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Audio Source Separation refers to the procedure of decomposing a mixed audio signal into its constituent components. This technique enables numerous applications, including creative music production, educational tools, karaoke, transcription, and music analysis. Despite the recent success of deep learning-based source separation techniques, these techniques often do not perform very accurately and do not provide high-quality separation of sources when many contain complex combinations in their mixtures. Source separation techniques generally rely on temporal or spectral features for analysis, which does not fully capture the complex dynamics of audio signals. To address these limitations, proposed the Amalgamation Audio Features (A2F), a hybrid representation combining temporal and spectral features. Then, Proposed the Audio Strips Network (ASNet), a novel framework designed to achieve clean and precise separation of individual audio sources with enhanced performance. ASNet utilized A2F, to separate sources more effectively. The model is trained and evaluated on the MUSDB, DSD100 and MUSDB18-HQ dataset, a benchmark for music source separation, and its standard measures like the Signal-to-Distortion Ratio (SDR) and Signal-to-Interference Ratio (SIR) are used to examine performance. ASNet achieves enhanced separation performance with SDR values of drums 12.63, vocal 11.42, bass 12.01 and other 11.14, and SIR values of drums 9.57, vocal 9.61, bass 9.66 and other 9.67. This advancement benefits musicians through high-quality remixing and creativity while aiding researchers in improving Deep Learning and hybrid audio processing models.

In vivo calcium imaging through microendoscopic lenses enables imaging of previously inaccessible neuronal populations deep within the brains of freely moving animals. However, it is computationally challenging to extract single-neuronal activity from microendoscopic data, because of the very large background fluctuations and high spatial overlaps intrinsic to this recording modality. Here, we describe a new constrained matrix factorization approach to accurately separate the background and then demix and denoise the neuronal signals of interest. We compared the proposed method against previous independent components analysis and constrained nonnegative matrix factorization approaches. On both simulated and experimental data recorded from mice, our method substantially improved the quality of extracted cellular signals and detected more well-isolated neural signals, especially in noisy data regimes. These advances can in turn significantly enhance the statistical power of downstream analyses, and ultimately improve scientific conclusions derived from microendoscopic data.

Plant tannins are widely found in plants and can be divided into hydrolyzed tannins and condensed tannins. In recent years, researchers have become more and more interested in using tannin-rich plants and plant extracts in ruminant diets to improve the quality of animal products. Some research results show that plant tannins can effectively improve the quality of meat and milk, and enhance the oxidative stability of the product. In this paper, the classification and extraction sources of plant tannins are reviewed, as well as the biological functions of plant tannins in animals. The antioxidant function of plant tannins is discussed, and the influence of their structure on antioxidation is analyzed. The effects of plant tannins against pathogenic bacteria and the mechanism of action are discussed, and the relationship between antibacterial action and antioxidant action is analyzed. The inhibitory effect of plant tannins on many kinds of pathogenic viruses and their action pathways are discussed, as are the antiparasitic properties of plant tannins. The anti-inflammatory action of tannins and its mechanism are analyzed. The function of plant tannins in antidiarrheal action and its influencing factors are discussed. In addition, the effects of plant tannins as feed additives on animals and the influencing factors are reviewed in this paper to provide a reference for further research.

Recent advances in calcium imaging, including the development of fast and sensitive genetically encoded indicators, high-resolution camera chips for wide-field imaging, and resonant scanning mirrors in laser scanning microscopy, have notably improved the temporal and spatial resolution of functional imaging analysis. Nonetheless, the variability of imaging approaches and brain structures challenges the development of versatile and reliable segmentation methods. Standard techniques, such as manual selection of regions of interest or machine learning solutions, often fall short due to either user bias, non-transferability among systems, or computational demand. To overcome these issues, we developed CalciSeg, a data-driven and reproducible approach for unsupervised functional calcium imaging data segmentation. CalciSeg addresses the challenges associated with brain structure variability and user bias by offering a computationally efficient solution for automatic image segmentation based on two parameters: regions’ size limits and number of refinement iterations. We evaluated CalciSeg efficacy on datasets of varied complexity, different insect species (locusts, bees, and cockroaches), and imaging systems (wide-field, confocal, and multiphoton), showing the robustness and generality of our approach. Finally, the user-friendly nature and open-source availability of CalciSeg facilitate the integration of this algorithm into existing analysis pipelines. •CalciSeg offers unbiased and unsupervised segmentation of functional imaging data.•CalciSeg is entirely data-driven and does not require training data.•Robust across various biological samples and imaging systems.•Successfully evaluated on samples differing in ROI size, shape, and number.•Straightforward and user-friendly integration into existing analysis pipelines.

The incipient bearing fault diagnosis is crucial to the industrial machinery maintenance. Developed based on the blind source separation, blind source extraction (BSE) has recently become the focus of intensive research work. However, owing to certain industrial restrictions, the number of sensors is usually less than that of the source signals, which is defined as an underdetermined BSE problem to identify the fault signals. The kernelized methods are found to be robust to the noise, especially in the presence of outliers, which makes it a suitable tool to extract fault signatures submerged in the strong environment noise. Thus, this paper proposes a new underdetermined BSE method based on the empirical mean decomposition and kernelized correlation. The experimental results indicate that the extracted fault signature presents more obvious periodicity. Two important parameters of this method, including the multi-shift number and the kernel size are investigated to improve the algorithm performance. Furthermore, performance comparisons with underdetermined BSE based on the second order correlation are made to emphasize the advantage of the presented method. The application of the proposed method is validated using the simulated signal and the rolling element bearing signal of the train vehicle axle.

There are many potential hazard sources along high-speed railways that threaten the safety of railway operation. Traditional ground search methods are failing to meet the needs of safe and efficient investigation. In order to accurately and efficiently locate hazard sources along the high-speed railway, this paper proposes a texture-enhanced ResUNet (TE-ResUNet) model for railway hazard sources extraction from high-resolution remote sensing images. According to the characteristics of hazard sources in remote sensing images, TE-ResUNet adopts texture enhancement modules to enhance the texture details of low-level features, and thus improve the extraction accuracy of boundaries and small targets. In addition, a multi-scale Lovász loss function is proposed to deal with the class imbalance problem and force the texture enhancement modules to learn better parameters. The proposed method is compared with the existing methods, namely, FCN8s, PSPNet, DeepLabv3, and AEUNet. The experimental results on the GF-2 railway hazard source dataset show that the TE-ResUNet is superior in terms of overall accuracy, F1-score, and recall. This indicates that the proposed TE-ResUNet can achieve accurate and effective hazard sources extraction, while ensuring high recall for small-area targets.

Down to Earths There are Earth-like extrasolar planets out there, but will we ever be able to see them? It's a tough ask: such a planet orbiting a nearby star is 1×10 10 times fainter than the star at a tiny angular separation of a tenth of an arcsecond or less. But now John Trauger and Wesley Traub report a laboratory demonstration of a technique that could be used on a space mission to detect an Earth-twin. Their system uses the coronagraph principle to suppress the starlight, and simple image processing to surpass the sensitivity of previously reported lab techniques by factors of 10,000 or more, and exceed the best results at ground-based observatories by factors of 100,000 or more. An Earth-like planet orbiting a nearby star is 1 × 10 −10 times fainter than the star at angular separations of typically 0.1 arcsecond or less, but this paper reports an experiment that suppresses the diffracted and scattered light near a star-like source to a level of 6 × 10 −10 in individual coronagraph images. The detection and characterization of an Earth-like planet orbiting a nearby star requires a telescope with an extraordinarily large contrast at small angular separations. At visible wavelengths, an Earth-like planet would be 1 × 10 -10 times fainter than the star at angular separations of typically 0.1 arcsecond or less 1 , 2 . There are several proposed space telescope systems that could, in principle, achieve this 3 , 4 , 5 , 6 . Here we report a laboratory experiment that reaches these limits. We have suppressed the diffracted and scattered light near a star-like source to a level of 6 × 10 -10 times the peak intensity in individual coronagraph images. In a series of such images, together with simple image processing, we have effectively reduced this to a residual noise level of about 0.1 × 10 -10 . This demonstrates that a coronagraphic telescope in space could detect and spectroscopically characterize nearby exoplanetary systems, with the sensitivity to image an ‘Earth-twin’ orbiting a nearby star.

The Elixir System at the Canada‐France‐Hawaii Telescope performs data characterization and calibration for all data from the wide‐field mosaic imagers CFH12K and MegaPrime. The project has several related goals, including monitoring data quality, providing high‐quality master detrend images, determining the photometric and astrometric calibrations, and automatic preprocessing of images for queued service observing (QSO). The Elixir system has been used for all data obtained with CFH12K since the QSO project began in 2001 January. In addition, it has been used to process archival data from the CFH12K and all MegaPrime observations beginning in 2002 December. The Elixir system has been extremely successful in providing well‐characterized data to the end observers, who may otherwise be overwhelmed by data‐processing concerns.

The vibration signals will take on cyclical characteristics when fault arises in the rolling element bearing, and a two-step blind source extraction (BSE) method for fault diagnosis of rolling element bearing is proposed in the paper using the above property. Firstly, calculate the theoretical basic cyclet of the target source fault signal, and the weighted separation matrix ŵ and desired source signal are obtained coarsely. Secondly, use ŵ as the initial weighted matrix and apply the fixed-point algorithm basing on high-order statistics on the observed signals, and much more perfect target source signal is got at last. The proposed method has the following advantages over other BSE method such as constrained independent component analysis (CICA) basing on the analyzed results of simulation and experiment: The fundamental period τ of the target source signal does not needed to be estimated accurately, and the reference signal also does not need to be constructed precisely. However, these two conditions are the necessary prerequisites of CICA. Besides, the proposed method also has the advantage of higher accuracy over the other recent BSE methods through comparison.

PurposeFault diagnosis methods based on blind source separation (BSS) for rolling element bearings are necessary tools to prevent any unexpected accidents. In the field application, the actual signal acquisition is usually hindered by certain restrictions, such as the limited number of signal channels. The purpose of this study is to fulfill the weakness of the existed BSS method.Design/methodology/approachTo deal with this problem, this paper proposes a blind source extraction (BSE) method for bearing fault diagnosis based on empirical mode decomposition (EMD) and temporal correlation. First, a single-channel undetermined BSS problem is transformed into a determined BSS problem using the EMD algorithm. Then, the desired fault signal is extracted from selected intrinsic mode functions with a multi-shift correlation method.FindingsExperimental results prove the extracted fault signal can be easily identified through the envelope spectrum. The application of the proposed method is validated using simulated signals and rolling element bearing signals of the train axle.Originality/valueThis paper proposes an underdetermined BSE method based on the EMD and the temporal correlation method for rolling element bearings. A simulated signal and two bearing fault signal from the train rolling element bearings show that the proposed method can well extract the bearing fault signal. Note that the proposed method can extract the periodic fault signal for bearing fault diagnosis. Thus, it should be helpful in the diagnosis of other rotating machinery, such as gears or blades.