is based on transforming the audio …

Although research in audio recognition has traditionally focused on speech and music
signals, the problem of environmental sound recognition (ESR) has received more attention …

Today, a large number of audio features exists in audio retrieval for different purposes, such
as automatic speech recognition, music information retrieval, audio segmentation, and …

The “bag-of-frames” approach (BOF) to audio pattern recognition represents signals as the
long-term statistical distribution of their local spectral features. This approach has proved …

Monitoring of human and social activities is becoming increasingly pervasive in our living
environment for public security and safety applications. The recognition of suspicious events …

Background subtraction is a widely used operation in the video surveillance, aimed at
separating the expected scene (the background) from the unexpected entities (the …