Noisy Background Research Articles

Detection of electrode misalignment and its effect on joint quality in resistance spot welding: a low-cost computer vision-based approach

Electrode misalignment in resistance spot welding can be caused by poor fitting or deformation of electrode with continuous usage. This leads to asymmetric weld nugget formation, porosity and expulsion. This paper presents a novel low-cost real-time inspection system for angular misalignment using an image processing approach. The proposed solution can effectively segment the electrode tips even from the image captured at noisy industrial background such as automotive assembly line, by using a regional convolutional neural network based object identification method. The trained model has a mean average precision and recall of 99.01% and 96.6%, respectively. A series of image processing tools and mathematical operations were used to identify the edge line contours of electrode tips accurately from the detection mask, and determine the angular misalignment with a maximum deviation of less than 0.06°. Experimental results showed that the weld nuggets exhibited porosity, shrinkage voids, and cracks when performed under angular misalignment conditions.

Optical Gesture Recognition for Virtual Cursor Navigation

In the rapidly evolving landscape of technology, there is a constant quest for innovations that set themselves apart. Gestures emerge as a highly coveted means of communication with machines. The significance of Human-Computer Interaction becomes evident when harnessing human gestures to govern computer applications. While conventional controls like mice, keyboards, and laser pointers have been commonplace, recent technological strides have introduced more effective techniques for application control. Numerous Gesture Recognition methods, leveraging image processing, have been explored in the past. However, the challenge of recognizing gestures in noisy backgrounds has persistently posed difficulties. In our proposed system, we intend to employ a technique known as Augmentation in Image processing to command a Media Player. This involves recognizing gestures to manipulate operations on the Media Player. Augmentation proves advantageous, particularly in the realm of virtual reality, as it is not constrained by background limitations. Additionally, it eliminates dependencies on specific accessories like gloves or color pointers for gesture recognition. The proposed system caters to users seeking a simplified and enhanced interaction with their computers.

Age-related differences in processing of emotions in speech disappear with babble noise in the background

ABSTRACT Older adults process emotional speech differently than young adults, relying less on prosody (tone) relative to semantics (words). This study aimed to elucidate the mechanisms underlying these age-related differences via an emotional speech-in-noise test. A sample of 51 young and 47 older adults rated spoken sentences with emotional content on both prosody and semantics, presented on the background of wideband speech-spectrum noise (sensory interference) or on the background of multi-talker babble (sensory/cognitive interference). The presence of wideband noise eliminated age-related differences in semantics but not in prosody when processing emotional speech. Conversely, the presence of babble resulted in the elimination of age-related differences across all measures. The results suggest that both sensory and cognitive-linguistic factors contribute to age-related changes in emotional speech processing. Because real world conditions typically involve noisy background, our results highlight the importance of testing under such conditions.

U-Plume: automated algorithm for plume detection and source quantification by satellite point-source imagers

Abstract. Current methods for detecting atmospheric plumes and inferring point-source rates from high-resolution satellite imagery are labor-intensive and not scalable with regard to the growing satellite dataset available for methane point sources. Here, we present a two-step algorithm called U-Plume for automated detection and quantification of point sources from satellite imagery. The first step delivers plume detection and delineation (masking) with a U-Net machine learning architecture for image segmentation. The second step quantifies the point-source rate from the masked plume using wind speed information and either a convolutional neural network (CNN) or a physics-based integrated mass enhancement (IME) method. The algorithm can process 62 images (each measuring 128 pixels × 128 pixels) per second on a single 2.6 GHz Intel Core i7-9750H CPU. We train the algorithm using large-eddy simulations of methane plumes superimposed on noisy and variable methane background scenes from the GHGSat-C1 satellite instrument. We introduce the concept of point-source observability, Ops=Q/(UWΔB), as a single dimensionless number to predict plume detectability and source rate quantification error from an instrument as a function of source rate Q, wind speed U, instrument pixel size W, and instrument-dependent background noise ΔB. We show that Ops can powerfully diagnose the ability of an imaging instrument to observe point sources of a certain magnitude under given conditions. U-Plume successfully detects and masks plumes from sources as small as 100 kg h−1 in GHGSat-C1 images over surfaces with low background noise and successfully handles larger point sources over surfaces with substantial background noise. We find that the IME method for source quantification is unbiased over the full range of source rates, while the CNN method is biased towards the mean of its training range. The total error in source rate quantification is dominated by wind speed at low wind speeds and by the masking algorithm at high wind speeds. A wind speed of 2–4 m s−1 is optimal for detection and quantification of point sources from satellite data.

Protein Detection and Localization in Plant Cells Using Spot-Tagging.

Fluorescent labelling of proteins enables the determination of their spatiotemporal localization but, sometimes, it can perturb their activity, native localization, and functionality. Spot-tag is a12-amino acid peptide recognized by a single-domain nanobody and could potentially resolve the issues associated with large fluorescence tags due to its small size. Here, using as an example the microtubule motor CENTROMERIC PROTEIN E-RELATED KINESIN 7.3 (KIN7.3), we introduce the spot-tag for protein labelling in fixed and living plant cells. Spot-tagging and detection by an anti-spot nanobody of ectopically expressed KIN7.3 did not interfere with its native localization. Most importantly, our spot-tagging pipeline facilitated the localization of KIN7.3 much more rapidly and likely accurately than labelling with large fluorescent proteins or even immunolocalization approaches. We should, though, note some limitations we have not resolved yet. Spot-tagging is functional only in fixed cells; it is available only as two fluorophores and may create a noisy background during imaging. However, we foresee that, besides the limitations of this method, spot-tagging will apply to many proteins, offsetting activity perturbations and low photon quantum yields of other protein-tagging approaches.

Approaching Electroencephalographic Pathological Spikes in Terms of Solitons

A delicate balance between dissipative and nonlinear forces allows traveling waves termed solitons to preserve their shape and energy for long distances without steepening and flattening out. Solitons are so widespread that they can generate both destructive waves on oceans’ surfaces and noise-free message propagation in silica optic fibers. They are naturally observed or artificially produced in countless physical systems at very different coarse-grained scales, from solar winds to Bose–Einstein condensates. We hypothesize that some of the electric oscillations detectable by scalp electroencephalography (EEG) could be assessed in terms of solitons. A nervous spike must fulfill strict mathematical and physical requirements to be termed a soliton. They include the proper physical parameters like wave height, horizontal distance and unchanging shape; the appropriate nonlinear wave equations’ solutions and the correct superposition between sinusoidal and non-sinusoidal waves. After a thorough analytical comparison with the EEG traces available in the literature, we argue that solitons bear striking similarities with the electric activity recorded from medical conditions like epilepsies and encephalopathies. Emerging from the noisy background of the normal electric activity, high-amplitude, low-frequency EEG soliton-like pathological waves with relatively uniform morphology and duration can be observed, characterized by repeated, stereotyped patterns propagating on the hemispheric surface of the brain over relatively large distances. Apart from the implications for the study of cognitive activities in the healthy brain, the theoretical possibility to treat pathological brain oscillations in terms of solitons has powerful operational implications, suggesting new therapeutical options to counteract their detrimental effects.

A novel residual global context shrinkage network based fault diagnosis method for rotating machinery under noisy conditions

In real industrial environments, vibration signals generated during the operation of rotating machinery are typically accompanied by significant noise. Existing deep learning methods often yield unsatisfactory diagnostic results when dealing with noisy signals. To address this problem, a novel residual global context shrinkage network (RGNet) is proposed in this paper. Firstly, to fully utilize the useful information in the raw vibration signal, a multi-sensor fusion strategy based on dispersion entropy is designed as the input of the deep network. Then, the RGNet is designed, which improves the long-distance modeling capability of the deep network while suppressing noise, optimizes the network gradient and computational performance. Finally, the noise suppression ability and feature extraction ability of the RGNet are intuitively revealed through an interpretability study. The advantages of the proposed method are proved through a series of comparison experiments under noisy backgrounds.

Using UNSEEN approach to attribute regional UK winter rainfall extremes

AbstractThree out of the five highest daily winter rainfall totals on record over Northern England have occurred from 2015 onwards. Heavy rainfall events in the winters of 2013–2014, 2015–2016 and 2019–2020 led to more than 2.8‐billion‐pounds of insurance losses from flooding in the UK. Has the frequency of these events been influenced by human‐induced climate change? Winter rainfall in the UK is extremely variable year‐to‐year, which makes the attribution of rainfall extremes particularly challenging. To tackle this problem, we introduce an UNprecedented Simulated Extreme Ensemble (UNSEEN) approach for the attribution of such extremes, thereby increasing the data available, and apply this approach to five recent flooding events on a regional scale. Using this method, for all five events we found a significant climate signal in the extreme regional rainfall totals immediately preceding the flooding. Results were fairly similar for each—with the events being found to become from 1.4 to 2.6 times more likely. An alternative attribution method that uses a different model with substantially less data did not find significant increases, reinforcing the need for very large amounts of data to detect significant changes in extreme rainfall against a noisy background of natural variability. We also examine how extreme rainfall is changing more broadly across English regions in winter, finding that 1‐in‐10 to 1‐in‐90‐year winter rainfall totals have changed significantly in Northern England. The high volume of data using UNSEEN has enabled us to examine the dynamics of these events, showing that daily extremes in winter are likely to have increased across all the circulation patterns responsible for high rainfall in English regions.

Exploring the Impact of Mismatch Conditions, Noisy Backgrounds, and Speaker Health on Convolutional Autoencoder-Based Speaker Recognition System with Limited Dataset

This paper presents a novel approach to enhance the success rate and accuracy of speaker recognition and identification systems. The methodology involves employing data augmentation techniques to enrich a small dataset with audio recordings from five speakers, covering both male and female voices. Python programming language is utilized for data processing, and a convolutional autoencoder is chosen as the model. Spectrograms are used to convert speech signals into images, serving as input for training the autoencoder. The developed speaker recognition system is compared against traditional systems relying on the MFCC feature extraction technique. In addition to addressing the challenges of a small dataset, the paper explores the impact of a "mismatch condition" by using different time durations of the audio signal during both training and testing phases. Through experiments involving various activation and loss functions, the optimal pair for the small dataset is identified, resulting in a high success rate of 92.4% in matched conditions. Traditionally, Mel-Frequency Cepstral Coefficients (MFCC) have been widely used for this purpose. However, the COVID-19 pandemic has drawn attention to the virus's impact on the human body, particularly on areas relevant to speech, such as the chest, throat, vocal cords, and related regions. COVID-19 symptoms, such as coughing, breathing difficulties, and throat swelling, raise questions about the influence of the virus on MFCC, pitch, jitter, and shimmer features. Therefore, this research aims to investigate and understand the potential effects of COVID-19 on these crucial features, contributing valuable insights to the development of robust speaker recognition systems.

A Serial Multi-Scale Feature Fusion and Enhancement Network for Amur Tiger Re-Identification.

The Amur tiger is an important endangered species in the world, and its re-identification (re-ID) plays an important role in regional biodiversity assessment and wildlife resource statistics. This paper focuses on the task of Amur tiger re-ID based on visible light images from screenshots of surveillance videos or camera traps, aiming to solve the problem of low accuracy caused by camera perspective, noisy background noise, changes in motion posture, and deformation of Amur tiger body patterns during the re-ID process. To overcome this challenge, we propose a serial multi-scale feature fusion and enhancement re-ID network of Amur tiger for this task, in which global and local branches are constructed. Specifically, we design a global inverted pyramid multi-scale feature fusion method in the global branch to effectively fuse multi-scale global features and achieve high-level, fine-grained, and deep semantic feature preservation. We also design a local dual-domain attention feature enhancement method in the local branch, further enhancing local feature extraction and fusion by dividing local feature blocks. Based on the above model structure, we evaluated the effectiveness and feasibility of the model on the public dataset of the Amur Tiger Re-identification in the Wild (ATRW), and achieved good results on mAP, Rank-1, and Rank-5, demonstrating a certain competitiveness. In addition, since our proposed model does not require the introduction of additional expensive annotation information and does not incorporate other pre-training modules, it has important advantages such as strong transferability and simple training.

Errors in visual search: Are they stochastic or deterministic?

In any visual search task in the lab or in the world, observers will make errors. Those errors can be categorized as “deterministic”: If you miss this target in this display once, you will definitely miss it again. Alternatively, errors can be “stochastic”, occurring randomly with some probability from trial to trial. Researchers and practitioners have sought to reduce errors in visual search, but different types of errors might require different techniques for mitigation. To empirically categorize errors in a simple search task, our observers searched for the letter “T” among “L” distractors, with each display presented twice. When the letters were clearly visible (white letters on a gray background), the errors were almost completely stochastic (Exp 1). An error made on the first appearance of a display did not predict that an error would be made on the second appearance. When the visibility of the letters was manipulated (letters of different gray levels on a noisy background), the errors became a mix of stochastic and deterministic. Unsurprisingly, lower contrast targets produced more deterministic errors. (Exp 2). Using the stimuli of Exp 2, we tested whether errors could be reduced using cues that guided attention around the display but knew nothing about the content of that display (Exp3a, b). This had no effect, but cueing all item locations did succeed in reducing deterministic errors (Exp3c).

A Moiré Removal Method Based on Peak Filtering and Image Enhancement

Screen photos often suffer from moiré patterns, which significantly affect their visual quality. Although many deep learning-based methods for removing moiré patterns have been proposed, they fail to recover images with complex textures and heavy moiré patterns. Here, we focus on text images with heavy moiré patterns and propose a new demoiré approach, incorporating frequency-domain peak filtering and spatial-domain visual quality enhancement. We find that the content of the text image mainly lies in the central region, whereas the moiré pattern lies in the peak region, in the frequency domain. Based on this observation, a peak-filtering algorithm and a central region recovery strategy are proposed to accurately locate and remove moiré patterns while preserving the text parts. In addition, to further remove the noisy background and paint the missing text parts, an image enhancement algorithm utilising the Otsu method is developed. Extensive experimental results show that the proposed method significantly removes severe moiré patterns from images with better visual quality and lower time cost compared to the state-of-the-art methods.

Novel compound multistable stochastic resonance weak signal detection

Abstract The research on stochastic resonance (SR) which is used to extract weak signals from noisy backgrounds is of great theoretical significance and promising application. To address the shortcomings of the classical tristable SR model, this article proposes a novel compound multistable stochastic resonance (NCMSR) model by combining the Woods–Saxon (WS) and tristable models. The influence of the parameters of the NCMSR systems on the output response performance is studied under different α stable noises. Meanwhile, the adaptive synchronization optimization algorithm based on the proposed model is employed to achieve periodic and non-periodic signal identifications in α stable noise environments. The results show that the proposed system model outperforms the tristable system in terms of detection performance. Finally, the NCMSR model is applied to 2D image processing, which achieves great noise reduction and image recovery effects.

Deep causal speech enhancement and recognition using efficient long-short term memory Recurrent Neural Network.

Long short-term memory (LSTM) has been effectively used to represent sequential data in recent years. However, LSTM still struggles with capturing the long-term temporal dependencies. In this paper, we propose an hourglass-shaped LSTM that is able to capture long-term temporal correlations by reducing the feature resolutions without data loss. We have used skip connections in non-adjacent layers to avoid gradient decay. In addition, an attention process is incorporated into skip connections to emphasize the essential spectral features and spectral regions. The proposed LSTM model is applied to speech enhancement and recognition applications. The proposed LSTM model uses no future information, resulting in a causal system suitable for real-time processing. The combined spectral feature sets are used to train the LSTM model for improved performance. Using the proposed model, the ideal ratio mask (IRM) is estimated as a training objective. The experimental evaluations using short-time objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ) have demonstrated that the proposed model with robust feature representation obtained higher speech intelligibility and perceptual quality. With the TIMIT, LibriSpeech, and VoiceBank datasets, the proposed model improved STOI by 16.21%, 16.41%, and 18.33% over noisy speech, whereas PESQ is improved by 31.1%, 32.9%, and 32%. In seen and unseen noisy situations, the proposed model outperformed existing deep neural networks (DNNs), including baseline LSTM, feedforward neural network (FDNN), convolutional neural network (CNN), and generative adversarial network (GAN). With the Kaldi toolkit for automated speech recognition (ASR), the proposed model significantly reduced the word error rates (WERs) and reached an average WER of 15.13% in noisy backgrounds.

Retention time-independent strategy for screening pesticide residues in herbs based on a fingerprint database and all ion fragmentation acquisition with LC-QTOF MS.

A retention time (RT)-independent strategy for nontargeted screening of pesticide residues in herbs was exploited using liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF MS). The core of this strategy is a fingerprint database coupled with a data-independent acquisition (DIA) scan mode of all ion fragmentation (AIF). In the fingerprint database, a total of 150 pesticides with quasimolecular ions and fragment ions at five-level collision energies were collected as qualified ions for screening. During the data acquisition, the AIF scan was performed via real unbiased full-spectrum MS/MS acquisition. Six herb matrices spiked with 30 banned pesticides were used to evaluate the applicability of the strategy in real samples. The use of the narrow ion mass extraction window (10 mDa) and the narrow RT window (0.1 min) enabled the effective extraction of spectra from noisy backgrounds and the discovery of suspected pesticides via similarity matching of filtered qualified ions. On average, more than 11/30 of pesticides at 1 ng mL-1 and more than 23/30 of pesticides at 10 ng mL-1 or lower could be screened out in each matrix using at least two qualified ions. In addition, the AIF mode exhibited superior anti-interference capability compared to data-dependent acquisition (DDA) and sequential window acquisition of all theoretical mass spectra (SWATH), as determined by comparing the limits of screening (LOSs) of 30 banned pesticides spiked into Isatidis Folium. Finally, the developed strategy was applied to screen pesticide residues in extracts of Ganoderma and Foeniculi Fructus. Phorate-sulfone and phorate-sulfoxide were found in Ganoderma, as well as terbufos-sulfone and terbufos-sulfoxide were found in Foeniculi Fructus. In conclusion, the developed RT-independent strategy based on a fingerprint database and AIF acquisition with LC-QTOF MS seems to be one of the most efficient tools for the analysis of nontargeted pesticide residues in complicated matrices.

APest-YOLO: A Multi-Scale Agricultural Pest Detection Model Based on Deep Learning

HighlightsWe propose a APest-YOLO model, an innovative agricultural pest detection model founded on a lightweight approach, thus improving the efficiency of pest detection while also reducing the model’s dimensions.The model incorporates a novel grouping atrous spatial pyramid pooling fast module with four convolution layers to enhance multi-scale pest feature representation, aiming for improved detection accuracy. Additionally, it utilizes a convolutional block attention module to reduce noise and complexity in background images, facilitating the extraction of more refined and smoother pest features for accurate detection.We conducted experiments on agricultural pest detection using a comprehensive multi-pest dataset. The APest-YOLO model surpasses existing detection models in terms of mAP0.5, mAP0.5:0.95.Abstract. Crop pests and diseases pose a significant threat to smart agriculture, making pest detection a critical component in agricultural applications. However, current detection methods often struggle to effectively identify multi-scale pest data. In response, we present a novel agricultural pest detection model (APest-YOLO) based on a lightweight approach. The APest-YOLO model enhances pest detection efficiency while reducing model size, which is different from the baseline models. Our model features an original grouping atrous spatial pyramid pooling fast module, comprising four convolution layers with varying rates to capture multi-scale and multi-level pest characteristics. Additionally, we incorporate a convolutional block attention module to extract smoother features from pest images with noisy and complex backgrounds. We evaluated the APest-YOLO model on a large-scale multi-pest dataset encompassing 37 pest species. Furthermore, the APest-YOLO model achieved 99.3% mAP0.5 and found that it outperforms baseline models, demonstrating effective pest species detection capabilities. Keywords: Attention mechanism, Convolutional neural network, Intelligent agriculture, Pest detection, YOLO.

Tar Spot Disease Identification and Severity Estimation Using Deep Learning

HighlightsImage classification was used to identify tar spot, NLB, GLS, and NLS corn disease with 99.51% testing accuracy.YOLOv7 object detection was used to locate and identify tar spot lesions on infected leaves with mAP@0.5 of 40.46%.A novel tar spot severity estimation approach was developed using deep learning and color histogram thresholding.A web-based disease diagnosis tool was developed to help with an accurate, in-field diagnosis of tar spot in corn.Abstract. Management of tar spot disease in corn has traditionally relied on manual field scouting and visual analysis since it was first observed in the US in 2015. The disease has been identified using deep learning (DL) models as the application of computer vision and DL techniques for disease management is increasing. The severity of the disease has been estimated using close-range images of infected corn leaves under lab conditions with uniform backgrounds. However, DL models trained using images acquired under uniform lab conditions are limited in their ability to generalize to field conditions. Although recent studies have shown success in quantifying the disease, its analysis under field conditions with complex backgrounds to provide a field-ready solution in the form of an application has not yet been developed. Therefore, this study acquired a custom handheld imagery dataset of 455 images for the tar spot disease in a greenhouse with noisy backgrounds to simulate field conditions for training DL-based disease identification models. The dataset was combined with a publicly available Corn Disease and Severity (CD&S) dataset consisting of field-acquired diseased images corresponding to Northern Leaf Blight (NLB), Gray Leaf Spot (GLS), and Northern Leaf Spot (NLS). Image classification models were first trained to accurately identify tar spot, NLB, GLS, and NLS diseases. To accurately locate and identify tar spot disease lesions, YOLOv7 object detection models were then trained. In addition, semantic segmentation models were trained using the UNet architecture for leaf and lesion segmentation. After training the image classification models, the highest testing accuracy of 99.51% was achieved with the InceptionV3 model. For YOLOv7 object detection, the highest mAP of 40.46% was achieved for locating and identifying tar spot disease lesions on infected leaves. Finally, for UNet semantic segmentation, the mIoU for leaf and lesion segmentation were 0.80 and 0.28, respectively. Therefore, traditional color histogram thresholding was used for segmenting the tar spot lesions, and DL techniques were used to develop a novel severity estimation framework. After evaluating the models, the image classification model was deployed on a progressive web application accessible through a smartphone to enable real-time analysis. In this study, different DL models were trained and evaluated for tar spot disease identification and its severity estimation. In addition, a smartphone-based disease diagnosis tool was developed that has the potential for an accurate, in-field diagnosis of tar spot in corn. Keywords: Computer vision, Deep learning, Disease identification, Image classification, Precision agriculture, Severity estimation, Tar spot.

Feature extraction and pattern recognition of gas pipeline flow noise signals in a strong noisy background.

The purpose of this study is to put forward a feature extraction and pattern recognition method for the flow noise signal of natural gas pipelines in view of the complex situation brought by the rapid development and expansion of urban natural gas infrastructure in China, especially in the case that there are active and abandoned pipelines, metal and nonmetal pipelines, and natural gas, water and power pipelines coexist in the underground of the city. Because the underground situation is unknown, gas leakage incidents caused by natural gas pipeline rupture occur from time to time, posing a threat to personal safety. Therefore, the motivation of this study is to provide a feasible method to accelerate the aging, renewal and transformation of urban natural gas pipelines to ensure the safe operation of urban natural gas pipeline network and promote the high-quality development of urban economy. Through the combination of experimental test and numerical simulation, this study establishes a database of urban natural gas pipeline flow noise signals, and uses principal component analysis (PCA) to extract the characteristics of flow noise signals, and develops a mathematical model for feature extraction. Then, a classification and recognition model based on backpropagation neural network (BPNN) is constructed, which realizes the detection and recognition of convective noise signals. The research results show that the theoretical method based on acoustic feature analysis provides guidance for the orderly and safe construction of urban natural gas pipeline network and ensures its safe operation. The research conclusion shows that through the simulation analysis of 75 groups of gas pipeline flow noise under different working conditions. Combined with the experimental verification of ground flow noise signals, the feature extraction and pattern recognition method proposed in this study has a recognition accuracy of up to 97% under strong noise background, which confirms the accuracy of numerical simulation and provides theoretical basis and technical support for the detection and recognition of urban gas pipeline flow noise.

Residual networks for text-independent speaker identification: Unleashing the power of residual learning

The human voice, a dynamic signal, conveys valuable information for speaker identification, encompassing gender, age, emotions, and language. In the biometrics industry, identifying voices in real-time amidst diverse accents, tones, and noisy backgrounds is a challenging task. Voice biometry, a complex aspect of speaker identification, is gaining importance in various applications, such as user authentication, attendance systems, forensics, and banking operations, as it eliminates the need for traditional credentials like cards or passwords. Recent advancements in Human–Computer Interaction technology have made conversational tasks technically feasible. Deep Neural Learning approaches, especially Convolutional Deep Neural Networks (CDNN), have emerged as a powerful tool in the field of speech processing, surpassing traditional Speaker Identification methods. This paper introduces a novel approach using 1-Dimensional Convolutional Residual Blocks for audio classification and Speaker Identification, specifically focusing on speaker recognition from spoken Hindi language. The proposed Residual architecture significantly enhances speaker identification, even in low Signal Noise Ratio environments, achieving an impressive accuracy rate of 86.02%. This outperforms traditional Gaussian Mixture Model (GMM) and Feed Forward Back-propagation Network (FFBN) model for the same set of speakers. Future research directions may explore the classification of audio and speaker identification using various acoustic features derived from speech signals.

Quantum-to-Classical Coexistence: Wavefunction Decay Kinetics, Photon Entanglement, and Q-Bits

By utilizing a generalized version of the Madelung quantum hydrodynamic framework that incorporates noise, we derive a solution using the path integral method to investigate how a quantum superposition of states evolves over time. This exploration seeks to comprehend the process through which a stable quantum state emerges when fluctuations induced by the noisy gravitational background are present. The model defines the conditions that give rise to a limited range of interactions for the quantum potential, allowing for the existence of coarse-grained classical descriptions at a macroscopic level. The theory uncovers the smallest attainable level of uncertainty in an open quantum system and examines its consistency with the localized behavior observed in large-scale classical systems. The research delves into connections and similarities alongside other theories such as decoherence and the Copenhagen foundation of quantum mechanics. Additionally, it assesses the potential consequences of wave function decay on the measurement of photon entanglement. To validate the proposed theory, an experiment involving entangled photons transmitted between detectors on the moon and Mars is discussed. Finally, the findings of the theory are applied to the creation of larger Q-bit systems at room temperatures.