… a large dataset of labeled impulsive sounds. However, there … impact the accuracy of the
detection, and high computational … CNN and RNN architectures for impulsive sound detection, …

… , a recurrent neural network (RNN) combined with CNN is … dataset to improve the environmental
sound classification. Batch … that the proposed CNN-RNN architecture achieves better …

… results of RNN with convolutional neural network (CNN) and also try to increase the reported
accuracy of the system using CNN … of vowel sounds of the patient with the different disease. …

… sound event detection task. We compare the performance of the proposed CRNN method
with CNN, RNN, … improvement for four different datasets consisting of everyday sound events. …

… In addition, by analyzing related works in sound detection systems [12], RNN has chosen …
In our series of experiments, four RNN models and one CNN model were trained during …

… (CNN). Additionally, we explore the combination of these models: (DNN, RNN and CNN). …
There are several studies using DL in sound event detection [4][5]. However, to the best of our …

… ) network, recurrent neural network (RNN), ordinary … sounds in high noise levels. Based on
supervised learning, the research has developed the novel CNN architecture for beep sound …

… This paper aims to perform anomaly detection on machine sound using various combinations
of CNN-RNN deep learning models. The Convolutional Neural Network (CNN) was …

… in longer sequences than a normal RNN, LSTM. In SED … CNN network, the network
combining CNN and RNN has stronger ability to process time information. Therefore, we use CNN…

… ), for drone sound detection. Our empirical results, which were … In summary, our RNN
models showed the best detection … the processing time for CNN and RNN without GPU usage. …