… Then, we concatenate the SMel model with a state–of–the–art CNN for urban sound
event detection [13], to form the end–to–end architecture. A similar approach based on end–to–end …

… deep learning models: ANN, CNN, RNN, LSTM plus GRU … Urban Sound 8K dataset, which
consists of 8,000 urban sound … Convolutional neural network (CNN) for image detection and …

… As seen in Figure 11, the architecture of a CRNN is straightforward; simply stack a CNN
over an RNN so that features map extracted by the CNN can be passed directly to the RNN. …

… recordings with a convolutional recurrent neural network. … and normal lung sounds), and
event detection at recording level… it with a CNN front-end to obtain the convolutional bidirectional …

… convolutional recurrent neural network for detecting sound event, … (CNN) is linked as the
front-end of recurrent neural network (… RNN, like CNN, is a highly effective neural network that is …

… (CNN) [14-15], recurrent neural networks (RNN) [16], residual network (ResNet) [17]. Most
recently, combination of the CNN, RNN … combined with RNN is used for both sound events …

… We use spectrogram with a CNN-RNN model as our baseline, which on comparison to our
other proposed methods, performs significantly lower in terms of both specificity and sensitivity…

… results of the proposed CNN-RNN approach for dangerous urban sound detection problems.
Firstly, the evaluation metrics to assess the proposed CNN-RNN deep learning model are …

… [15] have shown that MFCCs work well and are especially useful for encapsulating features
that are similar to how our ears respond to sound frequencies. These properties make …

… technique for wheezes detection in respiratory sounds using a … with a convolutional neural
network (CNN) to form a hybrid … respiratory sounds classification model, which assigns sound …