FMA: A Dataset For Music Analysis

We introduce the Free Music Archive (FMA), an open and easily accessible dataset suitable for evaluating several tasks in MIR, a field concerned with browsing, searching, and organizing large music collections. The community's growing interest in feature and end-to-end learning is however restrained by the limited availability of large audio datasets. The FMA aims to overcome this hurdle by providing 917 GiB and 343 days of Creative Commons-licensed audio from 106,574 tracks from 16,341 artists and 14,854 albums, arranged in a hierarchical taxonomy of 161 genres. It provides full-length and high-quality audio, pre-computed features, together with track- and user-level metadata, tags, and free-form text such as biographies. We here describe the dataset and how it was created, propose a train/validation/test split and three subsets, discuss some suitable MIR tasks, and evaluate some baselines for genre recognition. Code, data, and usage examples are available at https://github.com/mdeff/fmaComment: ISMIR 2017 camera-read

A comprehensive survey of multi-view video summarization

[EN] There has been an exponential growth in the amount of visual data on a daily basis acquired from single or multi-view surveillance camera networks. This massive amount of data requires efficient mechanisms such as video summarization to ensure that only significant data are reported and the redundancy is reduced. Multi-view video summarization (MVS) is a less redundant and more concise way of providing information from the video content of all the cameras in the form of either keyframes or video segments. This paper presents an overview of the existing strategies proposed for MVS, including their advantages and drawbacks. Our survey covers the genericsteps in MVS, such as the pre-processing of video data, feature extraction, and post-processing followed by summary generation. We also describe the datasets that are available for the evaluation of MVS. Finally, we examine the major current issues related to MVS and put forward the recommendations for future research(1). (C) 2020 Elsevier Ltd. All rights reserved.This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2B5B01070067)Hussain, T.; Muhammad, K.; Ding, W.; Lloret, J.; Baik, SW.; De Albuquerque, VHC. (2021). A comprehensive survey of multi-view video summarization. Pattern Recognition. 109:1-15. https://doi.org/10.1016/j.patcog.2020.10756711510

Volume 11, Number 02

Full text of Volume 11, Number 02 of Reaching Through Teaching.https://digitalcommons.kennesaw.edu/rtt/1029/thumbnail.jp

Oral Literature in the Digital Age

Thanks to ever-greater digital connectivity, interest in oral traditions has grown beyond that of researcher and research subject to include a widening pool of global users. When new publics consume, manipulate and connect with field recordings and digital cultural archives, their involvement raises important practical and ethical questions. This volume explores the political repercussions of studying marginalised languages; the role of online tools in ensuring responsible access to sensitive cultural materials; and ways of ensuring that when digital documents are created, they are not fossilized as a consequence of being archived. Fieldwork reports by linguists and anthropologists in three continents provide concrete examples of overcoming barriers—ethical, practical and conceptual—in digital documentation projects. Oral Literature in the Digital Age is an essential guide and handbook for ethnographers, field linguists, community activists, curators, archivists, librarians, and all who connect with indigenous communities in order to document and preserve oral traditions

Examination and Assessment of Commercial Anatomical E-Learning Tools: Software Usability, Dual-Task Paradigms and Learning

Technological innovation is changing the landscape of higher education, and the competing interests and responsibilities of today’s learners have propelled the movement of post-secondary courses into the online environment. In the anatomical sciences, commercialized e-learning tools have become a critical component for teaching the intricacies of the human body when physical classroom space and cadaveric resources are limited. This dissertation comparatively assessed the impact of two commercial anatomical e-learning tools (1) a simple 2-dimensional e-learning tool (A.D.A.M. Interactive Anatomy) and (2) a complex tool that allows for a 3-dimensional perspective (Netter’s 3D Interactive Anatomy). The comparison was then extended to include a traditional visual-kinesthetic method of studying anatomy (i.e. a physical skeleton). Applying cognitive load theory and working memory limitations as guiding principles, a dual-task assessment with cross over design was used to evaluate cognitive load. Students were assessed using baseline knowledge tests, observation task reaction times (a measure of cognitive load), mental rotation test scores (a measure of spatial ability) and anatomy post-tests (a measure of knowledge recall). Results from experiments carried out in this thesis suggest that the value of commercial anatomical e-learning tools cannot be assessed adequately on the basis of an educator’s, or a software developer’s, intuition alone. Despite the delivery benefits offered by e-learning tools and the positive feedback they often receive, this research demonstrates that neither commercial e-learning tool conferred any instructional advantage over textbook images. In fact, later results showed that the visual-kinesthetic experience of physically manipulating a skeleton yielded major positive impacts on knowledge recall that A.D.A.M. Interactive Anatomy, as a visual only tool, failed to deliver. The results of this dissertation also suggest that the design of e-learning tools can differentially influence students based on their spatial ability. Moreover our results suggest that learners with low spatial ability may also struggle to relate anatomical knowledge if they are examined on contralateral images. By objectively assessing commercial anatomical e-learning tools against traditional, visual-kinesthetic modalities, educators can be confident that the learning tool they select will give their students the best chance to acquire an understanding of human anatomy