scholarly journals Music Composition and Emotion Recognition Using Big Data Technology and Neural Network Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yu Wang
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Emotion Recognition ◽  
Frequency Domain ◽  
Music Composition ◽  
Feature Recognition ◽  
Short Term Memory ◽  
Learning Ability ◽  
Time Frequency ◽  
Big Data Technology

To implement a mature music composition model for Chinese users, this paper analyzes the music composition and emotion recognition of composition content through big data technology and Neural Network (NN) algorithm. First, through a brief analysis of the current music composition style, a new Music Composition Neural Network (MCNN) structure is proposed, which adjusts the probability distribution of the Long Short-Term Memory (LSTM) generation network by constructing a reasonable Reward function. Meanwhile, the rules of music theory are used to restrict the generation of music style and realize the intelligent generation of specific style music. Afterward, the generated music composition signal is analyzed from the time-frequency domain, frequency domain, nonlinearity, and time domain. Finally, the emotion feature recognition and extraction of music composition content are realized. Experiments show that: when the iteration times of the function increase, the number of weight parameter adjustments and learning ability will increase, and thus the accuracy of the model for music composition can be greatly improved. Meanwhile, when the iteration times increases, the loss function will decrease slowly. Moreover, the music composition generated through the proposed model includes the following four aspects: sadness, joy, loneliness, and relaxation. The research results can promote music composition intellectualization and impacts traditional music composition mode.

scholarly journals Spatiotemporal Monitoring and Evaluation Method for Sand-Filling of Immersed Tube Tunnel Foundation

2021 ◽  
Vol 11 (3) ◽  
pp. 1084
Author(s):  
Peng Wu ◽  
Ailan Che
Keyword(s):  
Neural Network ◽  
Elastic Wave ◽  
Frequency Domain ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Monitoring And Evaluation ◽  
Strong Nonlinearity ◽  
Filling Process ◽  
Time Frequency ◽  
Feature Parameters

The sand-filling method has been widely used in immersed tube tunnel engineering. However, for the problem of monitoring during the sand-filling process, the traditional methods can be inadequate for evaluating the state of sand deposits in real-time. Based on the high efficiency of elastic wave monitoring, and the superiority of the backpropagation (BP) neural network on solving nonlinear problems, a spatiotemporal monitoring and evaluation method is proposed for the filling performance of foundation cushion. Elastic wave data were collected during the sand-filling process, and the waveform, frequency spectrum, and time–frequency features were analysed. The feature parameters of the elastic wave were characterized by the time domain, frequency domain, and time-frequency domain. By analysing the changes of feature parameters with the sand-filling process, the feature parameters exhibited dynamic and strong nonlinearity. The data of elastic wave feature parameters and the corresponding sand-filling state were trained to establish the evaluation model using the BP neural network. The accuracy of the trained network model reached 93%. The side holes and middle holes were classified and analysed, revealing the characteristics of the dynamic expansion of the sand deposit along the diffusion radius. The evaluation results are consistent with the pressure gauge monitoring data, indicating the effectiveness of the evaluation and monitoring model for the spatiotemporal performance of sand deposits. For the sand-filling and grouting engineering, the machine-learning method could offer a better solution for spatiotemporal monitoring and evaluation in a complex environment.

scholarly journals Recurrent Neural Networks for Narrowband Signal Detection in the Time-Frequency Domain

2004 ◽  
Vol 213 ◽  
pp. 483-486
Author(s):  
David Brodrick ◽  
Douglas Taylor ◽  
Joachim Diederich
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Signal Detection ◽  
Frequency Domain ◽  
Recurrent Neural Networks ◽  
Radio Frequency Interference ◽  
Recurrent Networks ◽  
Time Frequency ◽  
Narrowband Signal ◽  
Radio Signals

A recurrent neural network was trained to detect the time-frequency domain signature of narrowband radio signals against a background of astronomical noise. The objective was to investigate the use of recurrent networks for signal detection in the Search for Extra-Terrestrial Intelligence, though the problem is closely analogous to the detection of some classes of Radio Frequency Interference in radio astronomy.

scholarly journals Bimodal Emotion Recognition Model for Minnan Songs

Information ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 145 ◽  
Author(s):  
Zhenglong Xiang ◽  
Xialei Dong ◽  
Yuanxiang Li ◽  
Fei Yu ◽  
Xing Xu ◽  
...  
Keyword(s):  
Neural Network ◽  
Emotion Recognition ◽  
Short Term Memory ◽  
Music Appreciation ◽  
Research Papers ◽  
Audio Features ◽  
Analysis Theory ◽  
Proposed Model ◽  
Song Lyrics ◽  
Long Short Term Memory

Most of the existing research papers study the emotion recognition of Minnan songs from the perspectives of music analysis theory and music appreciation. However, these investigations do not explore any possibility of carrying out an automatic emotion recognition of Minnan songs. In this paper, we propose a model that consists of four main modules to classify the emotion of Minnan songs by using the bimodal data—song lyrics and audio. In the proposed model, an attention-based Long Short-Term Memory (LSTM) neural network is applied to extract lyrical features, and a Convolutional Neural Network (CNN) is used to extract the audio features from the spectrum. Then, two kinds of extracted features are concatenated by multimodal compact bilinear pooling, and finally, the concatenated features are input to the classifying module to determine the song emotion. We designed three experiment groups to investigate the classifying performance of combinations of the four main parts, the comparisons of proposed model with the current approaches and the influence of a few key parameters on the performance of emotion recognition. The results show that the proposed model exhibits better performance over all other experimental groups. The accuracy, precision and recall of the proposed model exceed 0.80 in a combination of appropriate parameters.

Spectral features based convolutional neural network for accurate and prompt identification of schizophrenic patients

2020 ◽  
pp. 095441192096693
Author(s):  
Kuldeep Singh ◽  
Sukhjeet Singh ◽  
Jyoteesh Malhotra
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Frequency Domain ◽  
Short Term Memory ◽  
Spectral Power ◽  
Machine Learning Techniques ◽  
Spectral Amplitude ◽  
Spectral Features ◽  
Eeg Signals ◽  
Schizophrenic Patients

Schizophrenia is a fatal mental disorder, which affects millions of people globally by the disturbance in their thinking, feeling and behaviour. In the age of the internet of things assisted with cloud computing and machine learning techniques, the computer-aided diagnosis of schizophrenia is essentially required to provide its patients with an opportunity to own a better quality of life. In this context, the present paper proposes a spectral features based convolutional neural network (CNN) model for accurate identification of schizophrenic patients using spectral analysis of multichannel EEG signals in real-time. This model processes acquired EEG signals with filtering, segmentation and conversion into frequency domain. Then, given frequency domain segments are divided into six distinct spectral bands like delta, theta-1, theta-2, alpha, beta and gamma. The spectral features including mean spectral amplitude, spectral power and Hjorth descriptors (Activity, Mobility and Complexity) are extracted from each band. These features are independently fed to the proposed spectral features-based CNN and long short-term memory network (LSTM) models for classification. This work also makes use of raw time-domain and frequency-domain EEG segments for classification using temporal CNN and spectral CNN models of same architectures respectively. The overall analysis of simulation results of all models exhibits that the proposed spectral features based CNN model is an efficient technique for accurate and prompt identification of schizophrenic patients among healthy individuals with average classification accuracies of 94.08% and 98.56% for two different datasets with optimally small classification time.

A novel artificial neural network for flood forecasting based on deep learning encoder-decoder architecture

2020 ◽  
Author(s):  
Kangling Lin ◽  
Hua Chen ◽  
Chong-Yu Xu ◽  
Yanlai Zhou ◽  
Shenglian Guo
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Goodness Of Fit ◽  
Short Term Memory ◽  
Flood Forecasting ◽  
Learning Ability ◽  
Convolutional Network ◽  
Study Results ◽  
Decoder Architecture ◽  
Artificial Neural

<p>With the rapid growth of deep learning recently, artificial neural networks have been propelled to the forefront in flood forecasting via their end-to-end learning ability. Encoder-decoder architecture, as a novel deep feature extraction, which captures the inherent relationship of the data involved, has emerged in time sequence forecasting nowadays. As the advance of encoder-decoder architecture in sequence to sequence learning, it has been applied in many fields, such as machine translation, energy and environment. However, it is seldom used in hydrological modelling. In this study, a new neural network is developed to forecast flood based on the encoder-decoder architecture. There are two deep learning methods, including the Long Short-Term Memory (LSTM) network and Temporal Convolutional Network (TCN), selected as encoders respectively, while the LSTM was also chosen as the decoder, whose results are compared with those from the standard LSTM without using encoder-decoder architecture.</p><p>These models were trained and tested by using the hourly flood events data from 2009 to 2015 in Jianxi basin, China. The results indicated that the new neural flood forecasting networks based encoder-decoder architectures generally perform better than the standard LSTM, since they have better goodness-of-fit between forecasted and observed flood and produce the promising performance in multi-index assessment. The TCN as an encoder has better model stability and accuracy than LSTM as an encoder, especially in longer forecast periods and larger flood. The study results also show that the encoder-decoder architecture can be used as an effective deep learning solution in flood forecasting.</p><p></p>

Adaptive BP Wavelet Neural Network Method for Soft Fault Diagnosis in Analog Circuit

2013 ◽  
Vol 307 ◽  
pp. 327-330
Author(s):  
Wei Cong ◽  
Bo Jing ◽  
Hong Kun Yu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Adaptive Learning ◽  
Analog Circuit ◽  
Wavelet Neural Network ◽  
Learning Ability ◽  
Time Frequency ◽  
Soft Fault ◽  
Time Frequency Localization ◽  
Soft Fault Diagnosis

Because of the diversity and complexity of soft fault in analog circuit, the rapid and accurate diagnosis is very difficult. For this, an adaptive BP wavelet neural network diagnosis method of soft fault is proposed. It combines the time-frequency localization characteristics of wavelet and the self-learning ability of neural network in soft fault diagnosis of analog circuit, and by introducing the adaptive learning rate the diagnosis ability of BP wavelet neural network model can effectively be improved. In addition, PSPICE software is used to obtain the simulation data of actual analog circuit for the experiment. The results also verify the validity of the proposed method.

scholarly journals EEG-Based Emotion Recognition Using Quadratic Time-Frequency Distribution

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2739 ◽  
Author(s):  
Rami Alazrai ◽  
Rasha Homoud ◽  
Hisham Alwanni ◽  
Mohammad Daoud
Keyword(s):  
Emotion Recognition ◽  
Frequency Domain ◽  
Frequency Distribution ◽  
Support Vector ◽  
Eeg Signals ◽  
Time Frequency ◽  
Labeling Schemes ◽  
Frequency Representation ◽  
Frequency Features ◽  
Time Frequency Distribution

Accurate recognition and understating of human emotions is an essential skill that can improve the collaboration between humans and machines. In this vein, electroencephalogram (EEG)-based emotion recognition is considered an active research field with challenging issues regarding the analyses of the nonstationary EEG signals and the extraction of salient features that can be used to achieve accurate emotion recognition. In this paper, an EEG-based emotion recognition approach with a novel time-frequency feature extraction technique is presented. In particular, a quadratic time-frequency distribution (QTFD) is employed to construct a high resolution time-frequency representation of the EEG signals and capture the spectral variations of the EEG signals over time. To reduce the dimensionality of the constructed QTFD-based representation, a set of 13 time- and frequency-domain features is extended to the joint time-frequency-domain and employed to quantify the QTFD-based time-frequency representation of the EEG signals. Moreover, to describe different emotion classes, we have utilized the 2D arousal-valence plane to develop four emotion labeling schemes of the EEG signals, such that each emotion labeling scheme defines a set of emotion classes. The extracted time-frequency features are used to construct a set of subject-specific support vector machine classifiers to classify the EEG signals of each subject into the different emotion classes that are defined using each of the four emotion labeling schemes. The performance of the proposed approach is evaluated using a publicly available EEG dataset, namely the DEAPdataset. Moreover, we design three performance evaluation analyses, namely the channel-based analysis, feature-based analysis and neutral class exclusion analysis, to quantify the effects of utilizing different groups of EEG channels that cover various regions in the brain, reducing the dimensionality of the extracted time-frequency features and excluding the EEG signals that correspond to the neutral class, on the capability of the proposed approach to discriminate between different emotion classes. The results reported in the current study demonstrate the efficacy of the proposed QTFD-based approach in recognizing different emotion classes. In particular, the average classification accuracies obtained in differentiating between the various emotion classes defined using each of the four emotion labeling schemes are within the range of 73 . 8 % – 86 . 2 % . Moreover, the emotion classification accuracies achieved by our proposed approach are higher than the results reported in several existing state-of-the-art EEG-based emotion recognition studies.

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