scholarly journals Research on Autoarrangement System of Accompaniment Chords Based on Hidden Markov Model with Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Shuo Shi ◽  
Shuting Xi ◽  
Sang-Bing Tsai
Keyword(s):  
Machine Learning ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Template Matching ◽  
Hidden Markov ◽  
Data Set ◽  
Data Format ◽  
Automated Method ◽  
Machine Learning Model ◽  
And Training

Accompaniment production is one of the most important elements in music work, and chord arrangement is the key link of accompaniment production, which usually requires more musical talent and profound music theory knowledge to be competent. In this article, the machine learning model is used to replace manual accompaniment chords’ arrangement, and an automatic computer means is provided to complete and assist accompaniment chords’ arrangement. Also, through music feature extraction, automatic chord label construction, and model construction and training, the whole system finally has the ability of automatic accompaniment chord arrangement for the main melody. Based on the research of automatic chord label construction method and the characteristics of MIDI data format, a chord analysis method based on interval difference is proposed to construct chord labels of the whole track and realize the construction of automatic chord labels. In this study, the hidden Markov model is constructed according to the chord types, in which the input features are the improved theme PCP features proposed in this paper, and the input labels are the label data set constructed by the automated method proposed in this paper. After the training is completed, the PCP features of the theme to be predicted and improved are input to generate the accompaniment chords of the final arrangement. Through PCP features and template-matching model, the system designed in this paper improves the matching accuracy of the generated chords compared with that generated by the traditional method.

scholarly journals Detection of structural variations in densely-labelled optical DNA barcodes: A hidden Markov model approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259670
Author(s):  
Albertas Dvirnas ◽  
Callum Stewart ◽  
Vilhelm Müller ◽  
Santosh Kumar Bikkarolla ◽  
Karolin Frykholm ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Large Scale ◽  
Hidden Markov ◽  
Sequence Information ◽  
True Positive ◽  
Dna Barcodes ◽  
Structural Variations ◽  
Genomic Alterations ◽  
Data Set

Large-scale genomic alterations play an important role in disease, gene expression, and chromosome evolution. Optical DNA mapping (ODM), commonly categorized into sparsely-labelled ODM and densely-labelled ODM, provides sequence-specific continuous intensity profiles (DNA barcodes) along single DNA molecules and is a technique well-suited for detecting such alterations. For sparsely-labelled barcodes, the possibility to detect large genomic alterations has been investigated extensively, while densely-labelled barcodes have not received as much attention. In this work, we introduce HMMSV, a hidden Markov model (HMM) based algorithm for detecting structural variations (SVs) directly in densely-labelled barcodes without access to sequence information. We evaluate our approach using simulated data-sets with 5 different types of SVs, and combinations thereof, and demonstrate that the method reaches a true positive rate greater than 80% for randomly generated barcodes with single variations of size 25 kilobases (kb). Increasing the length of the SV further leads to larger true positive rates. For a real data-set with experimental barcodes on bacterial plasmids, we successfully detect matching barcode pairs and SVs without any particular assumption of the types of SVs present. Instead, our method effectively goes through all possible combinations of SVs. Since ODM works on length scales typically not reachable with other techniques, our methodology is a promising tool for identifying arbitrary combinations of genomic alterations.

scholarly journals High Performance Human Face Recognition using Gabor Based Pseudo Hidden Markov Model

2013 ◽  
Vol 4 (1) ◽  
pp. 81-102 ◽  
Author(s):  
Arindam Kar ◽  
Debotosh Bhattacharjee ◽  
Mita Nasipuri ◽  
Dipak Kumar Basu ◽  
Mahantapas Kundu
Keyword(s):  
Face Recognition ◽  
Markov Model ◽  
Hidden Markov Model ◽  
High Performance ◽  
Hidden Markov ◽  
Gabor Wavelet ◽  
Data Set ◽  
Scanning Method ◽  
Face Images ◽  
Gabor Wavelet Transformation

This paper introduces a novel methodology that combines the multi-resolution feature of the Gabor wavelet transformation (GWT) with the local interactions of the facial structures expressed through the Pseudo Hidden Markov Model (PHMM). Unlike the traditional zigzag scanning method for feature extraction a continuous scanning method from top-left corner to right then top-down and right to left and so on until right-bottom of the image i.e., a spiral scanning technique has been proposed for better feature selection. Unlike traditional HMMs, the proposed PHMM does not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the PHMM used to extract facial bands and automatically select the most informative features of a face image. Thus, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. Again with the use of most informative pixels rather than the whole image makes the proposed method reasonably faster for face recognition. This method has been successfully tested on frontal face images from the ORL, FRAV2D, and FERET face databases where the images vary in pose, illumination, expression, and scale. The FERET data set contains 2200 frontal face images of 200 subjects, while the FRAV2D data set consists of 1100 images of 100 subjects and the full ORL database is considered. The results reported in this application are far better than the recent and most referred systems.

scholarly journals Estimation of Malaria Symptom Data Set using Hidden Markov Model

2019 ◽  
pp. 1-29
Author(s):  
Drinold Mbete ◽  
Kennedy Nyongesa ◽  
Joseph Rotich
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Disease Status ◽  
Clinic Visit ◽  
Reliable Prediction ◽  
Data Set ◽  
Susceptibility To Infection ◽  
Partially Observed ◽  
Intense Research

Clinical study of malaria presents a modeling challenge as patients disease status and progress is partially observed and assessed at discrete clinic visit times. Since patients initiate visits based on symptoms, intense research has focused on identication of reliable prediction for exposure, susceptibility to infection and development of severe malaria complications. Despite detailed literature on malaria infection and transmission, very little has been documented in the existing literature on malaria symptoms modeling, yet these symptoms are common. Furthermore, imperfect diagnostic tests may yield misclassication of observed symptoms. Place and Duration of Study: The main objective of this study is to develop a Bayesian Hidden Markov Model of Malaria symptoms in Masinde Muliro University of Science and Technology student population. An expression of Hidden Markov Model is developed and the parameters estimated through the forward-backward algorithm.

scholarly journals Increasing the Efficiency of Genome-wide Association Mapping via Hidden Markov Models

2016 ◽  
Author(s):  
Hong Gao ◽  
Hua Tang ◽  
Carlos Bustamante
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Large Scale ◽  
Hidden Markov ◽  
Association Studies ◽  
Genome Wide Association ◽  
Trend Test ◽  
Genome Wide Association Studies ◽  
Data Set ◽  
Genome Wide

With the rapid production of high dimensional genetic data, one major challenge in genome-wide association studies is to develop effective and efficient statistical tools to resolve the low power problem of detecting causal SNPs with low to moderate susceptibility, whose effects are often obscured by substantial background noises. Here we present a novel method that serves as an optimal technique for reducing background noises and improving detection power in genome-wide association studies. The approach uses hidden Markov model and its derivate Markov hidden Markov model to estimate the posterior probabilities of a markers being in an associated state. We conducted extensive simulations based on the human whole genome genotype data from the GlaxoSmithKline-POPRES project to calibrate the sensitivity and specificity of our method and compared with many popular approaches for detecting positive signals including the χ^2 test for association and the Cochran-Armitage trend test. Our simulation results suggested that at very low false positive rates (<10^-6), our method reaches the power of 0.9, and is more powerful than any other approaches, when the allelic effect of the causal variant is non-additive or unknown. Application of our method to the data set generated by Welcome Trust Case Control Consortium using 14,000 cases and 3,000 controls confirmed its powerfulness and efficiency under the context of the large-scale genome-wide association studies.

scholarly journals Multi-Layer Hidden Markov Model Based Intrusion Detection System

2018 ◽  
Vol 1 (1) ◽  
pp. 265-286 ◽  
Author(s):  
Wondimu Zegeye ◽  
Richard Dean ◽  
Farzad Moazzami
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Detection System ◽  
Traffic Monitoring ◽  
Machine Learning Algorithms ◽  
Full Potential ◽  
Multi Phase

The all IP nature of the next generation (5G) networks is going to open a lot of doors for new vulnerabilities which are going to be challenging in preventing the risk associated with them. Majority of these vulnerabilities might be impossible to detect with simple networking traffic monitoring tools. Intrusion Detection Systems (IDS) which rely on machine learning and artificial intelligence can significantly improve network defense against intruders. This technology can be trained to learn and identify uncommon patterns in massive volume of traffic and notify, using such as alert flags, system administrators for additional investigation. This paper proposes an IDS design which makes use of machine learning algorithms such as Hidden Markov Model (HMM) using a multi-layer approach. This approach has been developed and verified to resolve the common flaws in the application of HMM to IDS commonly referred as the curse of dimensionality. It factors a huge problem of immense dimensionality to a discrete set of manageable and reliable elements. The multi-layer approach can be expanded beyond 2 layers to capture multi-phase attacks over longer spans of time. A pyramid of HMMs can resolve disparate digital events and signatures across protocols and platforms to actionable information where lower layers identify discrete events (such as network scan) and higher layers new states which are the result of multi-phase events of the lower layers. The concepts of this novel approach have been developed but the full potential has not been demonstrated.

Hybrid Physics-Based and Data-Driven Modeling for Improved Standpipe Pressure Prediction

2021 ◽  
Author(s):  
Oney Erge ◽  
Eric van Oort
Keyword(s):  
Machine Learning ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Hybrid Modeling ◽  
Data Driven ◽  
Learning Models ◽  
Drilling Data ◽  
Drilling Operations ◽  
Pump Pressure

Abstract During drilling operations, it is common to see pump pressure spikes when flow is initiated, including after a connection or after a prolonged break in drilling operations. It is important to be able to predict the magnitude of such pressure spikes to avoid compromising wellbore integrity. This study shows how a hybrid approach using data-driven machine learning coupled with physics-based modeling can be used to accurately predict the magnitude of pressure spikes. To model standpipe pressure behavior, machine learning techniques were combined with physics-based models via a rule-based, stochastic decision-making algorithm. To start, neural networks and deep learning models were trained using time-series drilling data. From there, physics-based equations that model the pressure required to break the mud's gel strength as well as the flow of non-Newtonian fluids through the entire circulation system were used to simulate standpipe pressure. Then, these two highly different methods for predicting/modeling standpipe pressure were combined by a hidden Markov model using a set of rules and transition probabilities. By combining machine learning and physics-based approaches, the best features of each model are leveraged by the hidden Markov model, yielding a more accurate and robust prediction of pressure. A similar result is not achievable with a purely data-driven black-box model, because it lacks a connection to the underlying physics. Our study highlights how drilling data analysis can be optimally leveraged. The overarching conclusion: hybrid modeling can more accurately predict pump pressure spikes and capture the transient events at flow initiation when compared to physics-based or machine learning models used in isolation. Moreover, the approach is not limited to pressure behavior but can be applied to a wide range of well construction operations. The proposed approach is easy to implement and the details of implementation are presented in this study. Being able to accurately model and manage the pressure response during drilling operations is essential, especially for wells drilled in narrow-margin environments. Pressure can be more accurately predicted through our proposed hybrid modeling, leading to safer, more optimized operations.

scholarly journals Automatic Classification and Analysis of Music Multimedia Combined with Hidden Markov Model

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yanjiao Chen
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Traditional Music ◽  
Automatic Classification ◽  
Multimedia Data ◽  
Classification Model ◽  
Model Structure ◽  
Data Set ◽  
Variable Parameters

Music multimedia is one of the more popular types of digital music. This article is based on the hidden Markov model (HMM) and proposed this kind of music multimedia automatic classification method. The method not only analyzes the characteristics of traditional music in detail but also fully considers the important characteristics of other music. At the same time, it uses bagging to train two groups of HMMs and automatically classifies them to achieve a better classification effect. This paper optimizes the variable parameters from different aspects such as model structure, data form, and model change to obtain the optimal HMM parameter value. This method not only considers the prior knowledge of feature words, word frequency, and number of documents but also fuses the meaning of the feature words into the hidden Markov classification model. Finally, by testing the hidden Markov model used in this paper on the music multimedia data set, the experimental results show that the method in this paper can effectively perform automatic classification according to the melody characteristics of music multimedia.

scholarly journals Identifying Intrusion Behaviour using Enhanced Hidden Markov Model

2020 ◽  
Vol 9 (4) ◽  
pp. 1618-1623
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Credit Card ◽  
Hidden Markov ◽  
Network Intrusion Detection ◽  
Detection Accuracy ◽  
Constant Attention ◽  
Network Intrusion

Data Mining is a method for detecting network intrusion detection in networks. It brings ideas from variety of areas including statistics, machine learning and database processes. Decreasing price of digital networking is now economically viable for network intrusion detection. This analysis chiefly examines the system intrusion detection with machine learning and DM methods. To improve the accuracy and efficiency of SHMM, we are collecting multiple observation in SHMM that will be called as Multiple Hidden Markov Model (MHMM). It is used to improve better Detection accuracy compare with SHMM. In the standard Hidden Markov Model, we have observed three fundamental problems are Evaluation and decoding another one is learning problem. The Evaluation problem can be used for word recognition. And the Decoding problem is related to constant attention and also the segmentation. In this Proposed Research, the primary purpose is to model the sequence of observation in Network log and credit card log transactions process using Enhanced Hidden Markov Model (EHMM). And show how it can be used for intrusion detection in Network. In this procedure, an EHMM is primarily trained with the conventional manners of a intruders. If the trained EHMM does not recognize an incoming Intruder transaction with adequately high probability, it is thought to be fraudulent.

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