25,187 research outputs found
Evaluating raw waveforms with deep learning frameworks for speech emotion recognition
Speech emotion recognition is a challenging task in speech processing field.
For this reason, feature extraction process has a crucial importance to
demonstrate and process the speech signals. In this work, we represent a model,
which feeds raw audio files directly into the deep neural networks without any
feature extraction stage for the recognition of emotions utilizing six
different data sets, EMO-DB, RAVDESS, TESS, CREMA, SAVEE, and TESS+RAVDESS. To
demonstrate the contribution of proposed model, the performance of traditional
feature extraction techniques namely, mel-scale spectogram, mel-frequency
cepstral coefficients, are blended with machine learning algorithms, ensemble
learning methods, deep and hybrid deep learning techniques. Support vector
machine, decision tree, naive Bayes, random forests models are evaluated as
machine learning algorithms while majority voting and stacking methods are
assessed as ensemble learning techniques. Moreover, convolutional neural
networks, long short-term memory networks, and hybrid CNN- LSTM model are
evaluated as deep learning techniques and compared with machine learning and
ensemble learning methods. To demonstrate the effectiveness of proposed model,
the comparison with state-of-the-art studies are carried out. Based on the
experiment results, CNN model excels existent approaches with 95.86% of
accuracy for TESS+RAVDESS data set using raw audio files, thence determining
the new state-of-the-art. The proposed model performs 90.34% of accuracy for
EMO-DB with CNN model, 90.42% of accuracy for RAVDESS with CNN model, 99.48% of
accuracy for TESS with LSTM model, 69.72% of accuracy for CREMA with CNN model,
85.76% of accuracy for SAVEE with CNN model in speaker-independent audio
categorization problems.Comment: 14 pages, 6 Figures, 8 Table
Emotion Recognition in the Wild using Deep Neural Networks and Bayesian Classifiers
Group emotion recognition in the wild is a challenging problem, due to the
unstructured environments in which everyday life pictures are taken. Some of
the obstacles for an effective classification are occlusions, variable lighting
conditions, and image quality. In this work we present a solution based on a
novel combination of deep neural networks and Bayesian classifiers. The neural
network works on a bottom-up approach, analyzing emotions expressed by isolated
faces. The Bayesian classifier estimates a global emotion integrating top-down
features obtained through a scene descriptor. In order to validate the system
we tested the framework on the dataset released for the Emotion Recognition in
the Wild Challenge 2017. Our method achieved an accuracy of 64.68% on the test
set, significantly outperforming the 53.62% competition baseline.Comment: accepted by the Fifth Emotion Recognition in the Wild (EmotiW)
Challenge 201
Spoken affect classification : algorithms and experimental implementation : a thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Computer Science at Massey University, Palmerston North, New Zealand
Machine-based emotional intelligence is a requirement for natural interaction between humans and computer interfaces and a basic level of accurate emotion perception is needed for computer systems to respond adequately to human emotion. Humans convey emotional information both intentionally and unintentionally via speech patterns. These vocal patterns are perceived and understood by listeners during conversation. This research aims to improve the automatic perception of vocal emotion in two ways. First, we compare two emotional speech data sources: natural, spontaneous emotional speech and acted or portrayed emotional speech. This comparison demonstrates the advantages and disadvantages of both acquisition methods and how these methods affect the end application of vocal emotion recognition. Second, we look at two classification methods which have gone unexplored in this field: stacked generalisation and unweighted vote. We show how these techniques can yield an improvement over traditional classification methods
Affective Music Information Retrieval
Much of the appeal of music lies in its power to convey emotions/moods and to
evoke them in listeners. In consequence, the past decade witnessed a growing
interest in modeling emotions from musical signals in the music information
retrieval (MIR) community. In this article, we present a novel generative
approach to music emotion modeling, with a specific focus on the
valence-arousal (VA) dimension model of emotion. The presented generative
model, called \emph{acoustic emotion Gaussians} (AEG), better accounts for the
subjectivity of emotion perception by the use of probability distributions.
Specifically, it learns from the emotion annotations of multiple subjects a
Gaussian mixture model in the VA space with prior constraints on the
corresponding acoustic features of the training music pieces. Such a
computational framework is technically sound, capable of learning in an online
fashion, and thus applicable to a variety of applications, including
user-independent (general) and user-dependent (personalized) emotion
recognition and emotion-based music retrieval. We report evaluations of the
aforementioned applications of AEG on a larger-scale emotion-annotated corpora,
AMG1608, to demonstrate the effectiveness of AEG and to showcase how
evaluations are conducted for research on emotion-based MIR. Directions of
future work are also discussed.Comment: 40 pages, 18 figures, 5 tables, author versio
Stacked Convolutional and Recurrent Neural Networks for Music Emotion Recognition
This paper studies the emotion recognition from musical tracks in the
2-dimensional valence-arousal (V-A) emotional space. We propose a method based
on convolutional (CNN) and recurrent neural networks (RNN), having
significantly fewer parameters compared with the state-of-the-art method for
the same task. We utilize one CNN layer followed by two branches of RNNs
trained separately for arousal and valence. The method was evaluated using the
'MediaEval2015 emotion in music' dataset. We achieved an RMSE of 0.202 for
arousal and 0.268 for valence, which is the best result reported on this
dataset.Comment: Accepted for Sound and Music Computing (SMC 2017
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