What Really Matters? A Study into People's Instinctive Evaluation Metrics for Continuous Emotion Prediction in Music

Imbrasaite, Vaiva; Baltruaitis, Tadas; Robinson, Peter

doi:10.1109/acii.2013.106

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2014

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“…To evaluate the results, we used three metrics suggested for emotion prediction in music evaluation [26]: average Euclidean distance (combined dimensions), average root mean squared error and average squared Pearson correlation coefficient (per dimension).…”

Section: Methodsmentioning

confidence: 99%

Continuous Conditional Neural Fields for Structured Regression

Baltrušaitis

Robinson

Morency

2014

Computer Vision – ECCV 2014

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Abstract. An increasing number of computer vision and pattern recognition problems require structured regression techniques. Problems like human pose estimation, unsegmented action recognition, emotion prediction and facial landmark detection have temporal or spatial output dependencies that regular regression techniques do not capture. In this paper we present continuous conditional neural fields (CCNF) -a novel structured regression model that can learn non-linear input-output dependencies, and model temporal and spatial output relationships of varying length sequences. We propose two instances of our CCNF framework: Chain-CCNF for time series modelling, and Grid-CCNF for spatial relationship modelling. We evaluate our model on five public datasets spanning three different regression problems: facial landmark detection in the wild, emotion prediction in music and facial action unit recognition. Our CCNF model demonstrates state-of-the-art performance on all of the datasets used.

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Section: Methodsmentioning

confidence: 99%