Feature selection using singular value decomposition and QR factorization with column pivoting for text-independent speaker identification

Detection of replay spoof speech using teager energy feature cues

Speech is one of the most sophisticated human motor skills. Speaker identification is the ability of a software component or hardware to acquire a speech signal, recognize the speakers included in the signal, and identify the speaker after the audio signals have been received. This study proposes a fluctuating equation inversion method using feature extraction for broadcast hosting. Feature extraction aims to provide useful signal features from natural audio that can be applied to various downstream processes, including recitation, evaluation, and categorization. Initially, data were collected from the CASIA dataset. This study evaluated the experimental outcomes of the proposed approach using mel-frequency cepstral coefficients, gammatone frequency cepstral coefficients, and linear frequency cepstral coefficients. The proposed technique was tested on a publicly accessible dataset, and the findings showed that it performed better in terms of recognition accuracy (98%), precision (97%), recall (96.05%), sensitivity (92.56%), and F1-score (95.09%) than the conventional feature extraction methods. The proposed approach can be utilized to improve audio signal quality and user experience across broadcast-hosting applications.

Drone classification using RF signal based spectral features

This paper reports some of the observations carried out on SUSE database for emotion classification. A comparative study is made to evaluate the performance of Linear Prediction Cepstral Coefficients (LPCCs) and Mel Frequency Cepstral Coefficients (MFCCs) for designing the emotion classification system for word level utterances. The significance of the orders of the coefficients has been carried out during this study. The results obtained using 12th order LPCC and 13th order MFCC are compared with respect to their reduced dimensions of lower orders. A new classification system based on the feature extraction technique using the 2nd, 3rd and 4th order coefficients of both MFCCs and LPCCs is also proposed. This paper compares the accuracy level of both MFCC and LPCC, enabling us to decide which orders of the parameters (both MFCC and LPCC) are more efficient in conveying the emotion for word level utterances. The initial experiments performed at word level utterances reveal that LPCC is more efficient in detecting emotion as compared to MFCC. Further, we noticed that the emotions conveyed in word level utterances are detected more accurately than that in sentence level utterances. The result suggests that word level approach provides better performance for emotion classification as compared to sentence level approach if the system is designed using vocal tract information only.

This paper presents an audio genre detection framework that can be used for a multi-language audio corpus. Cepstral coefficients are considered and analyzed as the feature set for both a language dependent and language independent genre identification (GID) task. Language information is found to increase the overall detection accuracy on an average by at least 2.6% from its language independent counterpart. Melfrequency cepstral coefficients have been widely used for Music Information Retrieval (MIR), however, the present study shows that Linear-frequency cepstral coefficients (LFCC) with a higher number of frequency bands can improve the detection accuracy. Two other GID architectures have also been considered, but the results show that the logenergy amplitudes from triangular linearly spaced filter banks and their deltas can offer average detection accuracy as high as 98.2%, when language information is taken into account.

Chapter 3 - Acoustic identification of insects based on cepstral data fusion and hidden Markov models

Acoustic vocalizations are common in marine mammals which can be used for classification purposes. Pinnipeds are a group of carnivore mammals composed by seals, sea lions, and walruses. But although, there is a great interest in research literature about acoustic monitoring of marine mammals, the identification of pinnipeds trough experts systems has been poorly studied. This paper brings a novel method for the automatic taxonomic classification of pinnipeds using a fusion of Mel and Linear Frequency Cepstral Coefficients (MFCCs and LFCCs), representing the acoustic signal in both low and high frequencies. In our experiment, we have used kNN for classification, achieving an average identification of 96.48% ± 9.17 over 18 pinniped species.

Kärntner Montanindustrie appoints Albion as UK agent for micaceous iron oxide, wollastonite & mica

The aim of the study was to inspect the acoustic properties and sleep characteristics of a preapneic snoring sound. The feasibility of forecasting upcoming respiratory events by snoring sound was also investigated. Participants with habitual snoring or a heavy breathing sound during sleep were recruited consecutively. Polysomnography was conducted, and snoring-related breathing sound was recorded simultaneously. Acoustic features and sleep features were extracted from 30-second samples, and a machine learning algorithm was used to establish 2 prediction models. A total of 74 eligible participants were included. Model 1, tested by 5-fold cross-validation, achieved an accuracy of 0.92 and an area under the curve of 0.94 for respiratory event prediction. Model 2, with acoustic features and sleep information tested by Leave-One-Out cross-validation, had an accuracy of 0.78 and an area under the curve of 0.80. Sleep position was found to be the most important among all sleep features contributing to the performance of the 2 models. Preapneic sound presented unique acoustic characteristics, and snoring-related breathing sound could be deployed as a real-time apneic event predictor. The models, combined with sleep information, serve as a promising tool for an early warning system to forecast apneic events. Wang B, Yi X, Gao J, etal. Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal. J Clin Sleep Med. 2021;17(9):1777-1784.

The proliferation of deepfake technologies presents serious challenges for forensic speech authentication. We propose a deep learning framework combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to improve detection of manipulated audio. Leveraging the spectral feature extraction of CNNs and the temporal modeling of LSTMs, the model demonstrates superior accuracy and generalization across the ASVspoof2019 LA and WaveFake datasets. Linear Frequency Cepstral Coefficients (LFCCs) were employed as acoustic features and outperformed MFCC and GFCC representations. To enhance transparency and trustworthiness, explainable artificial intelligence (XAI) techniques, including Grad-CAM and SHAP, were applied, revealing that the model focuses on high-frequency artifacts and temporal inconsistencies. These interpretable analyses validate both the models design and the forensic relevance of LFCC features. The proposed approach thus provides a robust, interpretable, and XAI-driven solution for forensic authentic detection.

This report presents an approach to predict the credit scores of customers using the Logistic Regression machine learning algorithm. The research objective of this project is to perform a comparative study between feature selection and feature extraction, against the same dataset using the Logistic Regression machine learning algorithm. For feature selection, we have used Stepwise Logistic Regression. For feature extraction, we have used Singular Value Decomposition (SVD) and Weighted Singular Value Decomposition (SVD). In order to test the accuracy obtained using feature selection and feature extraction, we used a public credit dataset having 11 features and 150,000 records. After performing feature reduction, Logistic Regression algorithm was used for classification. In our results, we observed that Stepwise Logistic Regression gave a 14% increase in accuracy as compared to Singular Value Decomposition (SVD) and a 10% increase in accuracy as compared to Weighted Singular Value Decomposition (SVD). Thus, we can conclude that Stepwise Logistic Regression performed significantly better than both Singular Value Decomposition (SVD) and Weighted Singular Value Decomposition (SVD). The benefit of using feature selection was that it helped us in identifying important features, which improved the prediction accuracy of the classifier.

<p>Classification problems often have a large number of features, but not all of them are useful for classification. Irrelevant and redundant features may even reduce the classification accuracy. Feature selection is a process of selecting a subset of relevant features, which can decrease the dimensionality, shorten the running time, and/or improve the classification accuracy. There are two types of feature selection approaches, i.e. wrapper and filter approaches. Their main difference is that wrappers use a classification algorithm to evaluate the goodness of the features during the feature selection process while filters are independent of any classification algorithm. Feature selection is a difficult task because of feature interactions and the large search space. Existing feature selection methods suffer from different problems, such as stagnation in local optima and high computational cost. Evolutionary computation (EC) techniques are well-known global search algorithms. Particle swarm optimisation (PSO) is an EC technique that is computationally less expensive and can converge faster than other methods. PSO has been successfully applied to many areas, but its potential for feature selection has not been fully investigated. The overall goal of this thesis is to investigate and improve the capability of PSO for feature selection to select a smaller number of features and achieve similar or better classification performance than using all features. This thesis investigates the use of PSO for both wrapper and filter, and for both single objective and multi-objective feature selection, and also investigates the differences between wrappers and filters. This thesis proposes a new PSO based wrapper, single objective feature selection approach by developing new initialisation and updating mechanisms. The results show that by considering the number of features in the initialisation and updating procedures, the new algorithm can improve the classification performance, reduce the number of features and decrease computational time. This thesis develops the first PSO based wrapper multi-objective feature selection approach, which aims to maximise the classification accuracy and simultaneously minimise the number of features. The results show that the proposed multi-objective algorithm can obtain more and better feature subsets than single objective algorithms, and outperform other well-known EC based multi-objective feature selection algorithms. This thesis develops a filter, single objective feature selection approach based on PSO and information theory. Two measures are proposed to evaluate the relevance of the selected features based on each pair of features and a group of features, respectively. The results show that PSO and information based algorithms can successfully address feature selection tasks. The group based method achieves higher classification accuracies, but the pair based method is faster and selects smaller feature subsets. This thesis proposes the first PSO based multi-objective filter feature selection approach using information based measures. This work is also the first work using other two well-known multi-objective EC algorithms in filter feature selection, which are also used to compare the performance of the PSO based approach. The results show that the PSO based multiobjective filter approach can successfully address feature selection problems, outperform single objective filter algorithms and achieve better classification performance than other multi-objective algorithms. This thesis investigates the difference between wrapper and filter approaches in terms of the classification performance and computational time, and also examines the generality of wrappers. The results show that wrappers generally achieve better or similar classification performance than filters, but do not always need longer computational time than filters. The results also show that wrappers built with simple classification algorithms can be general to other classification algorithms.</p>

<p>Classification aims to identify a class label of an instance according to the information from its characteristics or features. Unfortunately, many classification problems have a large feature set containing irrelevant and redundant features, which reduce the classification performance. In order to address the above problem, feature selection is proposed to select a small subset of relevant features. There are three main types of feature selection methods, i.e. wrapper, embedded and filter approaches. Wrappers use a classification algorithm to evaluate candidate feature subsets. In embedded approaches, the selection process is embedded in the training process of a classification algorithm. Different from the other two approaches, filters do not involve any classification algorithm during the selection process. Feature selection is an important process but it is not an easy task due to its large search space and complex feature interactions. Because of the potential global search ability, Evolutionary Computation (EC), especially Particle Swarm Optimization (PSO), has been widely and successfully applied to feature selection. However, there is potential to improve the effectiveness and efficiency of EC-based feature selection. The overall goal of this thesis is to investigate and improve the capability of EC for feature selection to select small feature subsets while maintaining or even improving the classification performance compared to using all features. Different aspects of feature selection are considered in this thesis such as the number of objectives (single-objective/multi-objective), the fitness function (filter/wrapper), and the searching mechanism. This thesis introduces a new fitness function based on mutual information which is calculated by an estimation approach instead of the traditional counting approach. Results show that the estimation approach works well on both continuous and discrete data. More importantly, mutual information calculated by the estimation approach can capture feature interactions better than the traditional counting approach. This thesis develops a novel binary PSO algorithm, which is the first work to redefine some core concepts of PSO such as velocity and momentum to suit the characteristics of binary search spaces. Experimental results show that the proposed binary PSO algorithm evolve better solutions than other binary EC algorithms when the search spaces are large and complex. Specifically, on feature selection, the proposed binary PSO algorithm can select smaller feature subsets with similar or better classification accuracies, especially when there are a large number of features. This thesis proposes surrogate models for wrapper-based feature selection. The surrogate models use surrogate training sets which are subsets of informative instances selected from the training set. Experimental results show that the proposed surrogate models assist PSO to reduce the computational cost while maintaining or even improving the classification performance compared to using only the original training set. The thesis develops the first wrapper-based multi-objective feature selection algorithm using MOEA/D. A new decomposition strategy using multiple reference points for MOEA/D is designed, which can deal with different characteristics of multi-objective feature selection such as highly discontinuous Pareto fronts and complex relationships between objectives. The experimental results show that the proposed algorithm can evolve more diverse non-dominated sets than other multi-objective algorithms. This thesis introduces the first PSO-based feature selection algorithm for transfer learning. In the proposed algorithm, the fitness function uses classification performance to reduce the differences between domains while maintaining the discriminative ability on the target domain. The experimental results show that the proposed algorithm can select feature subsets which achieve better classification performance than four state-of-the-art feature-based transfer learning algorithms.</p>

Feature selection (FS) is a very important pre-processing technique in machine learning and data mining. It aims to select a small subset of relevant and informative features from the original feature space that may contain many irrelevant, redundant and noisy features. Feature selection usually leads to better performance, interpretability, and lower computational cost. In the literature, FS methods are categorized into three main approaches: Filters, Wrappers, and Embedded. In this paper we introduce a new feature selection method called graph feature selection (GFS). The main steps of GFS are the following: first, we create a weighted graph where each node corresponds to each feature and the weight between two nodes is computed using a matrix of individual and pairwise score of a Decision tree classifier. Second, at each iteration, we split the graph into two random partitions having the same number of nodes, then we keep moving the worst node from one partition to another until the global modularity is converged. Third, from the final best partition, we select the best ranked features according to a new proposed variable importance criterion. The results of GFS are compared to three well-known feature selection algorithms using nine benchmarking datasets. The proposed method shows its ability and effectiveness at identifying the most informative feature subset.

Feature Selection (FS) is currently a very important and prominent research area. The focus of FS is to identify and to remove irrelevant and redundant features from large data sets in order to reduced processing time and to improve the predictive ability of the algorithms. Thus, this work presents a straightforward and efficient FS method based on the mean ratio of the attributes (features) associated with each class. The proposed filtering method, here called MRFS (Mean Ratio Feature Selection), has only equations with low computational cost and with basic mathematical operations such as addition, division, and comparison. Initially, in the MRFS method, the average from the data sets associated with the different outputs is computed for each attribute. Then, the calculation of the ratio between the averages extracted from each attribute is performed. Finally, the attributes are ordered based on the mean ratio, from the smallest to the largest value. The attributes that have the lowest values are more relevant to the classification algorithms. The proposed method is evaluated and compared with three state-of-the-art methods in classification using four classifiers and ten data sets. Computational experiments and their comparisons against other feature selection methods show that MRFS is accurate and that it is a promising alternative in classification tasks.