openEAR

Notes:

openEAR is a toolkit for emotion and affect recognition developed at the Technische Universität München (TUM) and now maintained and supported by audEERING. It provides a range of algorithms and tools for extracting audio features, as well as classifiers and pre-trained models for use in emotion and affect recognition tasks. These tools are implemented in C++ and are designed to be efficient and easy to use.

openEAR has been used in a number of research projects and has proven to be a useful resource for researchers working in the field of emotion and affect recognition. It is available as open-source software, which means that it can be freely accessed and modified by anyone who is interested in using it.

  • Affect recognition refers to the ability to identify and interpret the emotional states of others. This can be done through verbal and nonverbal cues, such as facial expressions, tone of voice, and body language.
  • Affective agent is a computer program or system that is designed to recognize and respond to human emotions. These agents are often used in artificial intelligence (AI) applications, such as chatbots or virtual assistants, to provide a more personalized and human-like experience for users.
  • Emotion detection is the process of identifying and interpreting the emotions of an individual. This can be done through various methods, such as analyzing facial expressions, tone of voice, or other nonverbal cues.
  • Emotion detection system is a computer program or system that is designed to identify and interpret the emotions of an individual. These systems may use a variety of techniques, such as machine learning algorithms, to analyze emotional cues and make determinations about an individual’s emotional state.
  • Emotion recognition is the process of identifying and interpreting the emotions of an individual through the use of various techniques, such as machine learning algorithms or facial recognition software.
  • Talking books are books that are designed to be read out loud by a computer program or other device. These books may be designed for people with visual impairments or for children learning to read. They often include features such as text-to-speech functionality, which allows the book to be read out loud, and may also include other interactive elements such as games or quizzes.

Resources:

Wikipedia:

References:

See also:

100 Best Emotion Recognition Videos | Affective Dialog Systems | Emotional Agents 2016

The OpenEar library of 3D models of the human temporal bone based on computed tomography and micro-slicing
D Sieber, P Erfurt, S John, GR Dos Santos, D Schurzig… – Scientific data, 2019 – nature.com
Virtual reality surgical simulation of temporal bone surgery requires digitized models of the full anatomical region in high quality and colour information to allow realistic texturization. Existing datasets which are usually based on microCT imaging are unable to fulfil these …

PAL: A Wearable Platform for Real-time, Personalized and Context-Aware Health and Cognition Support
M Khan, G Fernandes, U Sarawgi, P Rampey… – arXiv preprint arXiv …, 2019 – arxiv.org
… app, and machine learning server. PAL’s wearable device uses multimodal sensors (camera, microphone, heartrate) with ondevice machine learning and openear audio output to provide realtime and contextaware cognitive, behavioral and psychological interventions …

Effective emotion recognition in movie audio tracks
M Kotti, Y Stylianou – 2017 IEEE International Conference on …, 2017 – ieeexplore.ieee.org
… Page 2. is summarised. Discussion is carried out in Section4, where a com- parison with a baseline KNN classifier as weil as with features sim- ilar to those employed in AudiolVisual Emotion Challenge (AVEC) 2011 as extracted by openSMILE/openEAR is performed …

Multimodal mixed emotion detection
AS Patwardhan – 2017 2nd International Conference on …, 2017 – ieeexplore.ieee.org
… Audio-visual data along with depth information was recorded using the infrared sensor (Kinect). OpenEar toolkit and Face API was used for calculation of features … The OpenEar toolkit [28] was used for capturing audio data …

Proposal of a Tool for the Stimation of Satisfaction in Usability Test Under the Approach of Thinking Aloud
GE Chanchí G, LF Muñoz S, WY Campo M – Telematics and Computing …, 2018 – Springer
… voice. The proposed tool makes use of the openEAR library in order to extract the acoustic properties of arousal and valence in order to classify an audio segement within Russell’s emotional model … 2.5 Librería OpenEar. This …

Significance of DNN-AM for Multimodal Sentiment Analysis
SV Gangashetty – … , MIKE 2017, Hyderabad, India, December 13 …, 2017 – books.google.com
… The prosodic and spectral features can be used to build the sentiment classifier [6]. Audio features like pitch, intensity and loudness, are extracted using OpenEAR toolkit and SVM, Hidden Markov Models (HMM), Gaussian Mixture Model (GMM) classifiers are built to identify the …

SPeech ACoustic (SPAC): A novel tool for speech feature extraction and classification
T Özseven, M Dü?enci – Applied Acoustics, 2018 – Elsevier
… The most popular of these toolboxes is Praat [5], followed by OpenSMILE [6], OpenEAR [7], HTK [3] and CSL [8] in that order. Other than these toolboxes there are various tools developed in small sizes … OpenEAR [7], C++, Free, Command Line + GUI …

Hybrid Modality Level Challenges Faced in Multi-Modal Sentiment Analysis
A VJ – 2019 – papers.ssrn.com
… exceeds nearly 17000 tweets. SentiBank was proposed by Borth Et al[11]. OpenEAR is a tool used in [21] to classify the multimodal sentiment expressed through spanish videos with 2-8 minutes length by converting them into linguistic features. Fig …

Significance of DNN-AM for Multimodal Sentiment Analysis
H Abburi, R Prasath, M Shrivastava… – … Conference on Mining …, 2017 – Springer
… The prosodic and spectral features can be used to build the sentiment classifier [6]. Audio features like pitch, intensity and loudness, are extracted using OpenEAR toolkit and SVM, Hidden Markov Models (HMM), Gaussian Mixture Model (GMM) classifiers are built to identify the …

A Method for Opinion Classification in Video Combining Facial Expressions and Gestures
AG Junior, EM dos Santos – 2018 31st SIBGRAPI Conference …, 2018 – ieeexplore.ieee.org
… voice strength were automatically extracted using the OpenEAR software. Information … vector. Likewise, audio features, such as tone and voice strength, were also extracted from audio segments using the OpenEAR software. Text …

Speech Emotion Recognition Based on Feature Fusion
Q Shen, G Chen, L Chang – 2017 2nd International Conference …, 2017 – atlantis-press.com
… 2009EC [4] has chosen the most widely used features and functions in prosodic features, sound quality characteristics and spectralcharacteristics, including 16 Low-Level Descriptors (LLDs) and 12 statistical functions as shown in the Table 2. And through the openEAR [5] open …

Deviations of acoustic low-level descriptors in speech features of a set of triplets, one with autism
H Yatawatte, C Poellabauer… – 2018 40th Annual …, 2018 – ieeexplore.ieee.org
… D. Feature Extraction and Analysis Acoustic LLDs from the Geneva Minimalistic Acous- tic Parameter Set (GeMAPS) [17] and the openS- MILE/openEAR emobase feature set [18] were used to interpret the deviations of speech of A from C1 and C2 …

Sentiment analysis using relative prosody features
H Abburi, KR Alluri, AK Vuppala… – 2017 Tenth …, 2017 – ieeexplore.ieee.org
… The audio features are extracted from OPENEAR tool [6]. To classify the polarity of opinions in online videos, combination of multiple modalities such as text, audio and video features is explored [7] [8]. Both decision level and feature level fusion methods are used to merge in …

Exploring the significance of low frequency regions in electroglottographic signals for emotion recognition
SG Ajay, D Pravena, D Govind, D Pradeep – International Symposium on …, 2017 – Springer
… Keywords. EGG MFCC GMM HTK openEAR. Download conference paper PDF. 1 Introduction. With the advancements in Machine Learning and Artificial Intelligence, the need for effective Human-Machine interaction has gained significant importance …

Cross-Corpus Speech Emotion Recognition Based on Multiple Kernel Learning of Joint Sample and Feature Matching
P Yang – Journal of Electrical and Computer Engineering, 2017 – hindawi.com
Journal of Electrical and Computer Engineering is a peer-reviewed, Open Access journal that publishes original research articles as well as review articles in several areas of electrical and computer engineering. The subject areas covered by the journal are:

Harnessing ai for augmenting creativity: Application to movie trailer creation
JR Smith, D Joshi, B Huet, W Hsu, J Cota – Proceedings of the 25th ACM …, 2017 – dl.acm.org
… Audio segmentation was performed using OpenSmile [9]. For each au- dio segment a full fledged emotional vector representation was extracted using OpenEAR [10] an OpenSmile extension dedicated to audio emotion recognition …

A new hybrid PSO assisted biogeography-based optimization for emotion and stress recognition from speech signal
CK Yogesh, M Hariharan, R Ngadiran, AH Adom… – Expert Systems with …, 2017 – Elsevier
… Hübner et al., 2010). There are standard toolkits to extract speech signal feature like PRAAT, APARAT, OpenSMILE and OpenEAR (Boersma and van Heuven, 2001, Eyben et al., 2009, Eyben et al., 2010). The extracted features …

Understanding and predicting empathic behavior in counseling therapy
V Pérez-Rosas, R Mihalcea, K Resnicow… – Proceedings of the 55th …, 2017 – aclweb.org
… Starting with the turn-by-turn segmentation,5 we extract pitch (F0) on each speaker-specific segment using OpenEar (Eyben et al., 2009).6 We then measure the corre- lation of all pitch values during counselor follow- ing turns and during counselor leading turns across the …

Audio and Text based Multimodal Sentiment Analysis using Features Extracted from Selective Regions and Deep Neural Networks
H Abburi – 2017 – web2py.iiit.ac.in
… The audio features are automatically extracted from the audio track of each video clip using OpenEAR software and Hidden Markov Models (HMM) classifier is built to detect the sentiment [37]. Instead of extracting all the features …

Multimodal Sentiment Analysis of Arabic Videos
H Najadat, F Abushaqra – Journal of Image and Graphics, 2018 – joig.org
… As we note that all of multimodal sentiment analysis researcher [7], [8], [17], [18] used the open source software OpenEAR [19] to extract audio features from an audio track. Then an advanced analysis for these features determined the emotional state of the speakers …

A Sneak Preview of Sentiment Analysis
R Mamtora, L Ragha – … Conference on Smart City and Emerging …, 2018 – ieeexplore.ieee.org
… features. The audio features were extracted using an open source tool OpenEar whereas the visual features were determined using a commercial software OKAO … Video: openFACE, Okao Vision Audio: openEAR, openSMILE TABLE …

Speech Emotion Recognition Integrating Paralinguistic Features and Auto-encoders in a Deep Learning Model
RD Fonnegra, GM Díaz – International Conference on Human-Computer …, 2018 – Springer
… eNTERFACE’05 database (see Sect. 3.1). In [9] a transfer learning model is proposed, in which 16 low-level descriptors (LLDs) and 12 functionals audio features are extracted using the openEAR toolkit. A transfer learning model, which …

Speech Emotion Recognition Based on a Recurrent Neural Network Classification Model
RD Fonnegra, GM Díaz – International Conference on Advances in …, 2017 – Springer
… 05 database (see Sect. 3.4). In [14] a transfer learning model is proposed, in which 16 low-level descriptors (LLDs) and 12 functionals audio features are extracted using the openEAR toolkit. A transfer learning model, which …

Exploiting IoT services by integrating emotion recognition in Web of Objects
MA Jarwar, I Chong – 2017 International Conference on …, 2017 – ieeexplore.ieee.org
… posts, received through extracting emoticons from textual data. EmoVoice [8], and openEAR [9] are the open source toolkits for the emotion feature extraction from voice data. For emotions from images, OpenFace [10] and Java …

Automated depression analysis using convolutional neural networks from speech
L He, C Cao – Journal of biomedical informatics, 2018 – Elsevier
… Finally, it is difficult to select an appropriate toolkit to extract the features. Various available toolkits are widely used to extract low-level features, such as openSMILE [21], COVAREP [22], SPTK [23], KALDI [24], YAAFE [25], and OpenEAR [26] …

A review of affective computing: From unimodal analysis to multimodal fusion
S Poria, E Cambria, R Bajpai, A Hussain – Information Fusion, 2017 – Elsevier

Synchronous prediction of arousal and valence using LSTM network for affective video content analysis
L Zhang, J Zhang – 2017 13th International Conference on …, 2017 – ieeexplore.ieee.org
… In addition, clip-level temporal features (ie median, maximum, and minimum) of audio-visual features are also included to capture the temporal characteristics of affect. The openEAR open source implementation [16] is used for assisting the feature extraction …

OpenMM: An Open-Source Multimodal Feature Extraction Tool.
MR Morales, S Scherer, R Levitan – INTERSPEECH, 2017 – isca-speech.org
… In order to identify sentiment, they explored visual, acoustic, and text features. Acoustic fea- tures were extracted using the open-source software OpenEAR [18], which extracts prosody, energy, voice probabilities, spec- trum, and cepstral features …

Visual, laughter, applause and spoken expression features for predicting engagement within ted talks
F Haider, FA Salim, S Luz, C Vogel, O Conlan… – …, 2017 – pdfs.semanticscholar.org
… As a result, we have 120,382 chunks of audio from 1338 videos for experi- mentation (clustering). Acoustic feature extraction is performed using openSMILE toolkit [23]. The feature set is extracted using the openEAR configuration file …

Aggression recognition using overlapping speech
I Lefter, CM Jonker – 2017 Seventh International Conference …, 2017 – ieeexplore.ieee.org
… pitch. The feature set consisting of 62 features were extracted with the OpenEAR software [9]. 5. Methodology Our aim is to explore the additive value of using over- lapping speech information in addition to acoustic features …

Learning emotion-discriminative and domain-invariant features for domain adaptation in speech emotion recognition
Q Mao, G Xu, W Xue, J Gou, Y Zhan – Speech Communication, 2017 – Elsevier
… 1, which has three parts: feature extractor, emotion predictor and domain predictor. Specifically, we use the 384 attributes extracted by the open source toolkit openEAR (Eyben et al., 2009) of the speech signal as the input of our model …

Convolutional neural networks and feature fusion for bimodal emotion recognition on the emotiW 2016 challenge
J Yan, B Yan, G Lu, Q Xu, H Li… – … Congress on Image …, 2017 – ieeexplore.ieee.org
… modality [1], [8], [16], [17]. For those making use of the audio modality approaches, they majority utilize the openSMILE or openEAR [36] tool to extract the feature of the audio modality [4], [6], [7], [14], [15], [19], [37]. In this paper, after …

Three-Dimensional, Kinematic, Human Behavioral Pattern-Based Features for Multimodal Emotion Recognition
A Patwardhan – Multimodal Technologies and Interaction, 2017 – mdpi.com
This paper presents a multimodal emotion recognition method that uses a feature-level combination of three-dimensional (3D) geometric features (coordinates, distance and angle of joints), kinematic features such as velocity and displacement of joints, and features extracted …

Unsupervised domain adaptation for speech emotion recognition using PCANet
Z Huang, W Xue, Q Mao, Y Zhan – Multimedia Tools and Applications, 2017 – Springer
… To ensure reproducibility, the open source toolkit openEAR [11] is utilized to extract 384 attributes … Eyben F, Wollmer M, Schuller B (2009) Openear-introducing the munich open-source emotion and affect recognition toolkit, pp 1–6Google Scholar. 12 …

Emotion Recognition through Intentional Context
PL IHASZ, M KOVACS… – International Journal of …, 2019 – jstage.jst.go.jp
… of two gated recurrent layers (GRU) [25] and a third, fully connected layer (Classifier#1 in Figure 2). From the audio streams of the dialogues, 23 sets, a total of 989 low-level audio features were extracted for each utterance with the emotion recognition toolkit of OpenEar [26] …

A module-based framework to emotion recognition by speech: a case study in clinical simulation
LO Sawada, LY Mano, JRT Neto, J Ueyama – Journal of Ambient …, 2019 – Springer
… to develop a decision model. For the visual channel, 3D data of the face, head, hand ges- tures and body movement were used and, for the audio, the openEar toolkit was used (Eyben et al. 2009). Thus, for the recognition of …

Effect of language independent transcribers on spoken language identification for different Indian languages
R Saikia, SR Singh, P Sarmah – 2017 International Conference …, 2017 – ieeexplore.ieee.org
… audio features and manually transcribed text are consid- ered as baseline systems. Audio features are extracted using OpenEAR tool. A total of 988 features are extracted for each speech sample. All the classification results are reported using 10 fold cross validation …

Multimodal Learning Analytics’ Past, Present, and Potential Futures.
M Worsley, R Martinez-Maldonado – CrossMMLA@ LAK, 2018 – ceur-ws.org
… conduct the analyses. Examples of existing tools that researchers used include: Linguistic Inquiry Word Count (LIWC) (Tausczik & Pennebaker, 2010), FACET (previously CERT) (Littlewort et al., 2011), OpenEAR. In other cases …

VoiLA: An online intelligent speech analysis and collection platform
S Hantke, T Olenyi, C Hausner… – 2018 First Asian …, 2018 – ieeexplore.ieee.org
… classification. openSMILE [23], is a cross-platform classification toolkit that includes libraries for feature extraction. Pre-trained mod- els and scripts to train custom models are available from the related openEAR toolkit [26]. openSMILE …

Data-independent vs. data-dependent dimension reduction for pattern recognition in high dimensional spaces.
TM Hassan – 2017 – bear.buckingham.ac.uk
… 87 Page 12. xi LIST OF TABLES Table 4-1 Coherence, Condition Number and Row Rank for the dictionaries ….. 66 Table 5-1 Low Level Descriptors (LLD) used in Acoustic analysis with openEAR ….. 71 …

Induction of emotional states in educational video games through a fuzzy control system
CA Lara, H Mitre-Hernandez, J Flores… – IEEE Transactions on …, 2018 – ieeexplore.ieee.org
… contour, Mel spectrum, spectral roll-off point, and spectral centroid. We used the open-source toolkit openEAR [26] to process the audio and extract the acoustic features. Functional extraction The size of the matrices generated by …

Mosby’s Pharmacology Memory NoteCards-E-Book: Visual, Mnemonic, and Memory Aids for Nurses
JA Zerwekh, JC Claborn – 2018 – books.google.com
… Medication should be at least room temperature, not cold. • Openear canal of an adult by drawing back on the pinna and slightly upward. • Open ear canal of a child less than 3 years of age by drawing back on the pinna and slightly downward …

EmotionML
F Burkhardt, C Pelachaud, BW Schuller… – Multimodal interaction with …, 2017 – Springer
… The openEAR extension of openSMILE provides pre-trained models for emotion recognition and a ready-to-use speech emotion recognition engine [26] … openEAR – Introducing the Munich open-source emotion and affect recognition toolkit …

Multimodal gender detection
M Abouelenien, V Pérez-Rosas, R Mihalcea… – Proceedings of the 19th …, 2017 – dl.acm.org
… Mohamed Abouelenien, Verónica Pérez-Rosas, Rada Mihalcea, and Mihai Burzo We extracted these features using OpenEar [12]. We used a prede- fined feature set, EmoBase, which consists of a set of 988 prosodic features frequently used for emotion recognition tasks …

3-D convolutional recurrent neural networks with attention model for speech emotion recognition
M Chen, X He, J Yang, H Zhang – IEEE Signal Processing …, 2018 – ieeexplore.ieee.org
… The log-Mels are extracted by the openEAR toolkit [23] with the window size of 25 ms and a 10-ms shift, and both the training and test log-Mels are normalized by the global mean and the standard deviation of the training set …

Hierarchical classification of speech emotions
T Liogien? – 2017 – epublications.vu.lt
… of data sets. Page 15. 15 The initial full features set consisted of 6552 different speech emotion features. The features for the experiment were extracted using OpenEAR toolkit [11]. Comparison of feature selection criteria Emotion …

Speech emotion recognition using convolutional-recurrent neural networks with attention model
Y Mu, LAH Gómez, AC Montes… – DEStech …, 2017 – dpi-proceedings.com
… REFERENCES 1. Eyben, F., Wöllmer, M., & Schuller, B. (2009, September). OpenEAR— introducing the Munich open-source emotion and affect recognition toolkit. In Affective Computing and Intelligent Interaction and Workshops, 2009. ACII 2009 …

Emotion recognition from speech using representation learning in extreme learning machines
S Glüge, R Böck, T Ott – 9th International Joint Conference …, 2017 – digitalcollection.zhaw.ch
… 3.3 Feature Extraction The openEAR toolkit (Eyben et al., 2009) was used to extract 6552 features as 39 functionals of 56 acoustic low-level descriptors and their corresponding first and second order delta regression coefficients …

What is ‘open-earedness’, and how can it be measured?
DJ Hargreaves, A Bonneville-Roussy – Musicae Scientiae, 2018 – journals.sagepub.com
Recent years have seen some fundamental changes in the study of responses to music: the growth of neuroscientific approaches, in particular, is throwing new lig…

Using vision and speech features for automated prediction of performance metrics in multimodal dialogs
V Ramanarayanan, P Lange, K Evanini… – ETS Research …, 2017 – Wiley Online Library
… We used OpenSMILE (Eyben, Weninger, Gross, & Schuller, 2013) to extract features from the audio signal, specifically, the standard openEAR emobase and emobase2010 feature sets containing 988 and 1,582 features, respectively, which are tuned for recognition of …

Joint system for speech separation from speaking and non-speaking background, and de-reverberation: Application on real-world recordings
B Wiem, BMM Anouar, B Aîcha – 2017 3rd International …, 2017 – ieeexplore.ieee.org
… Multimedia. ACM, 2010. p. 1459-1462. [2] F. Eyben, M. W¨ollmer, and B. Schuller. openEAR – introducing the munich open-source emotion and affect recognition toolkit. In Proc. of ACII 2009, volume I, pages 576–581. IEEE, 2009 …

* Address all correspondence to: falk@ emt. inrs. ca 1 University of Ottawa, Ottawa, Ontario, Canada 2 Institut National de la Recherche Scientifique, INRS-EMT …
H Al Osman – Emotion and Attention Recognition Based on …, 2017 – books.google.com
… 82 Emotion and Attention Recognition Based on Biological Signals and Images [72] [73] [74] [75] [76] [77] [78] [79] [80] [81] [82] [83] [84] F. Eyben, M. Wöllmer, and B. Schuller,“OpenEAR— introducing the Munich open- source emotion and affect recognition toolkit,” in 2009 3rd …

Learning utterance-level representations for speech emotion and age/gender recognition using deep neural networks
ZQ Wang, I Tashev – 2017 IEEE international conference on …, 2017 – ieeexplore.ieee.org
… 2016. [4] F. Eyben, M. Wöllmer, and B. Schuller, ?OpenEAR— Introducing the Munich Open-source Emotion and Affect Recognition Toolkit,? in Affective Computing and Intelligent Interaction and Workshops, 2009, pp. 1–6 …

Visual and Text Sentiment Analysis Through Hierarchical Deep Learning Networks
A Chaudhuri – 2019 – Springer
Page 1. 123 SPRINGER BRIEFS IN COMPUTER SCIENCE Arindam Chaudhuri Visual and Text Sentiment Analysis through Hierarchical Deep Learning Networks Page 2. SpringerBriefs in Computer Science Series Editors Stan …

Survey on AI-Based Multimodal Methods for Emotion Detection
C Marechal, D Miko?ajewski, K Tyburek… – … and Simulation for Big …, 2019 – Springer
… as physiological signals, visual signals, and other physical sensors, given suitable input components. 4.2 openEAR (Emotion and Affect Recognition). It consists of three major components: the core component is the SMILE (Speech …

Collaborative discriminative multi-metric learning for facial expression recognition in video
H Yan – Pattern Recognition, 2018 – Elsevier
Skip to main content …

Affective and behavioural computing: Lessons learnt from the First Computational Paralinguistics Challenge
B Schuller, F Weninger, Y Zhang, F Ringeval… – Computer Speech & …, 2019 – Elsevier
… Corpus of naturalistic children’s speech (Steidl, 2009). In light of the Challenge, the first widely used open-source affect analysis toolkit openEAR (Eyben et al., 2009) was introduced. A follow-up effort, the INTERSPEECH 2010 …

Effect of dimensional emotion in discrete speech emotion classification
J Huang, Y Li, J Tao – 2017 – ir.ia.ac.cn
… 12, no. 6, pp. 490–501, Oct. 2010. [7] F. Eyben, M. Wöllmer, B. Schuller, “OpenEAR—introducing the Munich open-source emotion and affect recognition toolkit,” Affective Computing and Intelligent Interaction and Workshops, 2009. ACII 2009. 3rd International Conference on …

EMODA: a tutor oriented multimodal and contextual emotional dashboard
M Ez-Zaouia, E Lavoué – … of the Seventh International Learning Analytics …, 2017 – dl.acm.org
… In 1http://www.noldus.com 2https://www.microsoft.com/cognitive-services/en-us/emotion-api Page 3. terms of tools, we can cite for example OpenEAR [28] and Beyond Verbal Emotion Service API3 that we chose for our study. Textual cues are also considered …

A recursive framework for expression recognition: from web images to deep models to game dataset
W Li, C Tsangouri, F Abtahi, Z Zhu – Machine Vision and Applications, 2018 – Springer
… of-the-art methods in solving the emotion recognition in the wild problem [2]. In our previous work, we show that the fine-tuning CNN feature is the most effctive feature among the three multimodal features we used—a LBP-TOP-based video feature, an openEAR energy/spectral …

Literature Survey and Datasets
S Poria, A Hussain, E Cambria – Multimodal Sentiment Analysis, 2018 – Springer
In this chapter we present the literature on unimodal and multimodal approaches to sentiment analysis and emotion recognition. As discussed in the Sect. 2.1, both of these topics can be brought…

Improving Emotion Recognition Performance by Random-Forest-Based Feature Selection
O Egorow, I Siegert, A Wendemuth – International Conference on Speech …, 2018 – Springer
… computing. Trans. Affect. Comput. 7(2), 190–202 (2016)CrossRefGoogle Scholar. 10. Eyben, F., Wöllmer, M., Schuller, B.: OpenEAR – introducing the Munich open-source emotion and affect recognition toolkit. In: Proceedings …

Internet of Things shaping smart cities: a survey
A Shahid, B Khalid, S Shaukat, H Ali… – Internet of Things and Big …, 2018 – Springer
… To start with, network identification is a first step leading towards the network hacking, different tools were used in order to identify the network; among which, OpenEar is a tool which monitors 16 channels simultaneously but at the same time assigns a unique number to each …

Dynamic multimodal measurement of depression severity using deep autoencoding
H Dibeklio?lu, Z Hammal… – IEEE journal of biomedical …, 2017 – ieeexplore.ieee.org
… For each recording, the database includes self-reported BDI, spatiotemporal Local Gabor Binary Patterns (LGBP-TOP) video features, and a large set of voice features (a set of low level voice descriptors and functionals extracted using the freely open- source openEAR [23] and …

The Role of Linguistic and Prosodic Cues on the Prediction of Self-Reported Satisfaction in Contact Centre Phone Calls.
J Luque, C Segura, A Sánchez, M Umbert… – …, 2017 – pdfs.semanticscholar.org
… 5, pp. 255–265, 2002. [19] F. Eyben, M. Wollmer, and B. Schuller, “Openear – Introducing the Munich open-source emotion and affect recognition toolkit,” in Affective Computing and Intelligent Interaction and Work- shops, 2009. ACII 2009 …

Wide Range Features-Based On Speech Emotion Recognition for Sensible Effective Services
V Ramesh – 2017 – ijsrcseit.com
… In: Eurospeech [16]. Eyben F, Wöllmer M, Schuller B (2009) OpenEAR—introducing the Munich open-source emotion and affect recognition toolkit. In: Affective Computing and Intelligent Interaction and Workshops, 2009. ACII 2009. 3rd International Conference on, pp 1-6 [17] …

Dual Exclusive Attentive Transfer for Unsupervised Deep Convolutional Domain Adaptation in Speech Emotion Recognition
ENN Ocquaye, Q Mao, H Song, G Xu, Y Xue – IEEE Access, 2019 – ieeexplore.ieee.org
Page 1. This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/. This article has been accepted for publication in a future issue of this journal, but has not been fully edited …

A survey of multimodal sentiment analysis
M Soleymani, D Garcia, B Jou, B Schuller… – Image and Vision …, 2017 – Elsevier
Skip to main content …

A Supplementary Feature Set for Sentiment Analysis in Japanese Dialogues
PL Ihasz, M Kovacs, I Piumarta… – ACM Transactions on …, 2019 – dl.acm.org
… Lengthening the segments to more than 3 seconds was assumed to introduce too much noise during vectorization for the training of the classifiers.) The segments were transformed with the OpenEar software (Eyben et al. 2009) into a set of low-level audio spectral bands …

Creating an optimal 3D printed model for temporal bone dissection training
K Takahashi, Y Morita, S Ohshima… – Annals of Otology …, 2017 – journals.sagepub.com
Objective: Making a 3-dimensional (3D) temporal bone model is simple using a plaster powder bed and an inkjet printer. However, it is difficult to reproduce air…

Deep features-based speech emotion recognition for smart affective services
AM Badshah, N Rahim, N Ullah, J Ahmad… – Multimedia Tools and …, 2019 – Springer
… In: EurospeechGoogle Scholar. 16. Eyben F, Wöllmer M, Schuller B (2009) OpenEAR— introducing the Munich open-source emotion and affect recognition toolkit. In: Affective Computing and Intelligent Interaction and Workshops, 2009. ACII 2009 …

A Study on the Deployment of a Service Robot in an Elderly Care Center
D Portugal, P Alvito, E Christodoulou… – International Journal of …, 2019 – Springer
… microphones on the Asus Xtion PRO Live sensor. Real-time emotion and affect recognition is possible using the open source emotion and affect recognition (openEAR) framework [41]. This is an efficient, multi-threaded, real …

Using a PCA-based dataset similarity measure to improve cross-corpus emotion recognition
I Siegert, R Böck, A Wendemuth – Computer Speech & Language, 2018 – Elsevier
Skip to main content …

Deep Learning Based Facial Computing-Data, Algorithms and Applications
W Li – 2017 – search.proquest.com
… methods in solving the facial expression recognition- in-the-wild problem [8]. In our previous work, we show that the finetuning CNN feature is the most effctive feature among the three multimodal features we used –a LBP-TOP-based video feature, an openEAR energy/spectral …

Emotion recognition from speech with recurrent neural networks
V Chernykh, P Prikhodko – arXiv preprint arXiv:1701.08071, 2017 – arxiv.org
Page 1. Emotion Recognition From Speech With Recurrent Neural Networks Vladimir Chernykh MIPT, Skoltech Moscow vladimir.chernykh@phystech.edu Pavel Prikhodko Skoltech Moscow p.prikhodko@skoltech.ru Abstract …

CHEAVD: a Chinese natural emotional audio–visual database
Y Li, J Tao, L Chao, W Bao, Y Liu – Journal of Ambient Intelligence and …, 2017 – Springer
This paper presents a recently collected natural, multimodal, rich-annotated emotion database, CASIA Chinese Natural Emotional Audio–Visual Database (CHEAVD), which aims to provide a basic resource…

How is emotion change reflected in manual and automatic annotations of different modalities?
Y Xiang – 2017 – essay.utwente.nl
Page 1. How is emotion change reflected in manual and automatic annotations of different modalities? Master’s thesis Ye Xiang HMI, University of Twente March 2017 Graduation committee: Dr.ir.D.Reidsma Prof.dr.DKJ Heylen Page 2. ABSTRACT …

Applicability of emotion recognition and induction methods to study the behavior of programmers
M Wrobel – Applied Sciences, 2018 – mdpi.com
Recent studies in the field of software engineering have shown that positive emotions can increase and negative emotions decrease the productivity of programmers. In the field of affective computing, many methods and tools to recognize the emotions of computer users were proposed …

Deep learning for human affect recognition: insights and new developments
PV Rouast, M Adam, R Chiong – IEEE Transactions on …, 2019 – ieeexplore.ieee.org
Page 1. JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 1 Deep Learning for Human Affect Recognition: Insights and New Developments Philipp V. Rouast, Student Member, IEEE, Marc TP Adam, and Raymond Chiong, Senior Member, IEEE …

Multimodal Sentiment Analysis: A Comparison Study
IO Hussien, YH Jazyah – 2018 – pdfs.semanticscholar.org
… (2016) English 47 YouTube dataset (20F, 27M) Text-Audio-Video Feature/ Decisoin Softwares Luxand FSDK 1.7,GAVAM, fusion openEAR and Concept-gram and Sentic Net-based features … Softwares using CLM-Z and GAVAM, openEAR and using CNN Siddiquie et al …

Slandail: A Security System for Language and Image Analysis-Project No: 607691
K Ahmad – Available at SSRN 3060047, 2017 – papers.ssrn.com
Page 1. Electronic copy available at: https://ssrn.com/abstract=3060047 Slandail Security System for Language and Image Analysis Project No: 607691 1-1 FINAL PUBLISHABLE SUMMARY REPORT Grant Agreement number …

Emotional and motivational aspects of digital reading
J Kaakinen, O Papp-Zipernovszky… – Learning to Read in …, 2018 – books.google.com
Page 152. chapter 6 Emotional and motivational aspects of digital reading Johanna K. Kaakinen1, Orsolya Papp-Zipernovszky2, Egon Werlen3, Nuria Castells4, Per Bergamin3, Thierry Baccino5 & Arthur M. Jacobs6 1University …

Fusion Sentimental Analysis in Self-Growth Broadcasting
S Kim, YI Yoon – 2018 IEEE International Conference on Big …, 2018 – ieeexplore.ieee.org
… 147–150. [17] F. Eyben, M. Wöllmer, B. Schuller, “Openear—introducing the munich open-source emotion and affect recognition toolkit,” 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, IEEE (2009), pp. 1-6 759

Instrumenting and Analyzing Fabrication Activities, Users, and Expertise
J Gong, F Anderson, G Fitzmaurice… – Proceedings of the 2019 …, 2019 – dl.acm.org
Page 1. Instrumenting and Analyzing Fabrication Activities, Users, and Expertise Jun Gong*†, Fraser Anderson*, George Fitzmaurice*, Tovi Grossman*‡ *Autodesk Research Toronto, ON, Canada {first.last}@autodesk.com †Dartmouth …

Speech emotion recognition based on gaussian mixture models and deep neural networks
IJ Tashev, ZQ Wang, K Godin – 2017 Information Theory and …, 2017 – ieeexplore.ieee.org
… 1607.01759. 2016. [3] F. Eyben, M. Wllmer, and B. Schuller, “OpenEAR—introducing the Munich Open-source Emotion and Affect Recognition Toolkit,” in Affective Computing and Intelligent Interaction and Workshops, 2009, pp. 1 …

Automated Assessment For The Therapy Success Of Foreign Accent Syndrome: Based on Emotional Temperature
T Chalasani – 2017 – diva-portal.org
Page 1. Master of science in Computer Science July 2017 Faculty of Computer Science & Engineering Blekinge Institute of Technology SE-371 79 Karlskrona Sweden AUTOMATED ASSESSMENT FOR THE THERAPY SUCCESS OF FOREIGN ACCENT SYNDROME …

6 Autism and speech, language, and emotion–a survey
E Marchi, Y Zhang, F Eyben, F Ringeval… – Signal and Acoustic …, 2018 – books.google.com
Page 153. Erik Marchi, Yue Zhang, Florian Eyben, Fabien Ringeval and Björn Schuller 6 Autism and speech, language, and emotion–a survey Abstract: Individuals with Autism Spectrum Conditions (ASC) show difficulties in …

Listening Intently: Towards a Critical Media Theory of Ethical Listening
J Feldman – 2017 – search.proquest.com
Page 1. Sponsoring Committee: Professor Martin Scherzinger, Chairperson Professor Mara Mills, Chairperson Professor Helen Nissenbaum LISTENING INTENTLY: TOWARDS A CRITICAL MEDIA THEORY OF ETHICAL LISTENING Jessica Feldman …

Social networking data analysis tools & challenges
A Sapountzi, KE Psannis – Future Generation Computer Systems, 2018 – Elsevier
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An Empirical Feasibility Study Of Existing Voice And Face Recognition Algorithms For User Authentication
MP Dapakarage – 2017 – documents.ucsc.lk
Page 1. An Empirical Feasibility Study Of Existing Voice And Face Recognition Algorithms For User Authentication A dissertation submitted for the Degree of Master of Computer Science MP Dapakarage University of Colombo School of Computing 2017 Page 2. i Declaration …

Development of an Automatic Attitude Recognition System: A Multimodal Analysis of Video Blogs
NA Madzlan – 2017 – tara.tcd.ie
Page 1. DEVELOPMENT OF AN AUTOMATIC ATTITUDE RECOGNITION SYSTEM: A MULTIMODAL ANALYSIS OF VIDEO BLOGS Noor Alhusna Madzlan Thesis submitted for the Degree of Doctor in Philosophy School of Linguistics, Speech & Communication Sciences …

Jointly Aligning and Predicting Continuous Emotion Annotations
S Khorram, M McInnis… – IEEE Transactions on …, 2019 – ieeexplore.ieee.org
Page 1. 1949-3045 (c) 2019 IEEE. Personal use is permitted, but republication/ redistribution requires IEEE permission. See http://www.ieee.org/ publications_standards/publications/rights/index.html for more information. This …

Field Studies with Multimedia Big Data: Opportunities and Challenges (Extended Version)
MM Krell, J Bernd, Y Li, D Ma, J Choi… – arXiv preprint arXiv …, 2017 – arxiv.org
Page 1. arXiv:1712.09915v1 [cs.MM] 28 Dec 2017 Page 2. Field Studies with Multimedia Big Data: Opportunities and Challenges (Extended Version) Mario Michael Krell, Julia Bernd, Yifan Li, Daniel Ma, Jaeyoung Choi, Michael Ellsworth, Damian Borth, and Gerald Friedland …

Analysis of Excitation Information in Expressive Speech
SR Kadiri – 2018 – web2py.iiit.ac.in
Page 1. Analysis of Excitation Information in Expressive Speech Thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electronics and Communication Engineering by Sudarsana Reddy Kadiri 201132574 …

Exploration of Complementary Features for Speech Emotion Recognition Based on Kernel Extreme Learning Machine
L Guo, L Wang, J Dang, Z Liu, H Guan – IEEE Access, 2019 – ieeexplore.ieee.org
Page 1. Received May 10, 2019, accepted June 2, 2019, date of publication June 6, 2019, date of current version June 24, 2019. Digital Object Identifier 10.1109/ACCESS.2019.2921390 Exploration of Complementary Features for Speech Emotion Recognition Based on …

Multimodal Depression Detection: An Investigation of Features and Fusion Techniques for Automated Systems
MR Morales – 2018 – academicworks.cuny.edu
Page 1. City University of New York (CUNY) CUNY Academic Works Dissertations, Theses, and Capstone Projects Graduate Center 5-2018 Multimodal Depression Detection: An Investigation of Features and Fusion Techniques for Automated Systems …

Managing the Scarcity of Monitoring Data through Machine Learning in Healthcare Domain
A Maxhuni – 2017 – eprints-phd.biblio.unitn.it
Page 1. PhD Dissertation International Doctorate School in Information and Communication Technologies DISI – University of Trento Managing the Scarcity of Monitoring Data through Machine Learning in Healthcare Domain Alban Maxhuni Advisors: Dr. Oscar Mayora …

An Improved Approach of Intention Discovery with Machine Learning for POMDP-based Dialogue Management
RR Raval – 2019 – scholar.uwindsor.ca
Page 1. University of Windsor Scholarship at UWindsor Electronic Theses and Dissertations Theses, Dissertations, and Major Papers 2019 An Improved Approach of Intention Discovery with Machine Learning for POMDP-based Dialogue Management …

Unsupervised learning for expressive speech synthesis
I Jauk – 2017 – upcommons.upc.edu
Page 1. Unsupervised Learning for Expressive Speech Synthesis Doctoral Thesis — Author: Igor Jauk Supervisor: Antonio Bonafonte June 29, 2017 Page 2. Alea iacta est. IVLIVS CAESAR Page 3. Abstract Nowadays, especially …