scholarly journals A Model for Predicting Music Popularity on Streaming Platforms

2020 ◽  
Vol 27 (4) ◽  
pp. 108-117
Author(s):  
Carlos Vicente Soares Araujo ◽  
Marco Antônio Pinheiro de Cristo ◽  
Rafael Giusti
Keyword(s):  
Svm Classifier ◽  
Audio Features ◽  
Popular Songs ◽  
Rbf Kernel ◽  
Streaming Services ◽  
Music Market

The global music market moves billions of dollars every year, most of which comes from streamingplatforms. In this paper, we present a model for predicting whether or not a song will appear in Spotify’s Top 50, a ranking of the 50 most popular songs in Spotify, which is one of today’s biggest streaming services. To make this prediction, we trained different classifiers with information from audio features from songs that appeared in this ranking between November 2018 and January 2019. When tested with data from June and July 2019, an SVM classifier with RBF kernel obtained accuracy, precision, and AUC above 80%.

scholarly journals DEMYSTIFYING MUSICAL PREFERENCES AT TURKISH MUSIC MARKET THROUGH AUDIO FEATURES OF SPOTIFY CHARTS

2019 ◽  
Vol 4 (3) ◽  
pp. 264-279
Author(s):  
Fatih PINARBAÅžI
Keyword(s):  
Descriptive Statistics ◽  
Popular Song ◽  
Energy Characteristics ◽  
Audio Features ◽  
Popular Songs ◽  
Music Consumption ◽  
Online Music ◽  
Streaming Services ◽  
Music Market ◽  
Music Streaming

Online music streaming services are one of the important actors in music consumption for today’s consumers. In addition to widespread use of mobile devices, many changes in the patterns of music consumption are witnessed such as the purchase of single tracks instead of albums, listening to music on different platforms, and personalized music consumption options. This study aims to examine the concept of music consumption in Turkey through audio characteristics of popular songs. Top 200 popular song-lists for 6 months period are chosen as sample and audio characteristics provided by Spotify API service regarding 676 unique songs are analyzed. Following descriptive statistics of Turkey Music Market, clustering methodology is employed and three different clusters for songs are concluded. Finally, decision tree methodology is employed to classify the dataset with popularity scores and audio characteristics together, while loudness and energy characteristics are found as significant classifiers.

scholarly journals A COMPARISON STUDY OF DIFFERENT KERNEL FUNCTIONS FOR SVM-BASED CLASSIFICATION OF MULTI-TEMPORAL POLARIMETRY SAR DATA

2014 ◽  
Vol XL-2/W3 ◽  
pp. 281-285 ◽  
Author(s):  
B. Yekkehkhany ◽  
A. Safari ◽  
S. Homayouni ◽  
M. Hasanlou
Keyword(s):  
Kernel Function ◽  
Temporal Integration ◽  
Experimental Tests ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Rbf Kernel ◽  
Multi Temporal ◽  
Dimension Space

In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). <br><br> The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

scholarly journals Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel

2003 ◽  
Vol 15 (7) ◽  
pp. 1667-1689 ◽  
Author(s):  
S. Sathiya Keerthi ◽  
Chih-Jen Lin
Keyword(s):  
Support Vector Machines ◽  
Model Selection ◽  
Heuristic Method ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Vector Machines ◽  
Rbf Kernel ◽  
Linear Svm ◽  
Generalization Errors

Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular for practical use. Model selection in this class of SVMs involves two hyper parameters: the penalty parameter C and the kernel width σ. This letter analyzes the behavior of the SVM classifier when these hyper parameters take very small or very large values. Our results help in understanding the hyperparameter space that leads to an efficient heuristic method of searching for hyperparameter values with small generalization errors. The analysis also indicates that if complete model selection using the gaussian kernel has been conducted, there is no need to consider linear SVM.

scholarly journals Evaluation of Features in Detection of Dislike Responses to Audio–Visual Stimuli from EEG Signals

Computers ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 33 ◽  
Author(s):  
Firgan Feradov ◽  
Iosif Mporas ◽  
Todor Ganchev
Keyword(s):  
Visual Stimuli ◽  
Decomposition Methods ◽  
Automated Detection ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
K Nearest Neighbors ◽  
Eeg Activity ◽  
Rbf Kernel ◽  
Eeg Features

There is a strong correlation between the like/dislike responses to audio–visual stimuli and the emotional arousal and valence reactions of a person. In the present work, our attention is focused on the automated detection of dislike responses based on EEG activity when music videos are used as audio–visual stimuli. Specifically, we investigate the discriminative capacity of the Logarithmic Energy (LogE), Linear Frequency Cepstral Coefficients (LFCC), Power Spectral Density (PSD) and Discrete Wavelet Transform (DWT)-based EEG features, computed with and without segmentation of the EEG signal, on the dislike detection task. We carried out a comparative evaluation with eighteen modifications of the above-mentioned EEG features that cover different frequency bands and use different energy decomposition methods and spectral resolutions. For that purpose, we made use of Naïve Bayes classifier (NB), Classification and regression trees (CART), k-Nearest Neighbors (kNN) classifier, and support vector machines (SVM) classifier with a radial basis function (RBF) kernel trained with the Sequential Minimal Optimization (SMO) method. The experimental evaluation was performed on the well-known and widely used DEAP dataset. A classification accuracy of up to 98.6% was observed for the best performing combination of pre-processing, EEG features and classifier. These results support that the automated detection of like/dislike reactions based on EEG activity is feasible in a personalized setup. This opens opportunities for the incorporation of such functionality in entertainment, healthcare and security applications.

scholarly journals Consumers on Critique: A Survey of Classical Music Listeners’ Engagement with Professional Music Reviews

2020 ◽  
Vol 3 ◽  
pp. 205920432093133
Author(s):  
Elena Alessandri ◽  
Dawn Rose ◽  
Olivier Senn ◽  
Katrin Szamatulski ◽  
Antonio Baldassarre ◽  
...  
Keyword(s):  
Online Survey ◽  
Classical Music ◽  
Contemporary Music ◽  
Music Criticism ◽  
Professional Background ◽  
Musical Engagement ◽  
Streaming Services ◽  
Music Market ◽  
Typical Consumer ◽  
New Generation

Music criticism has a long tradition as a leading agent in the classical music discourse. However, some people question its function in the contemporary music market. We explored the topicality of classical music critique by asking: Who reads professional reviews today? And what do readers expect from review? Through an online survey (English/German), we profiled the listening habits of classical music listeners ( N = 1200) and their engagement with professional reviews. Our participants were more actively engaged with music, but contrary to the ‘highbrow’ stereotype, not more highly musically trained than the general population. They consumed music and opinion sources in a variety of ways. Approximately two-thirds ( n = 741) of the participants had recently engaged with professional reviews, which were perceived as the most useful form of opinion, followed by short written commentaries and, lastly, ratings. A multiple logistic regression model suggested that the typical consumer of professional music critique was older with higher levels of musical engagement and education, had a higher inclination to purchase music and lower usage of streaming services, and had a preference for detailed reviews from traditional sources (e.g. newspapers). According to review readers, reviews should cover a variety of topics and offer evaluations underpinned with reasons. Reviewers should be constructive, open-minded, respectful, and well informed; their professional background was less relevant. Professional reviews should not necessarily provide a recommendation on what to buy, but rather guide listeners’ musical appreciation and understanding. Professional criticism still has an audience, although more so among older, musically educated listeners. Critics need to explore various channels in order to connect to a new generation of classical music listeners.

scholarly journals Prediction of Alzheimer’s Disease Using Machine Learning Classifiers

2020 ◽  
Vol 9 (3) ◽  
pp. 116-120
Author(s):  
Mansour Rezaei ◽  
Ehsan Zereshki ◽  
Soodeh Shahsavari ◽  
Mohammad Gharib Salehi ◽  
Hamid Sharini
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Correct Diagnosis ◽  
Medical Science ◽  
Screening Tests ◽  
Support Vector ◽  
Svm Classifier ◽  
Rbf Kernel ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Background: Alzheimer’s disease (AD) is the most common brain failure for which no cure has yet been found. The disease starts with a disturbance in the brain structure and then it manifests itself clinically. Therefore, by timely and correct diagnosis of changes in the structure of the brain, the occurrence of this disease or at least its progression can be prevented. Due to the fact that magnetic resonance imaging (MRI) can be used to obtain very useful information from the brain, and also because it is non-invasive, this method has been considered by researchers. Materials and Methods: The data were obtained from an MRI database (MIRIAD) of 69 subjects including 46 AD patients and 23 healthy controls (HC). Individuals were categorized based on two criteria including NINCDS-ADRAD and MMSE, as the gold standard. In this paper, we used the support vector machine (SVM) and Bayesian SVM classifiers. Results: Using the SVM classifier with Gaussian radial basis function (RBF) kernel, we distinguished AD and HC with an accuracy of 88.34%. The most important regions of interest (ROIs) in this study included right para hippocampal gyrus, left para hippocampal gyrus, right hippocampus, and left hippocampus. Conclusion: This study showed that the SVM model with Gaussian RBF kernel can distinguish AD from HC with high accuracy. These studies are of great importance in medical science. Based on the results of this study, MRI centers and neurologists can perform AD screening tests in people over the age of 50 years.

scholarly journals Feature Extraction and Classification of MRI Using Hybrid RBF Kernel and SVM

2021 ◽  
pp. 418-426
Author(s):  
Suhas S ◽  
Dr. C. R. Venugopal
Keyword(s):  
Support Vector Machine ◽  
Image Retrieval ◽  
Classification Accuracy ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Mr Images ◽  
Rbf Kernel

An enhanced classification system for classification of MR images using association of kernels with support vector machine is developed and presented in this paper along with the design and development of content-based image retrieval (CBIR) system. Content of image retrieval is the process of finding relevant image from large collection of image database using visual queries. Medical images have led to growth in large image collection. Oriented Rician Noise Reduction Anisotropic Diffusion filter is used for image denoising. A modified hybrid Otsu algorithm termed is used for image segmentation. The texture features are extracted using GLCM method. Genetic algorithm with Joint entropy is adopted for feature selection. The classification is done by support vector machine along with various kernels and the performance is validated. A classification accuracy of 98.83% is obtained using SVM with GRBF kernel. Various features have been extracted and these features are used to classify MR images into five different categories. Performance of the MC-SVM classifier is compared with different kernel functions. From the analysis and performance measures like classification accuracy, it is inferred that the brain and spinal cord MRI classification is best done using MC- SVM with Gaussian RBF kernel function than linear and polynomial kernel functions. The proposed system can provide best classification performance with high accuracy and low error rate.

scholarly journals A combination of CSP-based method with soft margin SVM classifier and generalized RBF kernel for imagery-based brain computer interface applications

2020 ◽  
Vol 79 (25-26) ◽  
pp. 17521-17549 ◽  
Author(s):  
Amin Hekmatmanesh ◽  
Huapeng Wu ◽  
Fatemeh Jamaloo ◽  
Ming Li ◽  
Heikki Handroos
Keyword(s):  
Brain Computer Interface ◽  
Computer Interface ◽  
Svm Classifier ◽  
Soft Margin ◽  
Rbf Kernel

A Novel Hybrid Model for Image Classification

2011 ◽  
Vol 48-49 ◽  
pp. 98-101
Author(s):  
Jie Xu ◽  
Rong Zhu ◽  
Bo Hong
Keyword(s):  
Image Classification ◽  
Feature Space ◽  
Image Features ◽  
Learning Ability ◽  
Support Vector ◽  
Svm Classifier ◽  
Nonlinear Dimensionality Reduction ◽  
Training Process ◽  
Rbf Kernel ◽  
Best Parameters

Image classification poses challenges to retrieval technology. Though the Support Vector Machine (SVM) has been successfully applied to pattern recognition, its performance is limited by the feature space and parameters in the training process. Our work thus has two central themes. Construct the optimum feature space for training SVM from image features extraction by nonlinear dimensionality reduction based on manifold learning, and meanwhile establish the RBF kernel based SVM classifier by training with the best parameters with a global search capacity of the Quantum-behaved Particle Swarm Optimization (QPSO). Experiments show that our model not only improves the learning ability, but also significantly enhances the accuracy of image classification.

Computer aided system for breast cancer in digitized mammogram using shearlet band features with LS-SVM classifier

2016 ◽  
Vol 14 (03) ◽  
pp. 1650017 ◽  
Author(s):  
M. Kanchana ◽  
P. Varalakshmi
Keyword(s):  
Breast Cancer ◽  
Kernel Functions ◽  
Polynomial Kernel ◽  
Svm Classifier ◽  
Operating Characteristics ◽  
Directional Information ◽  
Rbf Kernel ◽  
Analysis Society ◽  
Life Threatening ◽  
Leave One Out

Breast cancer is life threatening and dangerous diseases among the women across the world. In this paper, mammogram image classification performed using LS-SVM with various kernels functions namely, Gaussian Radial Basis Function (GRBF) kernel, Polynomial kernel, Quadratic kernel, Linear kernel and MLP kernel. Shearlet transform is a multidimensional version of the composite dilation wavelet transform, and is especially designed to address anisotropic and directional information at various scales and directions, which is used to decompose the regions of interest (ROI) image after preprocessing stage. Initially, mammogram images are transformed into different resolution levels from 2 levels to 4 levels with various directions varying from 2 to 64. The evaluation of the system is carried out on the Mammography Image Analysis Society (MIAS) database. From the experimental analysis, based on classification accuracy and Receiver Operating Characteristics (ROC), it is concluded that LS-SVM with Gaussian RBF kernel function outperforms than Quadratic, polynomial, linear and MLP kernel functions. The classifiers were validated with leave-one-out (training) and cross-validation (testing) modes.

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