scholarly journals “Evacuate the dancefloor”: Exploring and classifying spotify music listening before and during the COVID-19 pandemic in DACH countries

2021 ◽  
Vol 30 ◽  
Author(s):  
Kework K. Kalustian ◽  
Nicolas Ruth
Keyword(s):  
Clustering Algorithm ◽  
Service Providers ◽  
Open Data ◽  
Svm Classifier ◽  
Music Listening ◽  
Circumplex Model ◽  
Test Scenario ◽  
Audio Features ◽  
Streaming Service ◽  
Input Variables

Many people used musical media via music streaming service providers to cope with the limitations of the COVID-19 pandemic. Accounting for such behavior from the perspective of uses-and-gratifications theory and situated cognition yields reliable explanations regarding people’s active and goal-oriented use of musical media. We accessed Spotify’s daily top 200 charts and their audio features from the DACH countries for the period during the first lockdown in 2020 and a comparable non-pandemic period situation in 2019 to support those theoretical explanations quantitatively with open data. After exploratory data analyses, applying a k-means clustering algorithm across the DACH countries allowed us to reduce the dimensionality of selected audio features. Following these clustering results, we discuss how these clusters are explainable using the arousal-valence-circumplex model and possibly be understood as (gratification) potentials that listeners can interact with to modulate their moods and thus emotionally cope with the stress of the pandemic. Then, we modeled a cross-validated binary SVM classifier to classify the two periods based on the extracted clusters and the remaining manifest variables (e.g., chart position) as input variables. The final test scenario of the classification task yielded high overall accuracy in classifying the periods as distinguishable classes. We conclude that these demonstrated approaches are generally suitable to classify the two periods based on the extracted mood clusters and the other input variables, and furthermore to interpret, by considering the model-related caveats, everyday music listening via those proxy variables as an emotion-focused coping strategy during the COVID-19 pandemic in DACH countries.

scholarly journals Identification of Micro-calcification in Mammogram for Breast Cancer Analysis using SVM Classifier

2018 ◽  
Vol 7 (2.16) ◽  
pp. 29
Author(s):  
Gaurav Makwana ◽  
Lalita Gupta
Keyword(s):  
Breast Cancer ◽  
Clustering Algorithm ◽  
Automatic Segmentation ◽  
Breast Cancer Diagnosis ◽  
The State ◽  
Svm Classifier ◽  
Common Disease ◽  
Test Technique ◽  
Novel Approach ◽  
Breast Calcification

Breast cancer is most common disease in women of all ages. To identify & confirm the state of tumor in breast cancer diagnosis, patients are undergo biopsy number of times to identify malignancy. Early detection of cancer can save the patient. In this paper a novel approach for automatic segmentation & classification of breast calcification is proposed. The diagnostic test technique for detection of breast condition is very costly & requires human expertise whereas proposed method can help in automatically identifying the disease by comparing the data with the standard database. In proposed method a database has been created to define various stage of breast calcification & testing images are pre-processed to resize, enhance & filtered to remove background noise. Clustering is performed by using k-means clustering algorithm. GLCM is used to extract out statistical feature like area, mean, variance, standard deviation, homogeneity, skewness etc. to classify the state of tumor. SVM classifier is used for the classification using extracted feature. 

scholarly journals DWD Geoportal – Converging open data, metadata and documentation in a user-friendly way

2021 ◽  
Author(s):  
Björn Reetz ◽  
Hella Riede ◽  
Dirk Fuchs ◽  
Renate Hagedorn
Keyword(s):  
Service Providers ◽  
Meteorological Data ◽  
Open Data ◽  
Time Of Day ◽  
Free Access ◽  
Free Text ◽  
Free Text Search ◽  
Easy Integration ◽  
Access To Data ◽  
User Friendly

<p>Since 2017, Open Data has been a part of the DWD data distribution strategy. Starting with a small selection of meteorological products, the number of available datasets has grown continuously over the last years. Since the start, users can access datasets anonymously via the website https://opendata.dwd.de to download file-based meteorological products. Free access and the variety of products has been welcomed by the general public as well as private met service providers. The more datasets are provided in a directory structure, however, the more tedious it is to find and select among all available data. Also, metadata and documentation were available, but on separate public websites. This turned out to be an issue, especially for new users of DWD's open data.</p><p>To help users explore the available datasets as well as to quickly decide on their suitability for a certain use case, the Open Data team at DWD is developing a geoportal. It enables free-text search along with combined access to data, metadata, and description along with interactive previews via OGC WMS.</p><p>Cloud technology is a suitable way forward for hosting the geoportal along with the data in its operational state. Benefits are expected for the easy integration of rich APIs with the geoportal, and the flexible and fast deployment and scaling of optional or prototypical services such as WMS-based previews. Flexibility is also mandatory to respond to fluctuating user demands, depending on time of day and critical weather situations, which is supported by containerization. The growing overall volume of meteorological data at DWD may mandate to allow customers to bring their code to the data – for on-demand processing including slicing and interpolation –  instead of transferring files to every customer. Shared cloud instances are the ideal interface for this purpose.</p><p>The contribution will outline a protoype version of the new geoportal and discuss further steps for launching it to the public.</p>

OpenMobileNetwork

2013 ◽  
Vol 9 (4) ◽  
pp. 46-64 ◽  
Author(s):  
Abdulbaki Uzun ◽  
Eric Neidhardt ◽  
Axel Küpper
Keyword(s):  
Network Topology ◽  
Linked Data ◽  
Service Providers ◽  
Open Data ◽  
Mobile Network ◽  
Base Station ◽  
Network Provisioning ◽  
Crowdsourced Data ◽  
Mobile Network Operators

Mobile network operators maintain data about their mobile network topology, which is mainly used for network provisioning and planning purposes restricting its full business potential. Utilizing this data in combination with the extensive pool of semantically modeled data in the Linking Open Data Cloud, innovative applications can be realized that would establish network operators as service providers and enablers in the highly competitive services market. In this article, the authors introduce the OpenMobileNetwork (available at http://www.openmobilenetwork.org/) as an open solution for providing approximated network topology data based on the principles of Linked Data along with a business concept for network operators to exploit their valuable asset. Since the quality of the estimated network topology is crucial when providing services on top of it, the authors further analyze and evaluate state-of-the-art approaches for estimating base station positions out of crowdsourced data and discuss the results in comparison to real base station locations.

Automating statistical diagrammatic representations with data characterization

2017 ◽  
Vol 17 (4) ◽  
pp. 316-334
Author(s):  
Pere Millán-Martínez ◽  
Pedro Valero-Mora
Keyword(s):  
Graphical Representation ◽  
Open Data ◽  
Graphical Representations ◽  
One Dimensional ◽  
The Public ◽  
Cognitive Studies ◽  
Multidimensional Databases ◽  
Diagrammatic Representations ◽  
Input Variables

The search for an efficient method to enhance data cognition is especially important when managing data from multidimensional databases. Open data policies have dramatically increased not only the volume of data available to the public, but also the need to automate the translation of data into efficient graphical representations. Graphic automation involves producing an algorithm that necessarily contains inputs derived from the type of data. A set of rules are then applied to combine the input variables and produce a graphical representation. Automated systems, however, fail to provide an efficient graphical representation because they only consider either a one-dimensional characterization of variables, which leads to an overwhelmingly large number of available solutions, a compositional algebra that leads to a single solution, or requires the user to predetermine the graphical representation. Therefore, we propose a multidimensional characterization of statistical variables that when complemented with a catalog of graphical representations that match any single combination, presents the user with a more specific set of suitable graphical representations to choose from. Cognitive studies can then determine the most efficient perceptual procedures to further shorten the path to the most efficient graphical representations. The examples used herein are limited to graphical representations with three variables given that the number of combinations increases drastically as the number of selected variables increases.

Feature Selection of Sudden Failure Based on Affinity Propagation Clustering

2012 ◽  
Vol 586 ◽  
pp. 241-246
Author(s):  
Li Min Li ◽  
Zhong Sheng Wang
Keyword(s):  
Feature Selection ◽  
Clustering Algorithm ◽  
Feature Space ◽  
Failure Diagnosis ◽  
Affinity Propagation ◽  
Svm Classifier ◽  
Incorrect Answer ◽  
Sudden Failure ◽  
Affinity Propagation Clustering ◽  
Selection Of

When diagnosing sudden mechanical failure, in order to make the result of classification more accurate, in this article we describe an affinity propagation clustering algorithm for feature selection of sudden machinery failure diagnosis. General methods of feature selection select features by reducing dimension of the features, at the same time changing the data in the feature space, which would result in incorrect answer to the diagnosis. While affinity propagation method is based on measuring similarity between features whereby redundancy therein is removed, and selecting the exemplar subset of features, while doesn't change the data in the feature space. After testing on clustering and taking the result of PCA and affinity propagation clustering as input of a same SVM classifier, we get the conclusion that the latter has lower error than the former.

scholarly journals An open ecosystem engagement strategy through the lens of global food safety

F1000Research ◽  
2015 ◽  
Vol 4 ◽  
pp. 129 ◽  
Author(s):  
Paul Stacey ◽  
Garin Fons ◽  
Theresa M Bernardo
Keyword(s):  
Food Safety ◽  
Service Providers ◽  
List Item ◽  
Open Data ◽  
Open Educational Resources ◽  
Educational Resources ◽  
List Type ◽  
Case Examples ◽  
Local Case ◽  
Global Food

The Global Food Safety Partnership (GFSP) is a public/private partnership established through the World Bank to improve food safety systems through a globally coordinated and locally-driven approach. This concept paper aims to establish a framework to help GFSP fully leverage the potential of open models.In preparing this paper the authors spoke to many different GFSP stakeholders who asked questions about open models such as:what is it?what’s in it for me?why use an open rather than a proprietary model?how will open models generate equivalent or greater sustainable revenue streams compared to the current “traditional” approaches? This last question came up many times with assertions that traditional service providers need to see opportunity for equivalent or greater revenue dollars before they will buy-in. This paper identifies open value propositions for GFSP stakeholders and proposes a framework for creating and structuring that value.Open Educational Resources (OER) were the primary open practice GFSP partners spoke to us about, as they provide a logical entry point for collaboration. Going forward, funders should consider requiring that educational resources and concomitant data resulting from their sponsorship should be open, as a public good. There are, however, many other forms of open practice that bring value to the GFSP. Nine different open strategies and tactics (Appendix A) are described, including: open content (including OER and open courseware), open data, open access (research), open government, open source software, open standards, open policy, open licensing and open hardware. It is recommended that all stakeholders proactively pursue "openness" as an operating principle.This paper presents an overall GFSP Open Ecosystem Engagement Strategy within which specific local case examples can be situated. Two different case examples, China and Colombia, are presented to show both project-based and crowd-sourced, direct-to-public paths through this ecosystem.

scholarly journals Fungal Disease in Cotton Leaf Detection and Classification using Neural Networks and Support Vector Machine

2019 ◽  
Vol 8 (10) ◽  
pp. 3664-3667 ◽  
Keyword(s):  
Support Vector Machine ◽  
Production Rate ◽  
Clustering Algorithm ◽  
Economic Status ◽  
Fungal Disease ◽  
Support Vector ◽  
Svm Classifier ◽  
Disease Detection ◽  
Cotton Leaf ◽  
Average Accuracy

Agriculture productivity is the main factor for improving economic status of India. Reduction in production rate is mainly due to various diseases in plants. Identification of plant disease in early stage is the main challenge for improving the production rate as well as economic status. This paper presents automatic disease detection in cotton crop for three types of diseases Alternaria Leaf Spot Fungal Disease (ALSFD), Grey Mildew Cotton Disease (GMCD), and Rust Foliar Fungal Disease (RFFD). The K-means clustering algorithm is used for disease segmentation for cotton leaf. The diseased cluster is segmented into three clusters. From cluster 2 the features Mean , Contrast, Energy, Correlation, Standard Deviation, Variance , Entropy, and Kurtosis are extracted. The extracted features for 30 samples are given to Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers for disease classification. The performance of these classifiers are compared. The ALSF disease is classified 77.4% for ANN and 84.3% for SVM, GMC disease is 87.8% for ANN and 98.7% in SVM, RFF disease is 90.1%for ANN and 93.2% for SVM. The overall average accuracy of ANN classifier is 85.1% for three diseases and overall average accuracy for SVM is 92.06% for three diseases. It is clearly observed from the analysis SVM classifier gives accurate disease detection compared to ANN.

scholarly journals The relationship between valence and chills in music: A corpus analysis

2020 ◽  
Author(s):  
Rémi de Fleurian ◽  
Marcus Thomas Pearce
Keyword(s):  
Computational Analysis ◽  
Corpus Analysis ◽  
Music Listening ◽  
Musical Preference ◽  
Audio Features ◽  
Personality Correlates ◽  
Control Set ◽  
The Difference ◽  
New Interpretation ◽  
The Relationship

Chills experienced in response to music listening have been linked to both happiness and sadness expressed by music. To investigate these conflicting effects of valence on chills, we conducted a computational analysis on a corpus of 988 tracks previously reported to elicit chills, by comparing them with a control set of tracks matched by artist, duration, and popularity. We analysed track-level audio features obtained with the Spotify Web API across the two sets of tracks, resulting in confirmatory findings that tracks which cause chills were sadder than matched tracks, and exploratory findings that they were also slower, less intense, and more instrumental than matched tracks on average. We also found that the audio characteristics of chills tracks were related to the direction and magnitude of the difference in valence between the two sets of tracks. We discuss these results in light of the current literature on valence and chills in music, provide a new interpretation in terms of personality correlates of musical preference, and review the advantages and limitations of our computational approach.

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 AMBIQUAL: Towards a Quality Metric for Headphone Rendered Compressed Ambisonic Spatial Audio

2020 ◽  
Vol 10 (9) ◽  
pp. 3188
Author(s):  
Miroslaw Narbutt ◽  
Jan Skoglund ◽  
Andrew Allen ◽  
Michael Chinen ◽  
Dan Barry ◽  
...  
Keyword(s):  
Service Providers ◽  
Spatial Audio ◽  
Localization Accuracy ◽  
Quality Metric ◽  
Audio Quality ◽  
Listening Tests ◽  
Objective Quality ◽  
Streaming Service ◽  
Quality Assessments

Spatial audio is essential for creating a sense of immersion in virtual environments. Efficient encoding methods are required to deliver spatial audio over networks without compromising Quality of Service (QoS). Streaming service providers such as YouTube typically transcode content into various bit rates and need a perceptually relevant audio quality metric to monitor users’ perceived quality and spatial localization accuracy. The aim of the paper is two-fold. First, it is to investigate the effect of Opus codec compression on the quality of spatial audio as perceived by listeners using subjective listening tests. Secondly, it is to introduce AMBIQUAL, a full reference objective metric for spatial audio quality, which derives both listening quality and localization accuracy metrics directly from the B-format Ambisonic audio. We compare AMBIQUAL quality predictions with subjective quality assessments across a variety of audio samples which have been compressed using the Opus 1.2 codec at various bit rates. Listening quality and localization accuracy of first and third-order Ambisonics were evaluated. Several fixed and dynamic audio sources (single and multiple) were used to evaluate localization accuracy. Results show good correlation regarding listening quality and localization accuracy between objective quality scores using AMBIQUAL and subjective scores obtained during listening tests.

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