Localization of Flying Bats from Multichannel Audio Signals by Estimating Location Map with Convolutional Neural Networks

We propose a method that uses ultrasound audio signals from a multichannel microphone array to estimate the positions of flying bats. The proposed model uses a deep convolutional neural network that takes multichannel signals as input and outputs the probability maps of the locations of bats. We present experimental results using two ultrasound audio clips of different bat species and show numerical simulations with synthetically generated sounds.

An integrated method based on refined composite multivariate hierarchical permutation entropy and random forest and its application in rotating machinery

Fault feature extraction of rotating machinery is crucial and challenging due to its nonlinear and nonstationary characteristics. In order to resolve this difficulty, a quality nonlinear fault feature extraction method is required. Hierarchical permutation entropy has been proven to be a promising nonlinear feature extraction method for fault diagnosis of rotating machinery. Compared with multiscale permutation entropy, hierarchical permutation entropy considers the fault information hidden in both high frequency and low frequency components. However, hierarchical permutation entropy still has some shortcomings, such as poor statistical stability for short time series and inability of analyzing multichannel signals. To address such disadvantages, this paper proposes a new entropy method, called refined composite multivariate hierarchical permutation entropy. Refined composite multivariate hierarchical permutation entropy can extract rich fault information hidden in multichannel signals synchronously. Based on refined composite multivariate hierarchical permutation entropy and random forest, a novel fault diagnosis framework is proposed in this paper. The effectiveness of the proposed method is validated using experimental and simulated signals. The results demonstrate that the proposed method outperforms multivariate multiscale fuzzy entropy, refined composite multivariate multiscale fuzzy entropy, multivariate multiscale sample entropy, multivariate multiscale permutation entropy, multivariate hierarchical permutation entropy, and composite multivariate hierarchical permutation entropy in recognizing the different faults of rotating machinery.

Open-Lake Experimental Investigation of Azimuth Angle Estimation Using a Single Acoustic Vector Sensor

Five well-known azimuth angle estimation methods using a single acoustic vector sensor (AVS) are investigated in open-lake experiments. A single AVS can measure both the acoustic pressure and acoustic particle velocity at a signal point in space and output multichannel signals. The azimuth angle of one source can be estimated by using a single AVS in a passive sonar system. Open-lake experiments are carried out to evaluate how these different techniques perform in estimating azimuth angle of a source. The AVS that was applied in these open-lake experiments is a two-dimensional accelerometer structure sensor. It consists of two identical uniaxial velocity sensors in orthogonal orientations, plus a pressure sensor—all in spatial collocation. These experimental results indicate that all these methods can effectively realize the azimuth angle estimation using only one AVS. The results presented in this paper reveal that AVS can be applied in a wider range of application in distributed underwater acoustic systems for passive detection, localization, classification, and so on.