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Sound reception was investigated in the Yangtze finless porpoise (Neophocaena phocaenoides asiaeorientalis) at its most sensitive frequency. The computed tomography scanning, sound speed, and density results were used to develop a three-dimensional numerical model of the porpoise sound-reception system. The acoustic fields showed that sounds can reach the ear complexes from various pathways, with distinct receptivity peaks on the forward, left, and right sides. Reception peaks were identified on the ipsilateral sides of the respective ears and found on the opposite side of the ear complexes. These opposite maxima corresponded to subsidiary hearing pathways in the whole head, especially the lower head, suggesting the complexity of the sound-reception mechanism in the porpoise. The main and subsidiary sound-reception pathways likely render the whole head a spatial receptor. The low-speed and -density mandibular fats, compared to other acoustic structures, are significant energy enhancers for strengthening forward sound reception. Based on the porpoise reception model, a biomimetic receptor was developed to achieve directional reception, and in parallel to the mandibular fats, the silicon material of low speed and density can significantly improve forward reception. This bioinspired and biomimetic model can bridge the gap between animal sonar and artificial sound control systems, which presents potential to be exploited in manmade sonar. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Yangtze finless porpoises (Neophocaena asiaeorientalis) are known to use the narrowband signals for echolocation. In this study, a finite-element model was configured based on computed tomography imaging technique and tissue physical properties measurement to simulate biosonar signal emission and transmission processes through animal’s head. The roles of the main structures in the head such as the air sacs, melon, bony structures, connective tissue, blubber, mandibular fat on the biosonar beam formation were investigated, and the relative importance of these structures was compared. The biosonar beam properties of this neonate porpoise were compared with those of adult ones. The method in this paper suggested an effective way for investigating the acoustic processes in the heads of the neonate odontocetes. [ABSTRACT FROM AUTHOR]