Computer-aided Melody Note Transcription Using the Tony Software: Accuracy and Efficiency

accepteddate-added: 2015-05-24 19:18:46 +0000 date-modified: 2017-12-28 10:36:36 +0000 keywords: Tony, melody, note, transcription, open source software bdsk-url-1: https://code.soundsoftware.ac.uk/attachments/download/1423/tony-paper_preprint.pdfdate-added: 2015-05-24 19:18:46 +0000 date-modified: 2017-12-28 10:36:36 +0000 keywords: Tony, melody, note, transcription, open source software bdsk-url-1: https://code.soundsoftware.ac.uk/attachments/download/1423/tony-paper_preprint.pdfWe present Tony, a software tool for the interactive an- notation of melodies from monophonic audio recordings, and evaluate its usability and the accuracy of its note extraction method. The scientific study of acoustic performances of melodies, whether sung or played, requires the accurate transcription of notes and pitches. To achieve the desired transcription accuracy for a particular application, researchers manually correct results obtained by automatic methods. Tony is an interactive tool directly aimed at making this correction task efficient. It provides (a) state-of-the art algorithms for pitch and note estimation, (b) visual and auditory feedback for easy error-spotting, (c) an intelligent graphical user interface through which the user can rapidly correct estimation errors, (d) extensive export functions enabling further processing in other applications. We show that Tony’s built in automatic note transcription method compares favourably with existing tools. We report how long it takes to annotate recordings on a set of 96 solo vocal recordings and study the effect of piece, the number of edits made and the annotator’s increasing mastery of the software. Tony is Open Source software, with source code and compiled binaries for Windows, Mac OS X and Linux available from https://code.soundsoftware.ac.uk/projects/tony/

Determinants of health-related lifestyles among university students

AIMS: To investigate students’ health-related lifestyles and to identify barriers and social determinants of healthier lifestyles. METHODS: An online survey, two focus groups, and three in-depth interviews across 2014/15. A stratified by School size and random sample [n=468] of university students answered a 67-item questionnaire comprising six scales: RAPA, REAP-S, CAGE, FTND, SWEMWBS and ad hoc scale for drug use/misuse. Stratified by gender X2 tests were run to test associations/estimate risks and three multivariate Logistic Regression models were adjusted. A thematic approach guided the analysis of qualitative data. RESULTS: 60% of the respondents were insufficiently physically active, 47% had an unbalanced diet and 30% had low mental wellbeing. Alcohol drinkers vs. abstinent were almost equally distributed. 42% of alcohol drinkers reported getting drunk at least once a month. Smokers accounted for 16% of the respondents. Identified risk factors for suboptimal physical activity were: Being a woman, not using the university gym and smoking. For unbalanced diet: low mental wellbeing and drugs use. Poor mental wellbeing was predicted by unbalanced diet, not feeling like shopping and cooking frequently, and a lack of help-seeking behaviour in case of distress. Qualitative analysis revealed seven thematic categories: transition to new life, university environment and systems, finances, academic pressure, health promotion in campus and recommendations. CONCLUSIONS: This study provides robust evidence that the health-related lifestyles of the student population are worrying and suggests that the trend in chronic diseases associated with unhealthy lifestyles sustained over years might be unlikely to change in future generations. University students’ health-related lifestyle is a concern. Nine out of the identified ten predictors of problematic physical activity, nutrition and mental wellbeing, were environmental/societal or institutional barriers. Universities must expand corporate responsibilities to include the promotion of health as part of their core values

Evaluation of the Activity Concentration of 40K, 226Ra and 232Th in Soil and Associated Radiological Parameters of Shanono and Bagwai Artisanal Gold Mining Areas, Kano State, Nigeria

No Abstract

A Megacam Survey of Outer Halo Satellites. IV. Two foreground populations possibly associated with the Monoceros substructure in the direction of NGC2419 and Koposov2

The origin of the Galactic halo stellar structure known as the Monoceros ring is still under debate. In this work, we study that halo substructure using deep CFHT wide-field photometry obtained for the globular clusters NGC2419 and Koposov2, where the presence of Monoceros becomes significant because of their coincident projected position. Using Sloan Digital Sky Survey photometry and spectroscopy in the area surrounding these globulars and beyond, where the same Monoceros population is detected, we conclude that a second feature, not likely to be associated with Milky Way disk stars along the line-of-sight, is present as foreground population. Our analysis suggests that the Monoceros ring might be composed of an old stellar population of age t ~ 9Gyr and a new component ~ 4Gyr younger at the same heliocentric distance. Alternatively, this detection might be associated with a second wrap of Monoceros in that direction of the sky and also indicate a metallicity spread in the ring. The detection of such a low-density feature in other sections of this halo substructure will shed light on its nature.Comment: 10 pages, 10 figures, accepted for publication in Ap

Application of Machine Learning Identification and Classification of Muturu and Keteku Cattle Species for a Smart Agricultural Practice in Developing Countries such as Nigeria

Smart technologies have drastically reshaped the traditional methods of practicing agriculture as witnessed in husbandry. In this paper, a novel application of machine learning identification and classification of Muturu and Keteku cattle species in Nigeria was proposed as the mainstream model that enables the precision and intelligence perception of animal husbandry for a smart agricultural practice using enhanced mask region-based convolutional neural networks (mask R-CNN). A performance accuracy of 0.92 mAP (mean Average Precision) was achieved by the enhanced mask R-CNN model, making it on a par with the existing models