scholarly journals Identifying the Russian voiceless non-palatalized fricatives /f/, /s/, and /ʃ/ from acoustic cues using machine learning

2021 ◽  
Vol 150 (3) ◽  
pp. 1806-1820
Author(s):  
Natalja Ulrich ◽  
Marc Allassonnière-Tang ◽  
François Pellegrino ◽  
Dan Dediu
Keyword(s):  
Machine Learning ◽  
Acoustic Cues

scholarly journals (Not) hearing happiness: Predicting fluctuations in happy mood from acoustic cues using machine learning.

Emotion ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 642-658 ◽  
Author(s):  
Aaron C. Weidman ◽  
Jessie Sun ◽  
Simine Vazire ◽  
Jordi Quoidbach ◽  
Lyle H. Ungar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acoustic Cues ◽  
Happy Mood

scholarly journals Acoustic Cues to Beat Induction: A Machine Learning Perspective

2006 ◽  
Vol 24 (2) ◽  
pp. 177-188 ◽  
Author(s):  
Fabien Gouyon ◽  
Gerhard Widmer ◽  
Xavier Serra ◽  
Arthur Flexer
Keyword(s):  
Machine Learning ◽  
Common Knowledge ◽  
Acoustic Properties ◽  
Acoustic Cues ◽  
Acoustic Features ◽  
Frequency Bands ◽  
Feature Sets ◽  
Special Relevance ◽  
Beat Induction ◽  
Large Corpus

This article brings forward the question of which acoustic features are the most adequate for identifying beats computationally in acoustic music pieces. We consider many different features computed on consecutive short portions of acoustic signal, among which those currently promoted in the literature on beat induction from acoustic signals and several original features, unmentioned in this literature. Evaluation of feature sets regarding their ability to provide reliable cues to the localization of beats is based on a machine learning methodology with a large corpus of beat-annotated music pieces, in audio format, covering distinctive music categories. Confirming common knowledge, energy is shown to be a very relevant cue to beat induction (especially the temporal variation of energy in various frequency bands, with the special relevance of frequency bands below 500 Hz and above 5 kHz). Some of the new features proposed in this paper are shown to outperform features currently promoted in the literature on beat induction from acoustic signals.We finally hypothesize that modeling beat induction may involve many different, complementary acoustic features and that the process of selecting relevant features should partly depend on acoustic properties of the very signal under consideration.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions
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