MUSIC Algorithm Research Articles

Spatial Filtering of Signals under Imprecise Calibration of Antenna Arrays

Introduction. Spatial filtering of signals is performed for the selection the signals of interest when the signals spectra overlap. The quality of spatial filtering depends on the accuracy of antenna array (AA) calibration, which allows estimation of the amplitude-phase distribution (APD) at all possible directions of arrival, thus ensuring the identity of reception paths. A mismatch between the actual and measured APD values leads to quality degradation in all spatial filtering methods.Aim. To develop a method for improving the quality of signal spatial filtering based on the estimates of the desired and interfering signal arrival directions formed by the MUSIC and ESPRIT algorithms under a priori uncertainty and imprecise AA calibration.Materials and methods. The quality of spatial filtering is improved by rejecting the interfering signals unsuppressed due to imprecisely measured APD of an AA. Statistical simulation modeling was carried out in the MATLAB environment; the data obtained experimentally were analyzed.Results. A method for spatial filtering based on MUSIC and ESPRIT completed with an additional rejection of unsuppressed interfering signals due to imprecise AA calibration is developed. An algorithm for basis construction for rejection under of a priori uncertainty of the signal-interference environment is substantiated. The results of statistical simulation modeling and experimental data processing have shown the feasibility of additional rejection applied to the selected signals by spatial filtering.Conclusion. The developed method for spatial filtering under the conditions of a priori uncertainty of the signal-interference situation and imprecise calibration of AA and reception paths ensures high quality characteristics across a wide dynamic range of desired and interfering signals. Whereas the Capon's method, which requires a priori knowledge of the arrival direction of the desired signal or its estimation, is capable of selecting only weak signals and suppressing strong ones under the conditions of imprecise APD.

Smart Decentralized Electric Vehicle Aggregators for Optimal Dispatch Technologies

The number of electric vehicles (EVs) is growing exponentially, which presents the power grid with new challenges to turn their reliance to renewable energy sources (RESs). Coordination between the available generations from RESs and the charging time should be managed to optimally utilize the available generation from RESs. The dispatch scheduling of EVs can significantly reduce the impact of these challenges on power systems. Three different technologies can be used to manage the dispatch of EV batteries which are unregulated charging (UC), unidirectional grid-to-vehicle (G2V), and bidirectional vehicle-to-grid (V2G) technologies. This study aims to address the primary reason for EV owners’ disbelief in the accuracy of battery wear models, which is impeding their involvement in V2G technology. This paper introduces a novel accurate EV battery wear model considering the instantaneous change in the operation of the EV battery. Moreover, an effective musical chairs algorithm (MCA) is used to reduce everyday expenses and increase revenue for V2G technologies in a short convergence time with accurate determination of optimal power dispatch scheduling. The results obtained from these three strategies are compared and discussed. The salient result from this comparison is that V2G technology increases wear and reduces the battery lifespan in comparison with the UC and G2V. The yearly expenses of G2V are reduced by 33% compared to the one associated with the UC. Moreover, the use of V2G technology provides each EV owner with USD 3244.4 net yearly profit after covering the charging and wear costs. The superior results extracted from the proposed model showed the supremacy of V2G usage, which is advantageous for both EV owners and the power grid.

Informatization Integration Strategy of Modern Vocal Music Teaching and Traditional Music Culture in Colleges and Universities in the Era of Artificial Intelligence

Abstract This paper utilizes deep learning algorithms to informally integrate modern vocal music teaching with traditional music culture and extracts audio time-domain features and frequency-domain features through neural network self-learning. Secondly, a large number of music tracks are decomposed into music patterns, which constitute a music pattern library, and a music training model is generated through the automatic music audio synthesis algorithm based on a recurrent neural network, and the GRU model is used for music training and model prediction. The strategy of integrating artificial intelligence and modern vocal music teaching mode through traditional music culture in modern vocal music teaching is informatized, and a controlled experiment is carried out with H Music Academy as an example. The results show that the average degree of completion of the learning objectives of the students in the two experimental classes is 89.32 and 87.16, respectively, which is 14.15 and 11.99 higher than the average degree of completion of the control class. This study demonstrates that the teaching mode of traditional music culture integration in modern vocal music teaching can enhance the student’s ability of vocal music skills and practically improve the students’ artistic literacy, which can improve the degree of completion of the student’s learning objectives and in turn, improve the overall level of vocal music teaching.

Multi-source underwater DOA estimation using PSO-BP neural network based on high-order cumulant optimization

Due to the complex and changeable environment under water, the performance of traditional DOA estimation algorithms based on mathematical model, such as MUSIC, ESPRIT, etc., degrades greatly or even some mistakes can be made because of the mismatch between algorithm model and actual environment model. In addition, the neural network has the ability of generalization and mapping, it can consider the noise, transmission channel inconsistency and other factors of the objective environment. Therefore, this paper utilizes Back Propagation (BP) neural network as the basic framework of underwater DOA estimation. Furthermore, in order to improve the performance of DOA estimation of BP neural network, the following three improvements are proposed. (1) Aiming at the problem that the weight and threshold of traditional BP neural network converge slowly and easily fall into the local optimal value in the iterative process, PSO-BP-NN based on optimized particle swarm optimization (PSO) algorithm is proposed. (2) The Higher-order cumulant of the received signal is utilized to establish the training model. (3) A BP neural network training method for arbitrary number of sources is proposed. Finally, the effectiveness of the proposed algorithm is proved by comparing with the state-of-the-art algorithms and MUSIC algorithm.

Research on super resolution angle measurement based on esprit algorithm

With the rapid development of communication, radar, sonar and many other fields, angle measurement has become a very important field in signal processing. Super resolution angle measurement, compared with the traditional angle measurement, has more advantages in accuracy, so it is widely used in practical production. Super resolution angle measurement includes MUSIC algorithm and ESPRIT algorithm. This paper firstly compares the MUSIC algorithm and ESPRIT algorithm, then elaborates the ESPRIT algorithm and deduces the process. In this paper, DOA estimation based on ESPRIT algorithm is carried out for three different number of input signal sources, and the errors are analysed according to different SNR (signal to noise ratio) values. Finally, based on the simulation results, the effectiveness of ESPRIT algorithm is evaluated.

AI Generated Music

In last few years we noticed that the usage of artificial intelligence growing very fast which is implemented in the photography, videography, computer vision and many Moe topics which we can’t explain in this paper but the point is AI is used everywhere. AI music generation is on of them in which we generate music through the AI with the help of machine learning models, we can generate music with the integration of AI generated music composition, emotional analysis, music generation algorithms, culture and genre exploration, purpose of music, music recommendations, copyright and ethical consideration, neuroscience, education and creativity, public perception. Our research paper is for the best music generation for the listeners of emotional music, cultural music and who wants the different music quality and quantity apart from the copyright issues.

A Music File Detection Method based on Convolutional Neural Network for Video-on-Demand Platform

On the video-on-demand (VOD) platform, audio processing includes noise suppression, loudness normalization, codec and others. However, these processing are not suitable for all scenes, especially for music audio quality and subjective perceptual. Therefore, different processing will be selected according to the music detector result. In this paper, a music or non-music audio detection used convolution neural network based on the Mel-spectrogram and modified spectral flux with music frame proportion estimation method is proposed. In the method, the Mel-spectrogram flux will greatly improve the traditional spectral flux calculation. This paper creatively proposes a music file-wise detection algorithm based on the VOD platform compared with previous frame-wise detection method. Proposed method has strong robustness, high computing efficiency, and low delay. It is more suitable for VOD platform audio detection requirements, and has direct benefits for audio quality control.

The Aesthetic Analysis of Music Generation Algorithms Based on Artificial Intelligence Technologies

The Aesthetic Analysis of Music Generation Algorithms Based on Artificial Intelligence Technologies

Combining circular laser sensing array with MUSIC algorithm for fast damage localization

Large plate shell structures are key supporting components for aerospace and transportation equipment. Rapid localization of damages in large-scale thin-walled structures is an urgently needed technology for on-site detection and maintenance. This paper proposed a rapid damage localization method based on a laser ultrasonic circular arrays, combining the advantages of the circular arrays and the MUSIC algorithm. It does not need reference signals in advance, the direct waves and scattered waves can be quickly distinguished and extracted according to the time of arrival (TOA). Then, the two-dimensional multiple signal classification method(2D-MUSIC) under near-field conditions is used to quickly locate damage, which is a feature subspace algorithm in array signal processing technology. Compared with other commonly used imaging algorithms, this method has great advantages in detection efficiency and angular resolution. According to the detection method proposed in this paper, the position of single damage and multiple incoherent damages within 0.12 m2 can be quickly found within 1.3 s, and the angle error and distance error are not more than 5° and 5 cm. However, for the damage of coherent sources, there may be artifacts interfering with the imaging results.

Exploration on 2D DOA estimation of linear array motion: Uniform linear motion

Generally, due to the limitation of the dimension of the array aperture, linear arrays cannot achieve two-dimensional (2D) direction of arrival (DOA) estimation. But the emergence of array motion provides a chance for that. In this paper, a generalized motion scheme and a novel method of 2D DOA estimation are proposed by exploring the linear array motion. To be specific, the linear arrays are controlled to move along an arbitrary direction at a constant velocity and snap per fixed time delay. All the received signals are processed to synthesize the comprehensive observation vector for an extended 2D virtual aperture. Subsequently, since most of 2D DOA estimation methods are not universal to our proposed motion scheme and the reduced-dimensional (RD) method fails to handle the case of the coupled parameters, a decoupled reduced-complexity multiple signals classification (DRC MUSIC) algorithm is designed specifically. Simulation results demonstrate that: a) our proposed scheme can achieve underdetermined 2D DOA estimation just by the linear arrays; b) our designed DRC MUSIC algorithm has the good properties of high accuracy and low complexity; c) our proposed motion scheme with the DRC method has better universality in the motion direction.

Research on multi-system interactive pop music composition based on artificial intelligence technology

Abstract The purpose of this paper is to analyze MIDI technology for pop music composition and the application of composition in artificial intelligence. Based on deep learning, a SeqVAE-based music melody generation algorithm is designed, the possibility of a pulse neural network-based music melody generation algorithm is explored, and a multi-system interactive pop music composition system is established and tested. This system's accuracy rate for bass and treble recognition was tested to be 4%, 4.1%, 2.47%, and 2.66, respectively, compared to the traditional recognition rate. The recommendation satisfaction rate was 96 when the number of queries was 50, the response speed increase time was less and kept within 3 seconds, and the CPU occupancy rate was within 40%.

Optimal allocation of multi-type FACTS devices for mitigating wind power spillage with enhancing voltage stability and social welfare

Most of countries around the world tends to increases the penetration of renewable energies generation in electrical power networks. This led to the emergence of many challenges in these systems, such as congestion of lines, voltage instability, etc. The most important of these problems is the spillage of renewable energies in order to maintain the stability of the power system. However, by using the traditional methods to mitigate the spillage, the stability of the power system may be deteriorated leading to a vulnerable power system against disturbances. This paper proposes a bilevel multi-objective Musical Chairs optimization algorithm for optimal allocation of multi-type flexible AC transmission system (FACTS) devices. The main target of the upper-level is to reduce the wind power spillage with minimize the investment cost of FACTS devices and load shedding, while maximize the voltage stability. Moreover, under different operating scenarios, the lower-level problem captured the market clearing with maintain the system constraints for maximize the social welfare. This leads to a robust and economical operating point where included enough levels of voltage security. The technique proposed in this paper is tested on the IEEE 24-bus modified reliability test system. The results show that the applicability of the proposed algorithm in aiding power system improvement planning for minimizing wind power spillage to integrate wind energy with maximizing the social welfare and improving the loadability and the voltage stability.

Convolutional neural network and recommendation algorithm for the new model of college music education

With the rapid development of the field of artificial intelligence, we expect that AI+ music can produce a new music education model to help efficient college students improve their personal music level. In this paper, we design a convolutional neural network music recommendation system, including a user modeling module, audio feature extraction module recommendation algorithm module, etc., which can model students' music preferences to generate Top recommendations for target users. It is helpful for teachers to timely and dynamically grasp the types of music that students like. Experiments show that the proposed method has certain feasibility and effectiveness. Compared with other traditional music recommendation algorithms, we can make full use of the powerful advantages of deep neural network automatic feature extraction and integrate the historical behavior information of users' interaction with music.

Range Sidelobe Iterative Suppression Algorithm for Extended Target with Non-Grid Multiple Scattering Points

This paper concentrates on extended targets to suppress high sidelobes in non-grid multiple scattering points. Notice that the problem essentially resolves the high sidelobes of the non-grid multiple scatterers after locating positions; we proposed a novel iterative algorithm based on an adaptive pulse compression algorithm on the basis of an off-grid position estimation. First, the grid offsets of the multiple scattering points through decoherence and super-resolution techniques are estimated by employing an enhanced MUSIC algorithm. Then, an optimization model is constructed for the non-grid multiple scattering points of the target. Finally, a sidelobe suppression algorithm based on the minimum mean square error (MMSE) criterion is presented. Numerical results reveal that the proposed algorithm can achieve better estimation performance on the offsets of the non-grid multiple scattering points, while it suppresses range sidelobes effectively.

Performance analysis of two-dimensional DOA estimation for uniform circular array

Perturbation analysis of direction of arrival (DOA) estimation has been studied mostly on one-dimensional arrays rather than planar arrays, which remains room for exploration. For uniform circular arrays with various model errors, we establish the corresponding system model. Then a performance analysis of the two-dimensional MUSIC algorithm is proposed by first-order error analysis and verified by simulation. The results show that the root-mean-squared error of DOA estimation is proportional to the standard deviation of the model errors, including gain-phase errors, sensor position errors, and mutual coupling effect.

A Fast Method for Multidimensional Joint Parameter Estimation of Polarization-Sensitive Arrays.

The paper proposes a fast method for the multidimensional parameter estimation of a polarization-sensitive array. Compared with conventional methods (e.g., MUSIC algorithm), the proposed method applies an iterative approach based on Newton's method to obtain joint estimation results instead of a spectral search and dimension reduction. It also extends the original Newton method to the 4D scale using the Hessian matrix. To reduce the complexity of establishing the aim function, Nystrom's method is applied to process the covariance matrix. A new threshold is also proposed to select the results, which can accomplish the parameter estimation with a small number of iterations while guaranteeing a high estimation accuracy. Finally, the proposed algorithm is analyzed in detail and the numerical simulations of various algorithms are compared to verify its effectiveness.

Music emotion representation based on non-negative matrix factorization algorithm and user label information.

Music emotion representation learning forms the foundation of user emotion recognition, addressing the challenges posed by the vast volume of digital music data and the scarcity of emotion annotation data. This article introduces a novel music emotion representation model, leveraging the nonnegative matrix factorization algorithm (NMF) to derive emotional embeddings of music by utilizing user-generated listening lists and emotional labels. This approach facilitates emotion recognition by positioning music within the emotional space. Furthermore, a dedicated music emotion recognition algorithm is formulated, alongside the proposal of a user emotion recognition model, which employs similarity-weighted calculations to obtain user emotion representations. Experimental findings demonstrate the method's convergence after a mere 400 iterations, yielding a remarkable 47.62% increase in F1 value across all emotion classes. In practical testing scenarios, the comprehensive accuracy rate of user emotion recognition attains an impressive 52.7%, effectively discerning emotions within seven emotion categories and accurately identifying users' emotional states.

Height Measurement for Meter Wave MIMO Radar based on Matrix Pencil Under Complex Terrain

Height measurement with meter wave (MW) MIMO radar in the actual complex terrain environment is a difficult problem because of terrain fluctuation, ground inclination, and multiple reflection points. Therefore, a practical signal model is studied, and a height measurement method of matrix pencil based on this signal model is proposed. Firstly, a more practical signal model of MW MIMO radar with the complex terrain is derived, and then the modified generalized MUSIC and maximum likelihood estimation algorithms are proposed. However, the modified algorithms need to know a priori information of reflected terrain angle. Therefore, the height measurement methods based on matrix pencil are studied which includes forward matrix pencil method, forward and backward matrix pencil method and unitary matrix pencil method. These method can quickly estimate the elevation and height without a priori information. Finally, the advantages/disadvantages of proposed algorithms under this complex terrain are compared by computer simulations. The results of simulations show that the effectiveness of our proposed algorithms.

Range ambiguity suppression under high-resolution estimation using the MUSIC-AP algorithm for pulse-Doppler radar

Range ambiguity is a significant challenge in conventional pulse-Doppler radar systems with medium or high pulse repetition frequencies, greatly impacting radar detection and imaging capabilities. The classic approaches such as waveform diversity and compressed sensing are based on the isolation between pulses. However, these methods encounter issues like residual ambiguity and grid mismatch, resulting in poor range ambiguity suppression performance. In this paper, the multiple signal classification-alternating projection (MUSIC-AP) method is proposed to address these challenges and achieve high-resolution estimation. The proposed method converts the range-velocity estimate into a spectrum estimate, enabling the utilization of the MUSIC algorithm to solve the grid mismatch problem and improve echo reconstruction. Then, to mitigate the effect of ambiguous energy on reconstruction, the MUSIC reconstruction subprocess is embedded into the AP framework. Through iterative approximation, the method gradually suppresses ambiguous and noise energy, thereby further enhancing the echo reconstruction accuracy and range ambiguity suppression performance. Simulations demonstrate the effectiveness of the proposed method. Moreover, algorithm analysis is conducted, considering aspects such as resolution, robustness, and sparsity, which provides a basis for parameter selection and applicability conditions.

System Construction of Intercity Culture and Xinjiang Music Integration in the Context of “One Belt, One Road” Based on SCP Model

Abstract Music content needs to draw nutrients from excellent regional culture, and excellent regional culture needs to be carried forward through music, so the integration of intercity culture and Xinjiang music is in line with the development and inheritance of music culture. This paper briefly introduces the analysis model of the SCP paradigm and the music recommendation algorithm improved by K-Means and Canopy clustering algorithms and analyzes the integration of intercity culture and Xinjiang music in the context of “One Belt One Road” based on the SCP model and the improved algorithm. The analysis shows that music lessons incorporating intercultural culture account for about 25% of the lesson time in Xinjiang’s primary and secondary school music curriculum, and in 2013, the content of Xinjiang’s local music culture at the junior high school level accounts for more than 35% of the total lesson time in the music curriculum. Intercultural culture and Xinjiang music are gradually merging, and music education will play a large role in further educational reform in terms of cultural transmission and integration.