Several speaker recognition algorithms failed to get the best results because of the wildly
varying datasets and feature sets for classification. Gender information helps reduce this …

Deep learning can be used for audio signal classification in a variety of ways. It can be used
to detect and classify various types of audio signals such as speech, music, and …

There are many sounds all around us and our brain can easily and clearly identify them.
Furthermore, our brain processes the received sound signals continuously and provides us …

For acoustic event recognition (AER), it is important to extract the semantic feature that
considers both the content information and the temporal ordering. To this end, our previous …

In this paper, a novel audio finger methodology for audio classification is proposed. The
fingerprint of the audio signal is a unique digest to identify the signal. The proposed model …

Recently, convolutional neural networks (CNN) have achieved the state-of-the-art
performance in acoustic scene classification (ASC) task. The audio data is often transformed …

In the important and challenging field of environmental sound classification (ESC), a crucial
and even decisive factor is the feature representation ability, which can directly affect the …

The principal method for screening and diagnosing lung disorders is auscultation of
respiratory sounds. In the medical field, automated auscultation is a new research field that …

Abstract Although Convolutional Neural Networks (CNNs) architecture based learning
systems have shown impressive results in the performance of numerous classification tasks …

In acoustic event recognition (AER), it is important to extract semantic features. As two
crucial aspects of semantic features, the essential content and the temporal structure can …