MediaEval 2015 - UNIZA System for the "Emotion in Music" task at MediaEval 2015

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In this working notes paper, we present the UNIZA system for the recognition of dynamic music emotional dimensions arousal and valence. The developed system is based on Support Vector Regression with Radial Basis kernel function. We selected 2 sets of features using stochastic evolutionary optimization algorithms namely Genetic Algorithm and Particle Swarm Algorithm. The models score the average Root Mean Square Error 0.3 605 for the valence dimension and 0.2540 for the arousal dimension. http://ceur-ws.org/Vol-1436/ http://www.multimediaeval.org

Education

UNIZA System
for the „Emotion in Music“ task
at MediaEval 2015
Ing.
Miroslav
Malík

Department
of
Telecommunica1ons
and
Mul1media

Faculty
of
Electrical
Engineering

University
of
Žilina,
Slovakia

Par9cle
Swamp

Op9miza9on
/
Gene9c
Algorithm

•  Parallel
run
of
PSO
/
GA

•  Best
individuals
from
both
algorithms
selected

into
next
itera1on
of
the
op1miza1on
process

•  ﬁtness
func1on
-‐
RMSE
between
predicted

and
ground
truth
labels

2

Audio
Features

•  Op1mal
features
from
baseline
set

•  Features
from
MIR
Toolbox

•  2
best
combina1ons
used:

– Op#mal_1
–
139
features

– Op#mal_2
–
129
features

– 72
iden1cal
features
(MFCC,
slope,
skewness...)

3

Results
of
our
system
on

the
development
data

4

•  (almost)
equal
result
obtained
using
only

approximately
50%
of
the
baseline
features

•  it
might
be
useful
to
apply
the
SVR
with

diﬀerent
kernel
parameters
and/or
also

diﬀerent
feature
sets
for
each
emo1onal

dimension

6

Conclusion

and
future
work

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This paper provides a description of the MediaEval 2015 “Affective Impact of Movies Task”, which is running for the fifth year, previously under the name “Violent Scenes Detection”. In this year’s task, participants are expected to create systems that automatically detect video content that depicts violence, or predict the affective impact that video content will have on viewers. Here we provide insights on the use case, task challenges, data set and ground truth, task run requirements and evaluation metrics. http://ceur-ws.org/Vol-1436/ http://www.multimediaeval.org

MediaEval 2015 - Affective Impact of Movies: Task Overview and Results

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In this paper, we describe the IRIT's approach used for the MediaEval 2015 "Emotion in Music" task. The goal was to predict two real-valued emotion dimensions, namely valence and arousal, in a time-continuous fashion. We chose to use recurrent neural networks (RNN) for their sequence modeling capabilities. Hyperparameter tuning was performed through a 10-fold cross-validation setup on the 431 songs of the development subset. With the baseline set of 260 acoustic features, our best system achieved averaged root mean squared errors of 0.250 and 0.238, and Pearson's correlation coefficients of 0.703 and 0.692, for valence and arousal, respectively. These results were obtained by first making predictions with an RNN comprised of only 10 hidden units, smoothed by a moving average filter, and used as input to a second RNN to generate the final predictions. This system gave our best results on the official test data subset for arousal (RMSE=0.247, r=0.588), but not for Valence. Valence predictions were much worse (RMSE=0.365, r=0.029). This may be explained by the fact that in the development subset, valence and arousal values were very correlated (r=0.626), and this was not the case with the test data. Finally, slight improvements over these figures were obtained by adding spectral atness and spectral valley features to the baseline set. http://ceur-ws.org/Vol-1436/ http://www.multimediaeval.org

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In this paper we describe our approach for the MediaEval's "Emotion in Music" task. Our method consists of deep Long-Short Term Memory Recurrent Neural Networks (LSTM-RNN) for dynamic Arousal and Valence regression, using acoustic and psychoacoustic features extracted from the songs that have been previously proven as effective for emotion prediction in music. Results on the challenge test demonstrate an excellent performance for Arousal estimation (r = 0.613 ± 0.278), but not for Valence (r = 0.026 ± 0.500). Issues regarding the quality of the test set annotations' reliability and distributions are indicated as plausible justifications for these results. By using a subset of the development set that was left out for performance estimation, we could determine that the performance of our approach may be underestimated for Valence (Arousal: r = 0.596 ± 0.386; Valence: r = 0.458 ± 0.551). http://ceur-ws.org/Vol-1436/ http://www.multimediaeval.org

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Samuel G. Fadel, RECOD, Brazil RECOD @ Placing Task of MediaEval 2016: A Ranking Fusion Approach for Geographic-Location Prediction of Multimedia Objects In Working Notes Proceedings of the MediaEval 2016 Workshop, Hilversum, Netherlands, October 20-21, CEUR-WS.org (2016) by Javier A. V. Muñoz, Lin Tzy Li, Ícaro C. Dourado, Keiller Nogueira, Samuel G. Fadel, Otávio A. B. Penatti1, Jurandy Almeida, Luís A. M. Pereira, Rodrigo T. Calumby, Jefersson A. dos Santos, Ricardo da S. Torres Paper: http://ceur-ws.org/Vol-1739/MediaEval_2016_paper_22.pdf Video: https://youtu.be/njZHg-5GR1I Abstract: We describe the approach proposed by the RECOD team for the estimation-based sub-task of Placing Task at MediaEval 2016. Our approach uses genetic programming (GP) to combine ranked lists defined in terms of textual and visual descriptors to automatically assign geographic locations to images and videos.

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Paper: http://ceur-ws.org/Vol-2882/paper40.pdf YouTube: https://youtu.be/SL5Hvu1mARY Trung-Quan Nguyen, Dang-Hieu Nguyen and Loc Tai Tan Nguyen : Use Visual Features From Surrounding Scenes to Improve Personal Air Quality Data Prediction Performance. Proc. of MediaEval 2020, 14-15 December 2020, Online. In this paper, we propose a method to predict the personal air quality index in an area by using the combination of the levels of the following pollutants: PM2.5, NO2, and O3, measured from the nearby weather stations of that area, and the photos of surrounding scenes taken at that area. Our approach uses the Inverse Distance Weighted (IDW) technique to estimate the missing air pollutant levels and then use regression to integrate visual features from taken photos to optimize the predicted values. After that, we can use those values to calculate the Air Quality Index (AQI). The results show that the proposed method may not improve the performance of the prediction in some cases.

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1. UNIZA System for the „Emotion in Music“ task at MediaEval 2015 Ing. Miroslav Malík Department of Telecommunica1ons and Mul1media Faculty of Electrical Engineering University of Žilina, Slovakia

2. UNIZA system 1

3. Par9cle Swamp Op9miza9on / Gene9c Algorithm •  Parallel run of PSO / GA •  Best individuals from both algorithms selected into next itera1on of the op1miza1on process •  ﬁtness func1on -‐ RMSE between predicted and ground truth labels 2

4. Audio Features •  Op1mal features from baseline set •  Features from MIR Toolbox •  2 best combina1ons used: – Op#mal_1 – 139 features – Op#mal_2 – 129 features – 72 iden1cal features (MFCC, slope, skewness...) 3

5. Results of our system on the development data 4

6. 5 Oﬃcial results

7. •  (almost) equal result obtained using only approximately 50% of the baseline features •  it might be useful to apply the SVR with diﬀerent kernel parameters and/or also diﬀerent feature sets for each emo1onal dimension 6 Conclusion and future work

8. THANK YOU FOR YOUR ATTENTION

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