scholarly journals Music Feature Extraction and Classification Algorithm Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jingwen Zhang
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Large Scale ◽  
Rapid Development ◽  
Audio Signal ◽  
Classification Model ◽  
Machine Learning Classification ◽  
Gating Mechanism ◽  
Music Information ◽  
Sound Spectrum

With the rapid development of information technology and communication, digital music has grown and exploded. Regarding how to quickly and accurately retrieve the music that users want from huge bulk of music repository, music feature extraction and classification are considered as an important part of music information retrieval and have become a research hotspot in recent years. Traditional music classification approaches use a large number of artificially designed acoustic features. The design of features requires knowledge and in-depth understanding in the domain of music. The features of different classification tasks are often not universal and comprehensive. The existing approach has two shortcomings as follows: ensuring the validity and accuracy of features by manually extracting features and the traditional machine learning classification approaches not performing well on multiclassification problems and not having the ability to be trained on large-scale data. Therefore, this paper converts the audio signal of music into a sound spectrum as a unified representation, avoiding the problem of manual feature selection. According to the characteristics of the sound spectrum, the research has combined 1D convolution, gating mechanism, residual connection, and attention mechanism and proposed a music feature extraction and classification model based on convolutional neural network, which can extract more relevant sound spectrum characteristics of the music category. Finally, this paper designs comparison and ablation experiments. The experimental results show that this approach is better than traditional manual models and machine learning-based approaches.

scholarly journals Using Machine Learning for Quantum Annealing Accuracy Prediction

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 187
Author(s):  
Aaron Barbosa ◽  
Elijah Pelofske ◽  
Georg Hahn ◽  
Hristo N. Djidjev
Keyword(s):  
Machine Learning ◽  
Maximum Clique ◽  
Classification Model ◽  
Maximum Clique Problem ◽  
Problem Instance ◽  
Np Hard ◽  
Machine Learning Classification ◽  
Hard Problems ◽  
Problem Instances ◽  
D Wave

Quantum annealers, such as the device built by D-Wave Systems, Inc., offer a way to compute solutions of NP-hard problems that can be expressed in Ising or quadratic unconstrained binary optimization (QUBO) form. Although such solutions are typically of very high quality, problem instances are usually not solved to optimality due to imperfections of the current generations quantum annealers. In this contribution, we aim to understand some of the factors contributing to the hardness of a problem instance, and to use machine learning models to predict the accuracy of the D-Wave 2000Q annealer for solving specific problems. We focus on the maximum clique problem, a classic NP-hard problem with important applications in network analysis, bioinformatics, and computational chemistry. By training a machine learning classification model on basic problem characteristics such as the number of edges in the graph, or annealing parameters, such as the D-Wave’s chain strength, we are able to rank certain features in the order of their contribution to the solution hardness, and present a simple decision tree which allows to predict whether a problem will be solvable to optimality with the D-Wave 2000Q. We extend these results by training a machine learning regression model that predicts the clique size found by D-Wave.

scholarly journals Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 82
Author(s):  
Ahmad O. Aseeri
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Probabilistic Approach ◽  
Classification Model ◽  
Gating Mechanism ◽  
Uncertainty Estimates ◽  
Wide Range

Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.

scholarly journals Harnessing Machine Learning for Classifying Economic Damage Trends in Transportation Infrastructure Projects

2021 ◽  
Vol 13 (11) ◽  
pp. 6376
Author(s):  
Junseo Bae ◽  
Sang-Guk Yum ◽  
Ji-Myong Kim
Keyword(s):  
Machine Learning ◽  
Financial Management ◽  
Construction Projects ◽  
Transportation Infrastructure ◽  
Classification Model ◽  
Insurance Companies ◽  
Economic Damage ◽  
Infrastructure Projects ◽  
Machine Learning Classification ◽  
Financial Losses

Given the highly visible nature, transportation infrastructure construction projects are often exposed to numerous unexpected events, compared to other types of construction projects. Despite the importance of predicting financial losses caused by risk, it is still difficult to determine which risk factors are generally critical and when these risks tend to occur, without benchmarkable references. Most of existing methods are prediction-focused, project type-specific, while ignoring the timing aspect of risk. This study filled these knowledge gaps by developing a neural network-driven machine-learning classification model that can categorize causes of financial losses depending on insurance claim payout proportions and risk occurrence timing, drawing on 625 transportation infrastructure construction projects including bridges, roads, and tunnels. The developed network model showed acceptable classification accuracy of 74.1%, 69.4%, and 71.8% in training, cross-validation, and test sets, respectively. This study is the first of its kind by providing benchmarkable classification references of economic damage trends in transportation infrastructure projects. The proposed holistic approach will help construction practitioners consider the uncertainty of project management and the potential impact of natural hazards proactively, with the risk occurrence timing trends. This study will also assist insurance companies with developing sustainable financial management plans for transportation infrastructure projects.

scholarly journals Can the application of certain music information retrieval methods contribute to the machine learning classification of electrocardiographic signals?

Heliyon ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e06257
Author(s):  
Ennio Idrobo-Ávila ◽  
Humberto Loaiza-Correa ◽  
Rubiel Vargas-Cañas ◽  
Flavio Muñoz-Bolaños ◽  
Leon van Noorden
Keyword(s):  
Machine Learning ◽  
Information Retrieval ◽  
Music Information Retrieval ◽  
Machine Learning Classification ◽  
Information Retrieval Methods ◽  
Music Information

scholarly journals Applying Machine Learning for Improving Performance Classification on Driving Behavior

2021 ◽  
Vol 4 (1) ◽  
pp. 8
Author(s):  
Ahmad Iwan Fadli ◽  
Selo Sulistyo ◽  
Sigit Wibowo
Keyword(s):  
Machine Learning ◽  
Traffic Accident ◽  
Large Scale ◽  
Detection System ◽  
Difficult Problem ◽  
Sensor Data ◽  
Driving Safety ◽  
Support Vector ◽  
Classification Methods ◽  
Machine Learning Classification

Traffic accident is a very difficult problem to handle on a large scale in a country. Indonesia is one of the most populated, developing countries that use vehicles for daily activities as its main transportation.  It is also the country with the largest number of car users in Southeast Asia, so driving safety needs to be considered. Using machine learning classification method to determine whether a driver is driving safely or not can help reduce the risk of driving accidents. We created a detection system to classify whether the driver is driving safely or unsafely using trip sensor data, which include Gyroscope, Acceleration, and GPS. The classification methods used in this study are Random Forest (RF) classification algorithm, Support Vector Machine (SVM), and Multilayer Perceptron (MLP) by improving data preprocessing using feature extraction and oversampling methods. This study shows that RF has the best performance with 98% accuracy, 98% precision, and 97% sensitivity using the proposed preprocessing stages compared to SVM or MLP.

scholarly journals Machine-learning based identification of undiagnosed dementia in primary care: a feasibility study

BJGP Open ◽  
2018 ◽  
Vol 2 (2) ◽  
pp. bjgpopen18X101589 ◽  
Author(s):  
Emmanuel A Jammeh ◽  
Camille, B Carroll ◽  
Stephen, W Pearson ◽  
Javier Escudero ◽  
Athanasios Anastasiou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Primary Care ◽  
Case Control ◽  
Classification Model ◽  
Dementia Diagnosis ◽  
Machine Learning Classification ◽  
Primary Care Data ◽  
Dementia Risk ◽  
Diagnosis Of Dementia ◽  
Read Codes

BackgroundUp to half of patients with dementia may not receive a formal diagnosis, limiting access to appropriate services. It is hypothesised that it may be possible to identify undiagnosed dementia from a profile of symptoms recorded in routine clinical practice.AimThe aim of this study is to develop a machine learning-based model that could be used in general practice to detect dementia from routinely collected NHS data. The model would be a useful tool for identifying people who may be living with dementia but have not been formally diagnosed.Design & settingThe study involved a case-control design and analysis of primary care data routinely collected over a 2-year period. Dementia diagnosed during the study period was compared to no diagnosis of dementia during the same period using pseudonymised routinely collected primary care clinical data.MethodRoutinely collected Read-encoded data were obtained from 18 consenting GP surgeries across Devon, for 26 483 patients aged >65 years. The authors determined Read codes assigned to patients that may contribute to dementia risk. These codes were used as features to train a machine-learning classification model to identify patients that may have underlying dementia.ResultsThe model obtained sensitivity and specificity values of 84.47% and 86.67%, respectively.ConclusionThe results show that routinely collected primary care data may be used to identify undiagnosed dementia. The methodology is promising and, if successfully developed and deployed, may help to increase dementia diagnosis in primary care.

scholarly journals A single-cell Raman-based platform to identify developmental stages of human pluripotent stem cell-derived neurons

2020 ◽  
Vol 117 (31) ◽  
pp. 18412-18423 ◽  
Author(s):  
Chia-Chen Hsu ◽  
Jiabao Xu ◽  
Bas Brinkhof ◽  
Hui Wang ◽  
Zhanfeng Cui ◽  
...  
Keyword(s):  
Stem Cells ◽  
Stem Cell ◽  
Single Cell ◽  
Raman Spectra ◽  
Large Scale ◽  
Developmental Stages ◽  
Single Cells ◽  
Classification Model ◽  
Label Free ◽  
Machine Learning Classification

Stem cells with the capability to self-renew and differentiate into multiple cell derivatives provide platforms for drug screening and promising treatment options for a wide variety of neural diseases. Nevertheless, clinical applications of stem cells have been hindered partly owing to a lack of standardized techniques to characterize cell molecular profiles noninvasively and comprehensively. Here, we demonstrate that a label-free and noninvasive single-cell Raman microspectroscopy (SCRM) platform was able to identify neural cell lineages derived from clinically relevant human induced pluripotent stem cells (hiPSCs). By analyzing the intrinsic biochemical profiles of single cells at a large scale (8,774 Raman spectra in total), iPSCs and iPSC-derived neural cells can be distinguished by their intrinsic phenotypic Raman spectra. We identified a Raman biomarker from glycogen to distinguish iPSCs from their neural derivatives, and the result was verified by the conventional glycogen detection assays. Further analysis with a machine learning classification model, utilizing t-distributed stochastic neighbor embedding (t-SNE)-enhanced ensemble stacking, clearly categorized hiPSCs in different developmental stages with 97.5% accuracy. The present study demonstrates the capability of the SCRM-based platform to monitor cell development using high content screening with a noninvasive and label-free approach. This platform as well as our identified biomarker could be extensible to other cell types and can potentially have a high impact on neural stem cell therapy.

scholarly journals Connecting Histopathology Imaging and Proteomics in Kidney Cancer through Machine Learning

2019 ◽  
Vol 8 (10) ◽  
pp. 1535 ◽  
Author(s):  
Francisco Azuaje ◽  
Sang-Yoon Kim ◽  
Daniel Perez Hernandez ◽  
Gunnar Dittmar
Keyword(s):  
Machine Learning ◽  
Large Scale ◽  
Diagnostic Value ◽  
Classification Model ◽  
Clinical Approach ◽  
Proteomics Data ◽  
Cell Renal Cell Carcinoma ◽  
Molecular Features ◽  
Genes Encoding ◽  
New Research

Proteomics data encode molecular features of diagnostic value and accurately reflect key underlying biological mechanisms in cancers. Histopathology imaging is a well-established clinical approach to cancer diagnosis. The predictive relationship between large-scale proteomics and H&E-stained histopathology images remains largely uncharacterized. Here we investigate such associations through the application of machine learning, including deep neural networks, to proteomics and histology imaging datasets generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) from clear cell renal cell carcinoma patients. We report robust correlations between a set of diagnostic proteins and predictions generated by an imaging-based classification model. Proteins significantly correlated with the histology-based predictions are significantly implicated in immune responses, extracellular matrix reorganization, and metabolism. Moreover, we showed that the genes encoding these proteins also reliably recapitulate the biological associations with imaging-derived predictions based on strong gene–protein expression correlations. Our findings offer novel insights into the integrative modeling of histology and omics data through machine learning, as well as the methodological basis for new research opportunities in this and other cancer types.

scholarly journals Monitoring Forest Change in the Amazon Using Multi-Temporal Remote Sensing Data and Machine Learning Classification on Google Earth Engine

2020 ◽  
Vol 9 (10) ◽  
pp. 580 ◽  
Author(s):  
Maria Antonia Brovelli ◽  
Yaru Sun ◽  
Vasil Yordanov
Keyword(s):  
Machine Learning ◽  
Forest Dynamics ◽  
Google Earth ◽  
Classification Model ◽  
Mitigation Measures ◽  
Computational Time ◽  
Forest Change ◽  
Machine Learning Classification ◽  
Cloud Processing ◽  
Google Earth Engine

Deforestation causes diverse and profound consequences for the environment and species. Direct or indirect effects can be related to climate change, biodiversity loss, soil erosion, floods, landslides, etc. As such a significant process, timely and continuous monitoring of forest dynamics is important, to constantly follow existing policies and develop new mitigation measures. The present work had the aim of mapping and monitoring the forest change from 2000 to 2019 and of simulating the future forest development of a rainforest region located in the Pará state, Brazil. The land cover dynamics were mapped at five-year intervals based on a supervised classification model deployed on the cloud processing platform Google Earth Engine. Besides the benefits of reduced computational time, the service is coupled with a vast data catalogue providing useful access to global products, such as multispectral images of the missions Landsat five, seven, eight and Sentinel-2. The validation procedures were done through photointerpretation of high-resolution panchromatic images obtained from CBERS (China–Brazil Earth Resources Satellite). The more than satisfactory results allowed an estimation of peak deforestation rates for the period 2000–2006; for the period 2006–2015, a significant decrease and stabilization, followed by a slight increase till 2019. Based on the derived trends a forest dynamics was simulated for the period 2019–2028, estimating a decrease in the deforestation rate. These results demonstrate that such a fusion of satellite observations, machine learning, and cloud processing, benefits the analysis of the forest dynamics and can provide useful information for the development of forest policies.

Sign in / Sign up

Export Citation Format

Share Document