Enhancing Computer Digital Signal Processing through the Utilization of RNN Sequence Algorithms

In this paper, alternative direction finding methods have been proposed using deep learning techniques. Firstly, Regeression and Classification models have created by using Convolutional Neural Networks (CNNs). In the second Convolutional Neural Networks and Recurrent Neural Networks (RNNs) have been utilized in the proposed methods. Despite having vast amount of direction finding studies, utilization of neural networks is scarce in literature and past works mostly only includes usage of CNNs. In this study, direction finding is performed by learning signals reaching multiple antenna arrays by networks. Created neural networks have been fed with different data formats and their performances against noisy and no-noise data have been shown. In addition, comparative analysis of the developed models were made in the similar Signal-to-Noise Ratio (SNR) range with the subspace based MUSIC algorithm, which is frequently used in direction finding.

Designing of VehiNet Using Convolutional Neural Networks and Deep Learning Techniques

In this digital era, there is an exponential growth of text-based content in the electronic world. Data as texts exist in the form of documents, social media posts on Facebook, Twitter, etc., logs, sensor data, and emails. Twitter is a social platform where users express their views on various aspects in a day to day life. Twitter produces over 500 million tweets daily that is 6000 tweets per second. Twitter data is, by definition, very noisy and unstructured in nature. Text classifications based on the machine learning techniques have problems like poor generalization ability and sparsity dimension explosion. Classifiers based on deep learning techniques are implemented to improve accuracy to overcome shortcomings of machine learning techniques and to avoid feature extraction processes and have high prediction accuracy and strong learning ability. In this work, the classification of tweets is performed on Covid-19 dataset by implementing deep learning techniques namely Convolution Neural Network (CNN), Recurrent Neural Network (RNN), Recurrent Convolution Neural Network (RCNN), Recurrent Neural Network with Long Short Term Memory (RNN+LSTM), and Bidirectional Long Short Term Memory with Attention (BI-LSTM + Attention). The algorithms are implemented using two-word embedding techniques namely Global Vectors for Word Representation (GloVe) and Word2Vec. RNN with Bidirectional LSTM model has performed better than all the classifiers considered. It has classified the text with an accuracy of 93% and above when used with GloVe and Word2Vec.

The natural language processing (NLP) area has been magically transformed by the Deep Neural Network (DNN). The two variations of Neural Networks, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) can handle the different NLP tasks effectively. CNN is based on feature extraction using N-grams at higher levels. The RNN can be effectively used to model the sequential information. Choosing a neural technique for the various NLP tasks is always a challenge. The paper focuses on the evaluating the performance of the two DNN techniques CNN and RNN for the various NLP tasks, so that appropriate technique can be selected.

The study presents the results of the development and testing of deep learning neural network architectures, which demonstrate high accuracy rates in classifying neurophysiological data, in particular hemodynamic brain activation patterns obtained by functional near-infrared spectroscopy, during solving mathematical problems on spatial-numerical associations. The analyzed signal represents a multidimensional time series of oxyhemoglobin and deoxyhemoglobin dynamics. Taking the specificity of the fNIRS signal into account, a comparative analysis of 2 types of neural network architectures was carried out: (1) architectures based on recurrent neural networks: recurrent neural network with long short-term memory, recurrent neural network with long short-term memory with fully connected layers, bidirectional recurrent neural network with long short-term memory, convolutional recurrent neural network with long short-term memory; (2) architectures based on convolutional neural networks with 1D convolutions: convolutional neural network, fully convolutional neural network, residual neural network. Trained long short-term memory recurrent neural network architectures showed worse results in accuracy in comparison with 1D convolutional neural network architectures. Residual neural network (model_Resnet) demonstrated the highest accuracy rates in three experimental conditions more than 88% in detecting age-related differences in brain activation during spatial-numerical association tasks considering the individual characteristics of the respondents’ signal.

Recently, one of the most common approaches used in speech recognition is deep learning. The most advanced results have been obtained with speech recognition systems created using convolutional neural network (CNN) and recurrent neural networks (RNN). Since CNNs can capture local features effectively, they are applied to tasks with relatively short-term dependencies, such as keyword detection or phoneme- level sequence recognition. This paper presents the development of a deep learning and speech command recognition system. The Google Speech Commands Dataset has been used for training. The dataset contained 65.000 one-second-long words of 30 short English words. That is, %80 of the dataset has been used in the training and %20 of the dataset has been used in the testing. The data set consists of one-second voice commands that have been converted into a spectrogram and used to train different artificial neural network (ANN) models. Various variants of CNN are used in deep learning applications. The performance of the proposed model has reached %94.60.

Joint variable frame rate and length analysis for speech recognition under adverse conditions

A trademark may be a word, phrase, symbol, sound, color, scent or design, or combination of these, that identifies and distinguishes the goods or services of a particular source from those of others. Obtaining a trademark is a complex, time intensive and costly process that involves varied steps before the trademark can be registered including searching prior trademarks, filing of the trademark application, review of the trademark application and final publication for opposition by the public. One of the crucial aspect in the review of the trademark application is determining the international classes of the filed marks based on their goods and services description. Currently, the process of identifying the international classes of a filed mark is performed manually in the United States Patent and Trademark Office (USPTO) and takes significant amount of time. Recently, word embeddings and deep neural networks (DNNs) have revolutionized the field of computer vision and demonstrated excellent performance in various natural language processing (NLP) tasks such as machine translation, speech recognition, sentence and document classification etc. to name a few. In this study, we explored fastText and different neural networks such as Convolution Neural Networks (CNN), Long Short Term Memory (LSTM), bidirectional versions of both LSTM and Gated Recurrent Unit (GRU) and Recurrent Convolutional Neural Network (RCNN) to automate the international class identification for the filed marks based on their goods and services description. Overall, it was found that the trademark word embeddings with RCNN model outperformed the other models. Our study thereby seeks to provide a solution towards the manual, time intensive and laborious process of identifying international classes for the trademarks.

Users of hearing aid devices encounter great difficulty in speech recognition in noisy environments, and show inferior performance in situations with interfering background noise or competing speakers as compared to the case of a quiet background. In such devices, a pre‐processing stage, which first removes the background interference, should greatly boost the speech recognition performance in noisy environments. In this work, the performance of a previously reported speech extraction algorithm is investigated for the purpose of improving the speech intelligibility of hearing aid users. Recently, we performed segregation of speech mixtures using our speech extraction algorithm and showed improvement in the speech recognition performance of normal hearing listeners. In this work, we investigate the performance of our algorithm for users of hearing aids on the same perceptual task of recognizing speech in the presence of either interfering speech or noise. We focus on tuning the segregation algorithm for hearing aid users, and emphasize on the preservation of the appropriate envelope and fine structure cues which will prove most helpful in the recognition of the target speech signal.

Deep learning (also known as deep structured learning, hierarchical learning, or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and nonlinear transformations. Deep learning has been characterized as a class of machine learning algorithms with the following characteristics [257]: • They use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern analysis (unsupervised) and classification (supervised). • They are based on the (unsupervised) learning of multiple levels of features or representations of the data. Higher-level features are derived from lower-level features to form a hierarchical representation. • They are part of the broader machine learning field of learning representations of data. • They learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts. These definitions have in common: multiple layers of nonlinear processing units and the supervised or unsupervised learning of feature representations in each layer, with the layers forming a hierarchy from low-level to high-level features. Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks (DBN), and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition, and bioinformatics where they have been shown to produce state-of-the-art results on various tasks. In this chapter we start in Section 7.1 with an introduction, giving a brief history of this field, the relevant literature, and its applications. Then we study some basic concepts of deep learning such as convolutional neural networks, recurrent neural networks, backpropagation algorithm, restricted Boltzmann machines, and deep learning networks in Section 7.2. Then we illustrate three examples for Apache Spark implementation for mobile big data (MBD), user moving pattern extraction, and combination with nonparametric Bayesian learning, respectively, in Sections 7.3 through 7.5. Finally, we have summary in Section 7.6.

Audio-visual speech recognition based on joint training with audio-visual speech enhancement for robust speech recognition

Deep learning methodologies have had a major impact on performance across a wide variety of machine learning tasks, and speech recognition is no exception. We describe a set of deep learning techniques that proved to be particularly successful in achieving performance gains in word error rate on a popular large vocabulary conversational speech recognition benchmark task (“Switchboard”). We found that the best performance is achieved by combining features from both recurrent and convolutional neural networks. We compare two recurrent architectures: partially unfolded nets with max-out activations and bidirectional long short-term memory nets. In addition, inspired by the success of convolutional networks for image classification, we designed a convolutional net with many convolutional layers and small kernels that create a receptive field with more nonlinearity and fewer parameters than standard configurations. When combined, these neural networks achieve a word error rate of 6.2% on this difficult task; this was the best reported rate at the time of this writing and is even more remarkable given that human performance itself is estimated to be 4% on this data.

With the huge success of applying deep learning (DL) methodologies to image recognition and natural language processing in recent years, researchers are now keen to use them in the machine condition monitoring (MCM) context. There are numerous papers in applying various DL techniques, such as auto-encoder, restricted Boltzmann machine, convolutional neural network and recurrent neural network, etc., to MCM problems ranging from component-level condition monitoring (machine tool wear prediction, bearing fault diagnosis and classification and hydraulic pump fault diagnosis) to system-level health management (aircraft and spacecraft diagnosis). In this paper, we give a brief overview in the area of DL for MCM with a focus on reviewing the most recent papers published since 2019. In Part 1, we present some critical views regarding whether any breakthrough has been achieved from an MCM domain expert perspective, with the main conclusion that DL has great potential for MCM applications, and a major breakthrough could come soon since the shortfalls lie more in data than in the DL methodologies. Our overall impression is that (a) DL models are not really showing their great potentials with only a small training data; (b) faulty-condition data is hard to come by for training DL, but normal condition data is abundant, so anomaly detection makes more sense; (c) applying DL only to the Case Western Reserve University (CWRU) bearing fault dataset is not sufficient for real world industrial applications as it was from a very simple test rig, and applying DL to data from complex systems like helicopter gearbox data may deliver much more convincing results. In Part 2, we enhance the main conclusion of the critical review with supplement views and a case study on analysing Bell-206B helicopter main gearbox planet bearing failure data using some traditional MCM techniques in contrast to applying the long short-term memory (LSTM) DL method. We can conclude from the case study that the DL-based methods are not necessarily always superior to the traditional MCM techniques for dataset from moderately complex machinery.

This paper proposes a novel deep learning architecture involving combinations of Convolutional Neural Networks (CNN) layers and Recurrent neural networks (RNN) layers that can be used to perform segmentation and classification of 5 cardiac rhythms based on ECG recordings. The algorithm is developed in a sequence to sequence setting where the input is a sequence of five second ECG signal sliding windows and the output is a sequence of cardiac rhythm labels. The novel architecture processes as input both the spectrograms of the ECG signal as well as the heartbeats' signal waveform. Additionally, we are able to train the model in the presence of label noise. The model's performance and generalizability is verified on an external database different from the one we used to train. Experimental result shows this approach can achieve an average F1 scores of 0.89 (averaged across 5 classes). The proposed model also achieves comparable classification performance to existing state-of-the-art approach with considerably less number of training parameters.

The advent of artificial intelligence (AI) has revolutionized human-computer interaction, making it more natural and intuitive. This research paper presents the development and implementation of an AI Desktop Voice Assistant designed to enhance productivity and accessibility for users. The voice assistant leverages advanced speech recognition, natural language processing (NLP), and machine learning techniques to understand and execute user commands. Key functionalities include voice-activated application control, web searches, personalized reminders, and real-time information retrieval. Our system integrates with widely-used APIs and services, providing a seamless user experience across various tasks. The paper discusses the architecture of the voice assistant, detailing the integration of components such as the speech recognition engine, NLP models, and the dialogue management system. We explore the challenges encountered during development, including accurate speech recognition in noisy environments and handling ambiguous user commands. Solutions implemented to address these challenges are also presented. Furthermore, we conduct a usability study to evaluate the effectiveness and user satisfaction of the voice assistant. The results indicate a high level of user engagement and satisfaction, demonstrating the practical benefits of incorporating AI-driven voice interfaces into desktop environments. Our findings contribute to the ongoing research in human-computer interaction, suggesting pathways for future enhancements in AI voice assistants..