Real-time source localization is crucial for high-end automation and artificial intelligence (AI) products. However, a low signal-to-noise ratio (SNR) and limited processing time can reduce ...localization accuracy. This work proposes a new architecture for a time-domain feedback-based beamformer that meets real-time processing demands. The main objective of this design is to locate reflective sources by estimating their direction of arrival (DOA) and signal range. Incorporating a feedback mechanism in this architecture refines localization precision, a unique aspect of this approach. We conducted an in-depth analysis to compare the effectiveness of time-domain feedback beamforming against conventional time-domain methods, highlighting their benefits and limitations. Our evaluation of the proposed architecture, based on critical performance indicators such as peak-to-sidelobe ratio, mainlobe width, and directivity factor, demonstrates its ability to improve beamformer effectiveness significantly.
This paper introduces a new technique for automatic modulation classification (AMC) in Cognitive Radio (CR) networks. The method employs a straightforward classifier that utilizes high-order cumulant ...for training. It focuses on the statistical behavior of both analog modulation and digital schemes, which have received limited attention in previous works. The simulation results show that the proposed method performs well with different signal-to-noise ratios (SNRs) and channel conditions. The classifier's performance is superior to that of complex deep learning methods, making it suitable for deployment in CR networks' end units, especially in military and emergency service applications. The proposed method offers a cost-effective and high-quality solution for AMC that meets the strict demands of these critical applications.
Designing beampatterns with constant beamwidth over a wide range of frequencies is useful in many applications in speech, radar, sonar and communication. In this paper, we design constant-beamwidth ...beamformers for concentric ring arrays. The proposed beamformers utilize the circular geometry to provide improved beamwidth consistency compared to beamformers which are designed for linear sensor arrays of the same order. In the proposed configuration, all sensors on each ring share the same weight value. This constraint significantly simplifies the beamformers and reduces the hardware and computational resources required in a physical setup. Furthermore, a theoretical justification of the beamforming method is provided. We demonstrate the advantages of the proposed beamformers compared to the one-dimensional configuration in terms of directivity index, white noise gain and sidelobe attenuation.
In this paper, we address the problem of dual-microphone speech reinforcement for improving in-car speech communication
via
howling control. A speech reinforcement system acquires speech from a ...speaker’s microphone and delivers it to the other listeners in the car cabin through loudspeakers. A car cabin’s small space makes it vulnerable to acoustic feedback, resulting in the appearance of howling noises. The proposed system aims to maintain a desired high amplification gain over time while not compromising the output speech quality. The dual-microphone system consists of a microphone for speech acquisition and another microphone that monitors the environment for howling detection, where its location depends on its howling detection sensitivity. The proposed algorithm contains a gain-control segment based on the magnitude-slope-deviation measure, which reduces the amplification-gain in the case of howling detection. To find the optimal locations of the howling-detection microphone in the cabin, for a devised set of scenarios, a Pareto optimization method is applied. The Pareto optimization considers the bi-objective nature of the problem, i.e., minimizing both the relative gain-reduction and the overall speech distortion. It is shown that the proposed dual-microphone system outperforms a single-microphone-based system. The performance improvement is demonstrated by showing the higher howling detection sensitivity of the dual-microphone system. Additionally, a microphone constellation design process, for optimal howling detection, is provided through the utilization of the Pareto fronts and anti-fronts approach.
This paper presents a novel approach utilizing uniform rectangular arrays to design a constant-beamwidth (CB) linearly constrained minimum variance (LCMV) beamformer, which also improves white noise ...gain and directivity. By employing a generalization of the convolutional Kronecker product beamforming technique, we decompose a physical array into virtual subarrays, each tailored to achieve a specific desired feature, and we subsequently synthesize the original array’s beamformer. Through simulations, we demonstrate that the proposed approach successfully achieves the desired beamforming characteristics while maintaining favorable levels of white noise gain and directivity. A comparative analysis against existing methods from the literature reveals that the proposed method performs better than the existing methods.
In this paper, we present a new method for multitask learning applied to ultrasound beamforming. Beamforming is a critical component in the ultrasound image formation pipeline. Ultrasound images are ...constructed using sensor readings from multiple transducer elements, with each element typically capturing multiple acquisitions per frame. Hence, the beamformer is crucial for framerate performance and overall image quality. Furthermore, post-processing, such as image denoising, is usually applied to the beamformed image to achieve high clarity for diagnosis. This work shows a fully convolutional neural network that can learn different tasks by applying a new weight normalization scheme. We adapt our model to both high frame rate requirements by fitting weight normalization parameters for the sub-sampling task and image denoising by optimizing the normalization parameters for the speckle reduction task. Our model outperforms single-angle delay and sum on pixel-level measures for speckle noise reduction, subsampling, and single-angle reconstruction.
This article presents a Kronecker-product (KP) beamforming approach incorporating sparse concentric circular arrays (SCCAs). The locations of the microphones on the SCCA are optimized concerning the ...broadband array directivity over a wide range of direction-of-arrival (DOA) deviations of a desired signal. A maximum directivity factor (MDF) sub-beamformer is derived accordingly with the optimal locations. Then, we propose two global beamformers obtained as a Kronecker product of a uniform linear array (ULA) and the SCCA sub-beamformer. The global beamformers differ by the type of the ULA, which is designed either as an MDF sub-beamformer along the <inline-formula><tex-math notation="LaTeX">\mathsf {x}</tex-math></inline-formula>-axis or as a maximum white noise gain sub-beamformer along the <inline-formula><tex-math notation="LaTeX">\mathsf {y}</tex-math></inline-formula>-axis. We analyze the performance of the proposed beamformers in terms of the directivity factor, the white noise gain, and their spatial beampatterns. Compared to traditional beamformers, the proposed beamformers exhibit considerably larger tolerance to DOA deviations concerning both the azimuth and elevation angles. Experimental results with speech signals in noisy and reverberant environments demonstrate that the proposed approach outperforms traditional beamformers regarding the perceptual evaluation of speech quality (PESQ) and short-time objective intelligibility (STOI) scores when the desired speech signals deviate from the nominal DOA.
Beamformers have been widely used to enhance signals from a desired direction and suppress noise and interfering signals from other directions. Constant beamwidth beamformers enable a fixed beamwidth ...over a wide range of frequencies. Most of the existing approaches to design constant beamwidth beamformers are based on optimization algorithms with high computational complexity and are often sensitive to microphone mismatches. Other existing methods are based on adjusting the number of sensors according to the frequency, which simplify the design, but cannot control the sidelobe level. Here, we propose a window-based technique to attain the beamwidth constancy, in which different shapes of standard window functions are applied for different frequency bins as the real weighting coefficients of microphones. Thereby, not only do we keep the beamwidth constant, but we also control the sidelobe level. Simulation results show the advantages of our method compared with existing methods, including lower sidelobe level, higher directivity factor, and higher white noise gain.
The Impact Tech Startup (ITS) is a new, rapidly developing type of organizational category. Based on an entrepreneurial approach and technological foundations, ITSs adopt innovative strategies to ...tackle a variety of social and environmental challenges within a for-profit framework and are usually backed by private investment. This new organizational category is thus far not discussed in the academic literature. The paper first provides a conceptual framework for studying this organizational category, as a combination of aspects of social enterprises and startup businesses. It then proposes a machine learning (ML)-based algorithm to identify ITSs within startup databases. The UN’s Sustainable Development Goals (SDGs) are used as a referential framework for characterizing ITSs, with indicators relating to those 17 goals that qualify a startup for inclusion in the impact category. The paper concludes by discussing future research directions in studying ITSs as a distinct organizational category through the usage of the ML methodology.
Determining the cyclic-alternating-pattern (CAP) phases in sleep using electroencephalography (EEG) signals is crucial for assessing sleep quality. However, most current methods for CAP ...classification primarily rely on classical machine learning techniques, with limited implementation of deep-learning-based tools. Furthermore, these methods often require manual feature extraction. Herein, we propose a fully automatic deep-learning-based algorithm that leverages convolutional neural network architectures to classify the EEG signals via their time-frequency representations. Through our investigation, we explored using time-frequency analysis techniques and found that Wigner-based representations outperform the commonly used short-time Fourier transform for CAP classification. Additionally, our algorithm incorporates contextual information of the EEG signals and employs data augmentation techniques specifically designed to preserve the time-frequency structure. The model is developed using EEG signals of healthy subjects from the publicly available CAP sleep database (CAPSLPDB) on Physionet. An experimental study demonstrates that our algorithm surpasses existing machine-learning-based methods, achieving an accuracy of 77.5% on a balanced test set and 81.8% when evaluated on an unbalanced test set. Notably, the proposed algorithm exhibits efficiency and scalability, making it suitable for on-device implementation to enhance CAP identification procedures.
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