scholarly journals Automatic Synthesis Technology of Music Teaching Melodies Based on Recurrent Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yingxue Zhang ◽  
Zhe Li
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Music Teaching ◽  
Acoustic Features ◽  
Dynamic Features ◽  
Automatic Synthesis ◽  
Short Delay ◽  
Music Score ◽  
Proposed Model ◽  
Score Model

Computer music creation boasts broad application prospects. It generally relies on artificial intelligence (AI) and machine learning (ML) to generate the music score that matches the original mono-symbol score model or memorize/recognize the rhythms and beats of the music. However, there are very few music melody synthesis models based on artificial neural networks (ANNs). Some ANN-based models cannot adapt to the transposition invariance of original rhythm training set. To overcome the defect, this paper tries to develop an automatic synthesis technology of music teaching melodies based on recurrent neural network (RNN). Firstly, a strategy was proposed to extract the acoustic features from music melody. Next, the sequence-sequence model was adopted to synthetize general music melodies. After that, an RNN was established to synthetize music melody with singing melody, such as to find the suitable singing segments for the music melody in teaching scenario. The RNN can synthetize music melody with a short delay solely based on static acoustic features, eliminating the need for dynamic features. The proposed model was proved valid through experiments.

scholarly journals Tonal Contour Generation for Isarn Speech Synthesis Using Deep Learning and Sampling-Based F0 Representation

2020 ◽  
Vol 10 (18) ◽  
pp. 6381 ◽  
Author(s):  
Pongsathon Janyoi ◽  
Pusadee Seresangtakul
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Speech Synthesis ◽  
Critical Factor ◽  
Dynamic Features ◽  
Linguistic Features ◽  
Proposed Model ◽  
F0 Contour ◽  
Tonal Contour

The modeling of fundamental frequency (F0) in speech synthesis is a critical factor affecting the intelligibility and naturalness of synthesized speech. In this paper, we focus on improving the modeling of F0 for Isarn speech synthesis. We propose the F0 model for this based on a recurrent neural network (RNN). Sampled values of F0 are used at the syllable level of continuous Isarn speech combined with their dynamic features to represent supra-segmental properties of the F0 contour. Different architectures of the deep RNNs and different combinations of linguistic features are analyzed to obtain conditions for the best performance. To assess the proposed method, we compared it with several RNN-based baselines. The results of objective and subjective tests indicate that the proposed model significantly outperformed the baseline RNN model that predicts values of F0 at the frame level, and the baseline RNN model that represents the F0 contours of syllables by using discrete cosine transform.

scholarly journals Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Jianlei Zhang ◽  
Yukun Zeng ◽  
Binil Starly
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Machine Tool ◽  
Recurrent Neural Network ◽  
Machine Tools ◽  
Prediction Performance ◽  
Sequential Data ◽  
Diagnosis And Prognosis ◽  
Proposed Model

AbstractData-driven approaches for machine tool wear diagnosis and prognosis are gaining attention in the past few years. The goal of our study is to advance the adaptability, flexibility, prediction performance, and prediction horizon for online monitoring and prediction. This paper proposes the use of a recent deep learning method, based on Gated Recurrent Neural Network architecture, including Long Short Term Memory (LSTM), which try to captures long-term dependencies than regular Recurrent Neural Network method for modeling sequential data, and also the mechanism to realize the online diagnosis and prognosis and remaining useful life (RUL) prediction with indirect measurement collected during the manufacturing process. Existing models are usually tool-specific and can hardly be generalized to other scenarios such as for different tools or operating environments. Different from current methods, the proposed model requires no prior knowledge about the system and thus can be generalized to different scenarios and machine tools. With inherent memory units, the proposed model can also capture long-term dependencies while learning from sequential data such as those collected by condition monitoring sensors, which means it can be accommodated to machine tools with varying life and increase the prediction performance. To prove the validity of the proposed approach, we conducted multiple experiments on a milling machine cutting tool and applied the model for online diagnosis and RUL prediction. Without loss of generality, we incorporate a system transition function and system observation function into the neural net and trained it with signal data from a minimally intrusive vibration sensor. The experiment results showed that our LSTM-based model achieved the best overall accuracy among other methods, with a minimal Mean Square Error (MSE) for tool wear prediction and RUL prediction respectively.

A recurrent neural network-based predictive model for pressure ulcers based on an intensive care unit dataset (MIMIC-III) (Preprint)

2021 ◽  
Author(s):  
Minseop Park ◽  
Hyeok Choi ◽  
Hee-Sung Ahn ◽  
Hee-Ju Kang ◽  
Saehoon Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Dynamic Features ◽  
Braden Scale ◽  
Mlp Model ◽  
Mimic Iii

BACKGROUND A pressure ulcer (PU) is a localized cutaneous injury caused by pressure or shear, which usually occurs in the region of a bony prominence. PUs are common in hospitalized patients and cause complications including infection. OBJECTIVE This study aimed to build a recurrent neural network-based algorithm to predict PUs 24 hours before their occurrence. METHODS This study analyzed a freely accessible intensive care unit (ICU) dataset, MIMIC- III. Deep learning and machine learning algorithms including long short-term memory (LSTM), multilayer perceptron (MLP), and XGBoost were applied to 37 dynamic features (including the Braden scale, vital signs and laboratory results, and interventions to reduce the risk of PUs) and 35 static features (including the length of time spent in the ICU, demographics, and comorbidities). Their outcomes were compared in terms of the area under the receiver operating characteristic (AUROC) and the area under the precision-recall curve (AUPRC). RESULTS A total of 1,048 cases of PUs (10.0%) and 9,402 controls (90.0%) without PUs satisfied the inclusion criteria for analysis. The LSTM + MLP model (AUROC: 0.7929 ± 0.0095, AUPRC: 0.4819 ± 0.0109) outperformed the other models, namely: MLP model (AUROC: 0.7777 ± 0.0083, AUPRC: 0.4527 ± 0.0195) and XGBoost (AUROC: 0.7465 ± 0.0087, AUPRC: 0.4052 ± 0.0087). Various features, including the length of time spent in the ICU, Glasgow coma scale, and the Braden scale, contributed to the prediction model. CONCLUSIONS This study suggests that recurrent neural network-based algorithms such as LSTM can be applied to evaluate the risk of PUs in ICU patients.

Automatic Segmentation and Classification of COVID-19 CT Image Using Deep Learning and Multi-Scale Recurrent Neural Network Based Classifier

2021 ◽  
Vol 11 (10) ◽  
pp. 2618-2625
Author(s):  
R. T. Subhalakshmi ◽  
S. Appavu Alias Balamurugan ◽  
S. Sasikala
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Superior Performance ◽  
Multi Scale ◽  
Proposed Model ◽  
Class Labels

In recent times, the COVID-19 epidemic turn out to be increased in an extreme manner, by the accessibility of an inadequate amount of rapid testing kits. Consequently, it is essential to develop the automated techniques for Covid-19 detection to recognize the existence of disease from the radiological images. The most ordinary symptoms of COVID-19 are sore throat, fever, and dry cough. Symptoms are able to progress to a rigorous type of pneumonia with serious impediment. As medical imaging is not recommended currently in Canada for crucial COVID-19 diagnosis, systems of computer-aided diagnosis might aid in early COVID-19 abnormalities detection and help out to observe the disease progression, reduce mortality rates potentially. In this approach, a deep learning based design for feature extraction and classification is employed for automatic COVID-19 diagnosis from computed tomography (CT) images. The proposed model operates on three main processes based pre-processing, feature extraction, and classification. The proposed design incorporates the fusion of deep features using GoogLe Net models. Finally, Multi-scale Recurrent Neural network (RNN) based classifier is applied for identifying and classifying the test CT images into distinct class labels. The experimental validation of the proposed model takes place using open-source COVID-CT dataset, which comprises a total of 760 CT images. The experimental outcome defined the superior performance with the maximum sensitivity, specificity, and accuracy.

A Robust False Spam Review Detection Using Deep Long Short-Term Memory (LSTM) Based Recurrent Neural Network

2020 ◽  
Vol 17 (8) ◽  
pp. 3421-3426
Author(s):  
D. Deva Hema ◽  
J. Tharun ◽  
G. Arun Dev ◽  
N. Sateesh
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Online Reviews ◽  
Physical Contact ◽  
Proposed Model ◽  
Deep Recurrent Neural Network ◽  
Bayes Algorithm ◽  
Fake Reviews

Our day-to-day activity is highly influenced by development of Internet. One of the rapid growing area in Internet is E-commerce. People are eager to buy products from online sites like Amazon, embay, Flipkart etc. Customers can write reviews about the products purchased online. The purchasing of good through online has been increasing exponentially since last few years. As there is no physical contact with goods before purchasing through online, people totally rely on reviews about the product before purchasing it. Hence review plays an important role in deciding the quality of the product. There are many customers who give online reviews about the product after using it. Hence the quality of the product is decided by the reviews of the customers. Thus, detection of fake reviews has become one of the important task. The proposed system will help in finding such fake reviews about the product, so that the fake reviews can be eliminated. Therefore, the purchasing of the products will be totally based on the genuine reviews. The proposed system uses Deep Recurrent Neural Network (DRNN) to predict the fake reviews and the performance of the proposed method has compared with Naïve Bayes Algorithm. The proposed model shows good accuracy and can handle huge amount of data over the existing system.

scholarly journals Hybrid deep learning model using recurrent neural network and gated recurrent unit for heart disease prediction

2021 ◽  
Vol 11 (6) ◽  
pp. 5467
Author(s):  
Surenthiran Krishnan ◽  
Pritheega Magalingam ◽  
Roslina Ibrahim
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Learning Model ◽  
Disease Prediction ◽  
The Neural Network ◽  
Proposed Model ◽  
Deep Learning Model

<span>This paper proposes a new hybrid deep learning model for heart disease prediction using recurrent neural network (RNN) with the combination of multiple gated recurrent units (GRU), long short-term memory (LSTM) and Adam optimizer. This proposed model resulted in an outstanding accuracy of 98.6876% which is the highest in the existing model of RNN. The model was developed in Python 3.7 by integrating RNN in multiple GRU that operates in Keras and Tensorflow as the backend for deep learning process, supported by various Python libraries. The recent existing models using RNN have reached an accuracy of 98.23% and deep neural network (DNN) has reached 98.5%. The common drawbacks of the existing models are low accuracy due to the complex build-up of the neural network, high number of neurons with redundancy in the neural network model and imbalance datasets of Cleveland. Experiments were conducted with various customized model, where results showed that the proposed model using RNN and multiple GRU with synthetic minority oversampling technique (SMOTe) has reached the best performance level. This is the highest accuracy result for RNN using Cleveland datasets and much promising for making an early heart disease prediction for the patients.</span>

scholarly journals Attention-Based Long Short-Term Memory Recurrent Neural Network for Capacity Degradation of Lithium-Ion Batteries

Batteries ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. 66
Author(s):  
Tadele Mamo ◽  
Fu-Kwun Wang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Lithium Ion ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Capacity Degradation ◽  
Long Short Term Memory

Monitoring cycle life can provide a prediction of the remaining battery life. To improve the prediction accuracy of lithium-ion battery capacity degradation, we propose a hybrid long short-term memory recurrent neural network model with an attention mechanism. The hyper-parameters of the proposed model are also optimized by a differential evolution algorithm. Using public battery datasets, the proposed model is compared to some published models, and it gives better prediction performance in terms of mean absolute percentage error and root mean square error. In addition, the proposed model can achieve higher prediction accuracy of battery end of life.

scholarly journals Low Latency Based Convolutional Recurrent Neural Network Model for Speech Command Recognition

2021 ◽  
Vol 50 (4) ◽  
pp. 656-673
Author(s):  
Chhayarani Ram Kinkar ◽  
Yogendra Kumar Jain
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Resource Constraints ◽  
Recognition Accuracy ◽  
Limited Resources ◽  
Low Latency ◽  
Model Parameters ◽  
Memory Requirement ◽  
Proposed Model

The presented paper proposes a new speech command recognition model for novel engineering applications with limited resources. We built the proposed model with the help of a Convolutional Recurrent Neural Network (CRNN). The use of CRNN instead of Convolutional Neural Network (CNN) helps us to reduce the model parameters and memory requirement as per resource constraints. Furthermore, we insert transmute and curtailment layer between the layers of CRNN. By doing this we further reduce model parameters and float number of operations to half of the CRNN requirement. The proposed model is tested on Google’s speech command dataset. The obtained result shows that the proposed CRNN model requires 1/3 parameters as compared to the CNN model. The number of parameters of the CRNN model is further reduced by 45% and the float numbers of operations between 2% to 12 % in different recognition tasks. The recognition accuracy of the proposed model is 96% on Google’s speech command dataset, and on laboratory recording, its recognition accuracy is 89%.

scholarly journals Nonlinear Autoregressive Recurrent Neural Network Model for Quality of Service Prediction

2020 ◽  
Vol 8 (6) ◽  
pp. 4762-4770
Keyword(s):  
Neural Network ◽  
Quality Of Service ◽  
Recurrent Neural Network ◽  
Prediction System ◽  
Qos Prediction ◽  
Proposed Model ◽  
Routing Monitoring ◽  
The Given ◽  
Nonlinear Autoregressive

Due to the advances in computer networks, Internet and multimedia communications, Quality of Service (QoS) monitoring and assessment become an increasingly important. The importance of assessing QoS stems from the fact it may reflect the resources availability of a network that may provide solutions for QoS provision, routing, monitoring, management … etc. In the literature, several monitoring and measurement approached and methods have been developed to quantify and predict the QoS of multimedia applications transmitted over such networks. In this research, a new QoS prediction system will be designed. The proposed system is based on using the Nonlinear Autoregressive with eXogenous input model (NARX) using recurrent neural network. This prediction system uses in addition to the QoS parameters; previous measured QoS values will used as inputs to this model. The expected output of this new model is the forecasted QoS. The proposed model will be trained, tested, validated and then optimized to provide a good estimate of the QoS provided by the given network. Simulation results are expected to show that the proposed model will have high accurate QoS prediction capabilities compared to other QoS assessment systems adopted in the literature.

scholarly journals Intelligent Content Conciser using Pointer Generated Network

2019 ◽  
Vol 8 (4) ◽  
pp. 11810-11814
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Recurrent Neural Network ◽  
End Users ◽  
Text Summarization ◽  
Document Similarity ◽  
Training Activity ◽  
Working Model ◽  
Proposed Model ◽  
Efficient Learning

The combination of best suited architecture and successful algorithm results in the increased nature of efficient learning among the end users. To increase the number of quality learner’s text summarization provides the best initiative among the readers and learners. As words and sentences comprise a document, document summarization finds diverse words with different sets of synonyms by performing training activity for the process. The S2S(Sequence to Sequence) training mechanism describes the embedding way of sentences and documents. The pointer generation enhances the new hybrid model for summary extraction. The proposed model implements attention mechanism and uses Recurrent Neural Network with LSTM cells at encoder and decoder. The working model focuses on many factors for summary extraction such as sentence/document similarity, repeatedness, indexing and sentence-context richness. It also keeps track of summarized text using coverage to avoid repetition

Sign in / Sign up

Export Citation Format

Share Document