The problem of poor real-time capability, a large number of traversals and the long operation time of the MUSIC algorithm bring great difficulties to the application of DOA estimation. For the above problems, this paper fuses MUSIC algorithm and genetic algorithm and proposes an improved genetic MUSIC algorithm (GA-MUSIC). Also, improvement strategies are proposed for the three operators of selection, crossover, and variation of the genetic algorithm. The GA-MUSIC was simulated for DOA estimation to verify the effectiveness of this algorithm. The research shows that the GA-MUSIC proposed in this paper has the best operational performance at different precisions and SNRs. This algorithm can effectively reduce the iterative computation of the algorithm with better real-time performance, and the search success rate is above 95% while ensuring accuracy. Furthermore, this paper corroborates that the proposed improved genetic MUSIC algorithm can achieve better performance in single signals and incoherent multi-signal sources. It can be seen that the algorithm in this paper can effectively solve the problems of low success rates of DOA estimation searches and poor real-time performance and has a significant reference value for practical engineering applications.

In modern underwater warfare, detecting and locating enemy submarines is the primary task, but there are still many difficulties in detecting submarines. The acoustic signals may distort in the ocean channel due to the complexity of the ocean, which affects the detection of sonar. That is to say, the impact of environmental factors needs to be considered in submarine detection. Therefore, this article proposes an underwater collaborative detection method based on complex marine environments, constructs a sonar underwater detection capability analysis system to improve detection performance, analyzes the impact of marine environmental factors on sonar detection capability, evaluates sonar performance using sonar quality factor, calculates sonar detection distance, establishes polar coordinates, and explores sonar detection capability; Besides, the concept of collaborative detection is also introduced, and a collaborative detection probability distribution surface is proposed to fuse the detection capabilities of different platforms, presenting the collaborative detection capabilities of multiple platforms in the form of probability contour lines. The detection performance of different deployment methods is explored by changing the placement position and number of platforms. At last, the research has shown that the underwater detection capability of sonar is closely related to the marine environment. The square layout in underwater collaborative detection has better detection performance. The research results of this article can provide references for the layout of platforms in underwater detection tasks.

With the development and widespread application of voice interaction technology, it has become crucial to improve the accuracy of blind source separation technology. In order to further enhance the separation results of vocal and accompaniment, this paper proposes an improved Wave-U-Net model. Based on the skip connection of the Wave-U-Net model, we propose a segmented attention module (SAM) consisting of a spatial attention module (SPAM) and a channel attention module (CAM) to replace the skip connections in this model to solve the semantic gap caused by feature concatenation. Furthermore, we replace the 1D convolution layer of the bottleneck layer in this model with an atrous spatial pyramid pooling (ASPP) module. The purpose is to increase the receptive field and obtain multi-scale features at the same time, thereby improving the speech separation performance of the model. We conduct experimental tests in the Musdb18 dataset, and analyze the performance of the model using the SDR, SIR and SAR evaluation indicators. The research results denote that compared with the Wave-U-Net network that only uses feature concatenation, the SDR values of the restored vocal and restored accompaniment are increased by 4.229dB and 4.626dB, respectively, and the separation performance is better than some existing baseline models.

The speech enhancement effect of traditional deep learning algorithms is not ideal under low signal-to-noise ratios (SNR). Skip connections-deep neural network (Skip-DNN) improves the traditional deep neural network (DNN) by adding skip connections between each layer of the neural network to solve the degradation problem of DNN. In this paper, the Multiresolution Cochleagram (MRCG) features in the gammachirp transform domain are denoised to obtain the improved MRCG (I-MRCG). The noise reduction method adopts the Minimum Mean-Square Error Short-Time Spectral Amplitude Estimator (MMSE-STSA) and takes I-MRCG as the input feature and Skip-DNN as the training network to improve the speech enhancement effect of the model. This paper also proposes an improved source-to-distortion ratio (SDR) loss function. When the loss function uses the improved SDR, it will improve the performance of Skip-DNN speech enhancement model. The experiments in this paper are performed on the Edinburgh dataset. When using I-MRCG as the input feature of Skip-DNN, the average perceptual evaluation of speech quality (PESQ) is 2.9137, and the average short-time objective intelligibility (STOI) is 0.8515. Compared with MRCG as Skip-DNN input features, the improvements are 0.91% and 0.71%, respectively. When the improved SDR is used as the loss function of the speech model, the average PESQ is 2.9699 and the average STOI is 0.8547. Compared with other loss functions, the improved SDR has a better enhancement effect when used as the loss function of the speech enhancement model.

In order to reveal the relationship between steam turbine operating state and energy characteristics, the energy characteristics and frequency band distribution of steam turbine tail pipe pressure pulsation acquisition are studied. Wavelet analysis is used to remove the noise of steam turbine pressure pulsation signal, the noiseless signal is transformed by short‐time Fourier transform, and the energy histogram and energy proportion diagram of each frequency band are given. The traditional image continuation method is improved, the cubic Hermite interpolation method is used to replace the traditional cubic spline interpolation method, and then the traditional Hilbert–Huang transform (HHT) method is improved, which overcomes the endpoint effect problems, and reduces the overshoot and undershoot phenomenon in curve fitting to a certain extent. Using wavelet transform, empirical mode decomposition and improved HHT to study the distribution diagram, energy proportion diagram, and frequency band energy of pressure pulsation signal with frequency and time. The results show that with the increase of internal cavitation, the energy ratio in low‐frequency band decreases, the signal energy in high‐frequency band increases, the spectrum widens, and tends to the state of continuous spectrum.