Automatic detection of head voice in sung musical signals via machine learning classification of time‐varying partial intensities

2006 ◽  
Vol 120 (5) ◽  
pp. 3029-3029
Author(s):  
Ryan J. Cassidy ◽  
Gautham J. Mysore
Keyword(s):  
Machine Learning ◽  
Automatic Detection ◽  
Time Varying ◽  
Machine Learning Classification ◽  
Head Voice ◽  
Musical Signals

scholarly journals MACHINE LEARNING CLASSIFICATION OF BEACH STATE FROM ARGUS IMAGERY

2020 ◽  
pp. 37
Author(s):  
Ashley Ellenson ◽  
Joshua Simmons ◽  
Greg Wilson ◽  
Tyler Hesser ◽  
Kristen D. Splinter
Keyword(s):  
Machine Learning ◽  
Wave Breaking ◽  
Strong Influence ◽  
Temporal Evolution ◽  
Dynamic Equilibrium ◽  
Machine Learning Algorithms ◽  
Beach Erosion ◽  
Time Varying ◽  
Machine Learning Classification

Nearshore beach morphology is of interest to coastal managers due to the strong influence it exerts on subaerial beach erosion, pollutant dispersal, and recreational safety. In particular, wave breaking conditions and nearshore hydrodynamics are highly dependent on sandbar configuration. The term 'beach state' describes specific planform configurations of nearshore morphology that are in dynamic equilibrium with the time-varying forcing conditions. Beach state categories were first introduced by Wright and Short (1984), who observed sandbar systems in Narrabeen-Collaroy, Australia and extended by Lippman and Holman (1990), based on observations of time-exposure Argus imagery of sandbar systems in at Duck, NC, USA. In this study, we use machine learning algorithms to identify beach states from Argus imagery at two distinct sites: Narrabeen-Collaroy (hereafter Narrabeen), SE Australia, and Duck, NC. We assess the ability of the algorithm to classify beach states at each site and its transferability from one beach to another. Additionally, we investigate the extent to which the spatial and temporal evolution of beach states influences the ability of the algorithm to classify images into discrete beach states.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/38OM8CseIww

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

2020 ◽  
Vol 13 (5) ◽  
pp. 508-523 ◽  
Author(s):  
Guan‐Hua Huang ◽  
Chih‐Hsuan Lin ◽  
Yu‐Ren Cai ◽  
Tai‐Been Chen ◽  
Shih‐Yen Hsu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Stage ◽  
Brain Images ◽  
Functional Brain ◽  
Machine Learning Classification

Handcrafted MRI radiomics and machine learning: Classification of indeterminate solid adrenal lesions

2021 ◽  
Vol 79 ◽  
pp. 52-58
Author(s):  
Arnaldo Stanzione ◽  
Renato Cuocolo ◽  
Francesco Verde ◽  
Roberta Galatola ◽  
Valeria Romeo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Classification

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 Machine learning classification of new asteroid families members

2020 ◽  
Author(s):  
Valerio Carruba
Keyword(s):  
Machine Learning ◽  
Family Members ◽  
Classification Algorithms ◽  
New Members ◽  
Machine Learning Classification ◽  
New Family ◽  
Robotic Telescope ◽  
Traditional Approaches ◽  
Asteroid Families

<p>Asteroid families are groups of asteroids that are the product of collisions or of the rotational fission of a parent object.  These groups are mainly identified in proper elements or frequencies domains.   Because of robotic telescope surveys, the number of known asteroids has increased from about 10,000 in the early 90's to more than 750,000 nowadays. Traditional approaches for identifying new members of asteroid families, like the hierarchical clustering method (HCM), may   struggle to keep up with the growing rate of new discoveries. Here we used machine learning classification algorithms to identify new family members based on the orbital distribution in proper (a,e,sin(i)) of previously known family constituents. We compared the outcome of nine classification algorithms from stand alone and ensemble approaches.  The Extremely Randomized Trees (ExtraTree) method had the highest precision, enabling to  retrieve up to 97% of family members identified with standard HCM.</p>

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