Acoustic scene classification (ASC) has gained significant interest recently due to its diverse
applications. Various audio signal processing and machine learning methods have been …

The number of publications on acoustic scene classification (ASC) in environmental audio
recordings has constantly increased over the last few years. This was mainly stimulated by …

The automatic detection of an emotional state from human speech, which plays a crucial role
in the area of human-machine interaction, has consistently been shown to be a difficult task …

Spectrogram representations of acoustic scenes have achieved competitive performance for
acoustic scene classification. Yet, the spectrogram alone does not take into account a …

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 …

Machine learning based heart sound classification represents an efficient technology that
can help reduce the burden of manual auscultation through the automatic detection of …

Obstructive sleep apnea (OSA) is a common chronic sleep disorder that disrupts breathing
during sleep and is associated with many other medical conditions, including hypertension …

The goal of Acoustic Scene Classification (ASC) is to recognise the environment in which an
audio waveform has been recorded. Recently, deep neural networks have been applied to …

There are lots of research papers for ASC, and in recent years it is rapidly increasing.
DCASE also provides different types of competition for the submission of several papers to …

Acoustic Scene Classification aims to recognize the unique acoustic characteristics of an
environment. Recently, Convolutional Neural Networks (CNNs) have boosted the accuracy …