Denoising odontocete echolocation clicks using a hybrid model with convolutional neural network and long short-term memory network.

Ocean noise negatively influences the recording of odontocete echolocation clicks. In this study, a hybrid model based on the convolutional neural network (CNN) and long short-term memory (LSTM) network-called a hybrid CNN-LSTM model-was proposed to denoise echolocation clicks. To learn the model parameters, the echolocation clicks were partially corrupted by adding ocean noise, and the model was trained to recover the original echolocation clicks. It can be difficult to collect large numbers of echolocation clicks free of ambient sea noise for training networks. Data augmentation and transfer learning were employed to address this problem. Based on Gabor functions, simulated echolocation clicks were generated to pre-train the network models, and the parameters of the networks were then fine-tuned using odontocete echolocation clicks. Finally, the performance of the proposed model was evaluated using synthetic data. The experimental results demonstrated the effectiveness of the proposed model for denoising two typical echolocation clicks-namely, narrowband high-frequency and broadband echolocation clicks. The denoising performance of hybrid models with the different number of convolution and LSTM layers was evaluated. Consequently, hybrid models with one convolutional layer and multiple LSTM layers are recommended, which can be adopted for denoising both types of echolocation clicks.

Transfer learning for denoising the echolocation clicks of finless porpoise (Neophocaena phocaenoides sunameri) using deep convolutional autoencoders.

Ocean noise has a negative impact on the acoustic recordings of odontocetes' echolocation clicks. In this study, deep convolutional autoencoders (DCAEs) are presented to denoise the echolocation clicks of the finless porpoise (Neophocaena phocaenoides sunameri). A DCAE consists of an encoder network and a decoder network. The encoder network is composed of convolutional layers and fully connected layers, whereas the decoder network consists of fully connected layers and transposed convolutional layers. The training scheme of the denoising autoencoder was applied to learn the DCAE parameters. In addition, transfer learning was employed to address the difficulty in collecting a large number of echolocation clicks that are free of ambient sea noise. Gabor functions were used to generate simulated clicks to pretrain the DCAEs; subsequently, the parameters of the DCAEs were fine-tuned using the echolocation clicks of the finless porpoise. The experimental results showed that a DCAE pretrained with simulated clicks achieved better denoising results than a DCAE trained only with echolocation clicks. Moreover, deep fully convolutional autoencoders, which are special DCAEs that do not contain fully connected layers, generally achieved better performance than the DCAEs that contain fully connected layers.

Classification of odontocete echolocation clicks using convolutional neural network.

A method based on a convolutional neural network for the automatic classification of odontocete echolocation clicks is presented. The proposed convolutional neural network comprises six layers: three one-dimensional convolutional layers, two fully connected layers, and a softmax classification layer. Rectified linear units were chosen as the activation function for each convolutional layer. The input to the first convolutional layer is the raw time signal of an echolocation click. Species prediction was performed for groups of m clicks, and two strategies for species label prediction were explored: the majority vote and maximum posterior. Two datasets were used to evaluate the classification performance of the proposed algorithm. Experiments showed that the convolutional neural network can model odontocete species from the raw time signal of echolocation clicks. With the increase in m, the classification accuracy of the proposed method improved. The proposed method can be employed in passive acoustic monitoring to classify different delphinid species and facilitate future studies on odontocetes.

Convolutional neural network for detecting odontocete echolocation clicks.

In this work, a convolutional neural network based method is proposed to automatically detect odontocetes echolocation clicks by analyzing acoustic data recordings from a passive acoustic monitoring system. The neural network was trained to distinguish between click and non-click clips and was subsequently converted to a full-convolutional network. The performance of the proposed network was evaluated using synthetic data and real audio recordings. The experimental results indicate that the proposed method works stably with echolocation clicks of different species.

Three-dimensional analytical solution for transient guided wave propagation in liquid-filled pipe systems.

The objective of this study is to investigate the three-dimensional (3-D) analytical solution for transient guided wave propagation in liquid-filled pipe systems using the eigenfunction expansion method (EEM). The eigenfunctions corresponding to finite liquid-filled pipe systems with a traction-free lateral boundary and rigid smooth end boundaries are obtained. Additionally, the orthogonality of the eigenfunctions is proved in detail. Subsequently, the exact 3-D analytical transient response of finite liquid-filled pipe systems to external body forces is constructed using the EEM, based on which, the approximate 3-D analytical transient response of the systems to external surface forces is derived. Furthermore, the analytical solution for transient guided wave propagation in finite liquid-filled pipe systems is extended explicitly and concisely to infinite liquid-filled pipe systems. Several numerical examples are given to illustrate the analysis of the spatial and frequency distributions of the radial and axial displacement amplitudes of various guided wave modes; the numerical examples also simulate the transient displacement of the pipe wall and the transient pressure of the internal liquid from the present solution. The present solution can provide some theoretical guidelines for the guided wave nondestructive evaluation of liquid-filled pipes and the guided wave technique for downhole data transfer.