In this research paper, we presented a novel approach to detect impulsive sounds in real-
time using a combination of Deep CNN and RNN architectures. The proposed approach …

Sound event detection (SED) assists in the detainment of intruders. In recent decades,
several SED methods such as support vector machine (SVM), K-Means clustering, principal …

The objective of a sound event detector is to recognize anomalies in an audio clip and return
their onset and offset. However, detecting sound events in noisy environments is a …

Most of the surveillance systems for public safety are solely based on one or more video
cameras. These camera systems have some drawbacks such that they have poor …

CNN+ RNN models have become the mainstream approach for semi-supervised sound
event detection, and the CNN part is mainly a stack of several 2D convolutional layers to …

Sound event detection is intended to analyze and recognize the sound events in audio
streams and it has widespread applications in real life. Recently, deep neural networks such …

Video analysis has become a standard feature of many security cameras. However, built-in
audio analytics continues to be quite rare despite the presence of both the audio channel …

Rare sound event detection is a newly proposed task in IEEE DCASE 2017 to identify the
presence of monophonic sound event that is classified as an emergency and to detect the …

Parking is a crucial element in urban mobility management. The availability of parking areas
makes it easier to use a service, determining its success. Proper parking management …

We propose using derivative features for sound event detection based on deep neural
networks. As input to the networks, we used log-mel-filterbank and its first and second …