Supplemental Material for (Not) Hearing Happiness: Predicting Fluctuations in Happy Mood From Acoustic Cues Using Machine Learning

Emotion ◽  
2019 ◽  
Keyword(s):  
Machine Learning ◽  
Acoustic Cues ◽  
Happy Mood

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.

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

2018 ◽  
Author(s):  
Aaron C. Weidman ◽  
Jessie Sun ◽  
Simine Vazire ◽  
Jordi Quoidbach ◽  
Lyle H Ungar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acoustic Analysis ◽  
Emotional State ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Acoustic Features ◽  
Total N ◽  
Happy Mood ◽  
Insight Into

Recent popular claims surrounding virtual assistants suggest that computers will soon be able to hear our emotions. Supporting this possibility, promising work has harnessed big data and emergent technologies to automatically predict stable levels of one specific emotion, happiness, at the community (e.g., counties) and trait (i.e., people) levels. Furthermore, research in affective science has shown that non-verbal vocal bursts (e.g., sighs, gasps) and specific acoustic features (e.g., pitch, energy) can differentiate between distinct emotions (e.g., anger, happiness), and that machine-learning algorithms can detect these differences. Yet, to our knowledge, no work has tested whether computers can automatically detect normal, everyday within-person fluctuations in one emotional state from acoustic analysis. To address this issue in the context of happy mood, across three studies (total N = 20,197), we asked participants to repeatedly report their state happy mood, and to provide audio recordings—including both direct speech and ambient sounds—from which we extracted acoustic features. Using three different machine learning algorithms (neural networks, random forests, and support vector machines) and two sets of acoustic features, we found that acoustic features yielded minimal predictive insight into happy mood above chance. Neither multilevel modeling analyses nor human coders provided additional insight into state happy mood. These findings suggest that it is not yet possible to automatically assess fluctuations in one emotional state (i.e., happy mood) from acoustic analysis, pointing to a critical future direction for affective scientists interested in acoustic analysis of emotion and automated emotion detection.

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
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