Many studies have employed reinforcement learning (RL) techniques to successfully create
portfolio strategies in recent years. However, since financial markets are extremely noisy …

Audiovisual scene understanding is a challenging problem due to the unstructured spatial–
temporal relations that exist in the audio signals and spatial layouts of different objects in the …

Background and purpose One of the essential purposes of sound classification is to achieve
similar/over classification ability of the human auditory system (HAS). A new dataset and a …

Environmental sound classification (ESC) is an important research problem with a broad
range of applications including audio-based surveillance, audio-visual systems, smart …

One dimension convolutional neural networks (1D CNN) that directly take raw waveforms as
input has less competition than 2D CNN recognizing environmental sound. In order to …

Automatic modulation recognition (AMR) of radio signal is an important research topic in the
area of non-cooperative communication and cognitive radio. Recently deep learning (DL) …

Abstract Knowledge distillation as a broad class of methods has led to the development of
lightweight and memory efficient models, using a pre-trained model with a large capacity …

Environmental sound classification (ESC) is a challenging problem due to the unstructured
spatial-temporal relations that exist in the sound signals. Recently, many studies have …

With the intelligent development of industrial production, sound monitoring technology has
been widely used to monitor the operation status of mechanical facilities, and this progress …

Cough is a common symptom associated with respiratory diseases and its analysis plays a
crucial role in monitoring the health conditions of affected persons. Traditional cough …