Endowing machines with sensing capabilities similar to those of humans is a prevalent
quest in engineering and computer science. In the pursuit of making computers sense their …

This paper presents a comprehensive overview of current deep-learning methods for
automatic object classification of underwater sonar data for shoreline surveillance …

Features, including one or more acoustic features, visual features, linguistic features, and
physical features may be extracted from signals obtained by one or more sensors with a …

Despite the fact that several sites have reported the effectiveness of convolutional neural
networks (CNNs) on some tasks, there is no deep analysis regarding why CNNs perform …

In many speech signal processing applications, voice activity detection (VAD) plays an
essential role for separating an audio stream into time intervals that contain speech activity …

In an attempt to increase the robustness of automatic speech recognition (ASR) systems, a
feature extraction scheme is proposed that takes spectro-temporal modulation frequencies …

Two new approaches to the feature extraction process for automatic stress and emotion
classification in speech are proposed and examined. The first method uses the empirical …

To test if simultaneous spectral and temporal processing is required to extract robust
features for automatic speech recognition (ASR), the robust spectro-temporal two …

This paper presents a new feature extraction technique for speaker recognition using Radon
transform (RT) and discrete cosine transform (DCT). The spectrogram is compact, efficient in …

In image segmentation and classification tasks, utilizing filters based on the target object
improves performance and requires less training data. We use the Gabor filter as …