Cardiac sounds are produced by the mechanical activities of the heart and provide useful information about the function of the heart valves. Due to the transient and unstable nature of the heart's sound and the limitation of the human hearing system, it is difficult to categorize heart sound signals based on what is heard from a stethoscope. Therefore, providing an automated algorithm for primary diagnosis of heart disease by analytic use of heart sound signals is very valuable. In this paper, an automated method for classifying cardiac sounds using signals recorded from a phonocardiogram is presented. In the proposed method, the Mel frequency cepstral coefficients along with wavelet-based features are extracted from the heart sound signals. In the next step, the most informative features are selected using the Sequential Forward Floating Search (SFFS) algorithm. Finally, the selected feature set is fed into the classifier, support vector machines, to classify heart sounds. The performance of the proposed method was evaluated using a public dataset presented by the organizers of the the PhysioNet/CinC Challenge 2016. The proposed method provided an average MAcc of 88.15%, an average sensitivity of 92.74% and an average specificity of 83.56% in the classification of cardiac sounds. The results show that the proposed method has better performance than the best available methods and is a suitable tool in the analysis of heart sounds.

Sound-related behavioural studies explore the effects of sound on underwater animals in tanks under controlled laboratory conditions. In this study, we aim to assess sound pressure distributions and gradients in a small-sized water-filled tank that can be used in behavioural studies to examine anthropogenic sound effects on fish and invertebrates and to raise awareness among scientists in the field of underwater acoustics. To assess sound pressure, a water-filled fish tank was used with dimensions of 35*30*25cm; water depth 20cm. The playedback sound was a white noise file with continuous temporal pattern (200-4000 Hz). The results of the sound pressure gradient in the fish tank showed that in the side wall near the speaker location, there was a sharp sound pressure gradient. Moreover, near all the walls of the fish tank, despite moving away from the sound source, the walls of the fish tank played a role as a secondary source of sound and induced elevation of the sound pressure levels. Oscillation of sound levels and the lack of a clear gradient of sound in the longitudinal direction in the fish tank may be due to the presence of standing waves and related to the dimensions of the fish tank. It is essential to assess the acoustic field in any tank in which sound playbacks and bioacoustics are to be carried out.

Direction-of-arrival (DOA) estimation of audio signals is critical in different areas, including electronic war, sonar, etc. The beamforming methods like Minimum Variance Distortionless Response (MVDR), Delay-and-Sum (DAS), and subspace-based Multiple Signal Classification (MUSIC) are the most known DOA estimation techniques. The mentioned methods have high computational complexity. Hence using the algorithms with high computational complexity in the real-time DOA estimation applications is a serious challenge. On the other hand, the main characteristic of the methods is their high potential for parallelization. The objective of this paper is a parallel implementation of the considered algorithms using a Graphics Processing Unit (GPU) instead of a Central Processing Unit (CPU) for increasing execution speed and real-time implementation of the mentioned algorithms. To this aim, the Kuda programming model is used to implement the algorithm on a GPU. This algorithm is also implemented serially in MATLAB to investigate the parallel implementation performance. The results show that parallel implementation of these algorithms can increase the program execution time ten times more than serial implementation. Accuracies of different implementations are validated using simulations by MATLAB and Kuda.

Commercial quadcopters with many private, commercial, and public sector applications are a rapidly advancing technology. Currently, there is no guarantee to facilitate the safe operation of these devices in the community. Three different automatic commercial quadcopters identification methods are presented in this paper. Among these three techniques, two are based on deep neural networks in which all the feature extraction and classification processes are performed automatically. Deep learning-based methods include the convolutional neural network (CNN), LTSM networks, and a combination of those. The third method is presented using cepstral coefficients and support vector machines. In deep learning-based algorithms, the spectral patterns extracted from the commercial quadcopters' sounds are used as input data. The spectral patterns are obtained by applying the short-time Fourier transform method to the acoustic data. Besides, the cepstral coefficients and the support vector machines are used in the third method to identify and classify the received acoustic signals. The performance of the deep learning and cepstrum coefficients-based methods are compared using the acoustic datasets recorded from the commercial quadcopters. The results show that all three presented methods have adequate performance in identifying the quadcopters. However, the LSTM-CNN method had the best performance by providing an average accuracy of 95.31%, average sensitivity of 96.24%, and average specificity of 95.61%.

In this article, the eigenvalues of the sound equation are used to determine the refractive index. This refractive index helps to extract the acoustic components of the material such as the speed of sound in the material. This will help in identifying targets, especially in the field of signal processing. For this purpose, a method has been extracted that can be used to establish a relation between the eigenvalues and refractive index, which is the main goal of this article. In order to find this relation, it is necessary to obtain appropriate basis vectors for the space of problem, so that the eigenvectors can be expanded in terms of these bases. Therefore, in this article, the eigenvectors of the fourth-order elliptic operator are used as independent bases for the Sobolov space, with the help of which the nonlinear eigenvalue problem becomes a matrix relation that can be easily implemented. Finally, the proposed method is applied to several objects with different refractive indices and the results are compared with the dual space method, which shows that the proposed method has less error in reconstructing the refractive index. As the refractive index value increases, the error is also reduced.

Classrooms, as one of the most important educational environments, play a major role in the learning and academic progress of students. reverberation time, as one of the most important acoustic parameters inside rooms, has a significant effect on sound quality. The inefficiency of classical formulas such as Sabin, caused this article to examine the use of machine learning methods as an alternative method for predicting the environment's reverberation time. In this research, firstly, by using methods based on geometrical acoustics and by using Odeon software, the collection of required data sets at frequencies of 500 and 2000 Hz is done. In this dataset, 4 classrooms with a rectangular space, along with elements such as desks and chairs, windows, and doors, were used. After that, to provide a system based on machine learning, multilayer perceptron neural network and neural network based on radial basis functions along with K-means clustering algorithm and also convolutional neural network has been used. These models consider the characteristics of the environment and finally estimate the values of reverberation time as a function of frequency. In this research, by using the multi-layer perceptron neural network, the determination coefficient was 93% for the frequency of 500 Hz and 95% for the frequency of 2000 Hz. Also, by using the neural network based on radial basis functions, for the frequency of 500 Hz, the coefficient of determination was 82% and for the frequency of 2000 Hz, the coefficient of determination was recorded as 89%. Also, by using a one-dimensional convolutional neural network, a determination coefficient of 94% was recorded for the frequency of 500 Hz, and a determination coefficient of 96% for the frequency of 2000 Hz.

In this work the structural phase stability and phononic properties of sodium hydroxide compound have been reported. The calculations have been performed using the pseudo potential method with plane wave based on density functional theory (DFT). Local density approximation (LDA) and generalized gradient approximation (GGA) have been used for modeling the exchange-correlation potential. Negative frequencies have been observed to indicate system instability. The calculated results are in good accordance with existing theoretical and experimental data.

In this paper, the phononic structure and Enthalpy of CaB2 compound in simple hexagonal and orthorhombic phase have been investigated. The calculations were performed using the pseudo-potential method in the framework of the density functional theory and using the Quantum-Espresso code. Using the group theory and the characteristic table of the composition point group, the phonon modes were identified in terms of symmetry species. Phonon modes are obtained at points with high symmetry in both phases, which are all positive, so they do not show structural instability. Also, it can be seen that the frequency of acoustic phonons becomes zero when their energy tends to zero, while optical phonons have a repulsive frequency of zero and are almost without scattering. In addition, the enthalpy diagram in two phases shows the heat absorbed by the system. The obtained results are in good accordance with other available data.

Open cell aluminum foams have good acoustic dampening properties. In this research, in order to improve the sound damping capacity of these foams, 0.9, 1.5 and 3wt% carbon nanotubes were added to the aluminum foam. Foams were produced by the space holder method in powder metallurgy. The results showed that Al-0.9wt%CNT nanocomposite foam had the highest average sound transmission loss in the frequency range of 500-2048 Hz with 43.11 dB, which compared to the average sound transmission loss of pure aluminum with 18.35 dB, has improved by about 135%. Also, this sample was compared with rockwool with a density of 120 kg/m3, which is generally the best choice in the sound insulation industry. In addition to reducing the thickness, the Al-0.9wt%CNT nanocomposite foam has improved the average loss of sound transmission by more than 157%.