Sound event detection (SED) has been widely applied in different applications such as
smart home, video surveillance, environmental monitoring. The SED models which are …

Audio event detection (AED) systems can leverage the power of specialized algorithms for
detecting the presence of a specific sound of interest within audio captured from the …

Sound event detection (SED) aims to detect temporal boundaries of sound events from
acoustic recordings. Sound events in real-life recordings often overlap with each other (ie …

In recent years, the application of deep learning to environmental sound classification (ESC)
has received considerable attention owing to its powerful ability to recognize the context of …

This paper presents a review of anomalous sound event detection (SED) approaches. SED
is becoming more applicable for real-world appliactaions such as security, fire determination …

The lack of strongly labeled data can limit the potential of a Sound Event Detection (SED)
system trained using deep learning approaches. To address this issue, this paper proposes …

Sound events often occur in unstructured environments where they exhibit wide variations in
their frequency content and temporal structure. Convolutional neural networks (CNNs) are …

Sound event detection (SED) aims to identify the types and the temporal boundaries of the
target sound events. Successful detection of various sound events can reveal rich …

Real-time automatic identification of audio distress signals in urban areas is a task that in a
smart city can improve response times in emergency alert systems. The main challenge in …

In this paper, we introduce our sound events detection system using a mean-teacher model
with convolutional recurrent neural network (CRNN) for DCASE 2020 Task4, which include …