Abstract: Emotion recognition in conversation (ERC) has received much attention, lately, from researchers due to its potential widespread applications in diverse areas, such as health-care, education, and human resources. In this paper, we present Dialogue Graph Convolutional Network (DialogueGCN), a graph neural network based approach to ERC. We leverage self and inter-speaker dependency of the interlocutors to model conversational context for emotion recognition. Through the graph network, DialogueGCN addresses context propagation issues present in the current RNN-based methods. We empirically show that this method alleviates such issues, while outperforming the current state of the art on a number of benchmark emotion classification datasets.
Paper: https://arxiv.org/abs/1908.11540 (EMNLP 2019)
Blog Post: https://towardsdatascience.com/emotion-recognition-using-graph-convolutional-networks-9f22f04b244e
I am the primary author of the paper. Feel free to ask questions.
https://reddit.com/link/rz508g/video/ygq2qbr71ia81/player
We are pleased to announce the third year of the Emotion and Theme Recognition in Music task held within the MediaEval 2021 evaluation campaign.
The Benchmarking Initiative for Multimedia Evaluation (MediaEval) organizes an annual cycle of scientific evaluation tasks in the area of multimedia access and retrieval. In our task, we invite the participants to try their skills at predicting mood and theme tags associated with music recordings using audio analysis and machine learning algorithms.
The task is framed as an auto-tagging problem with tags specific to moods and themes (e.g., happy, dark, epic, melodic, love, film, space). The task uses the MTG-Jamendo dataset: https://mtg.github.io/mtg-jamendo-dataset/, presented at the Machine Learning for Music Discovery Workshop at ICML 2019: https://hdl.handle.net/10230/42015
All interested researchers are warmly welcomed to participate. The deadline for all submissions for the challenge is November 5. Participants will be able to present their results at the MediaEval Multimedia Benchmark Workshop, to be held on December 6-8 in Bergen, Norway with opportunity for online participation: https://multimediaeval.github.io/
The registration for the task is available at the MediaEval website. A full description of the task is available here: https://multimediaeval.github.io/2021-Emotion-and-Theme-Recognition-in-Music-Task/
There are 4 questions and this is for a University project. Filling this in would be a genuine huge help for me.
Thank you in advance!
HI guys.
Do you know of any free API for facial emotion recognition? It doesn't have to be too complex, but i couldn't find anything so far. Thanks in advance!
I’m pretty sure it was newer. Oh and there’s a Halloween mask on Amazon that reminds me of it but I can’t put a picture on this community for some God only knows reason. But if you want to see the hollowing mask search Amazon for: “Halloween Mask LED Halloween Costume LED Glow Scary”. It’s kind of Hacker / computer-esque… driving me nuts.
Hello!
I’m part of a team at the University of Aberdeen conducting an online study as part of my postgraduate research project looking at emotion recognition in adults with ADHD. We're using a dynamic morphing task to investigate this.
In addition, we're exploring the role of attention/working memory in emotion recognition – we want to know if performing another memory task at the same time as we're trying to recognise emotions affects how quickly and accurately we do this.
The experiment takes roughly 30-40 minutes to complete, and includes two small questionnaires.
We are looking for people aged 18-45 with and without ADHD to take part. If you're interested, please follow this link to participate: https://tstbl.co/211-383
Please note: Requires a computer running Google Chrome. Tablets/mobile devices are not supported.
If you know anyone else who might be interested, please feel free to crosspost this or share this link with others!
If you have any questions regarding the study, please feel free to comment/DM me, or contact t24ah20@abdn.ac.uk.
Thanks!
PEC/4710/2021/4
I hope this video can give even little strength to other families who are dealing SCJ issue...🙏 Video is in Korean but I translated into English so theres ENG subtitle.
Hello, I have a small comprehension problem and need your help.
I am currently working on a method for emotion recognition in brain waves using an EEG.
The input parameters are the raw EEG data and the output should be arousal and valence values between -1 to 1.
My steps so far:
- Data cleaning -> remove artifacts and filtering the data.
- Feature extraction.
The Features I used:
(Mean, Standard deviations, Means of the absolute values of the first differences, Means of the absolute values of the second differences, Skewness, Kurtosis, Variance, Peak-to-peak (PTP) amplitude, Integral approximation of the spectrum (Theta, LowerAlpha, UpperAlpha, Beta, Gamma), Linelength, Hjorth Activity, Hjorth Mobility, Hjorth Complexity, Petrosian fractal dimension, Hurst fractal dimension)
And the Output of the Feature extraction look like this
Each row represents a participant trial and in each column represents a feature divided into channels.
The channels are in the typical 10-20 system. (Fp1, Fp2, Fz ...)
So the Feature table roughly look like this:
| Participant 0 | FP1_Mean | FP1_Variance | ... | FP2_Mean |
|---|---|---|---|---|
| Participant 1 | FP1_Mean | FP1_Variance | ... | FP2_Mean |
Since this is my own data collected through a study, it is unfortunately not labeled, so it has to be clustered using an unsupervised clustering algorithm.
What would be the next steps ? Can someone help me with this ?
I study computer science, so I program all the algorithms myself in Python. But I don't know how to transform the output data to fit into a clustering algorithm to get the valence and arousal values afterwards
Hi Everyone! I am a BSc psychology student at the University of York and I would really appreciate your participation in a study for my dissertation.
I am investigating whether impaired emotion recognition commonly associated with Autism can be better explained by Alexithymia instead. This study can be completed by anyone aged 18 or above that does not have a diagnosis of Autism Spectrum Disorder. The experiment involves a face identity and emotion recognition task followed by two questionnaires. There is also a depression and anxiety questionnaire which is entirely optional. As usual, participation is completely anonymous and again, very greatly appreciated!
The implications of this research could help establish paradigms that would reduce misdiagnosis of ASD!
https://research.sc/participant/login/dynamic/22444227-BD01-45BB-9EE3-CE0A6B1D04F9
I build a face recognition and analysis system using Deep Learning (in german language). I attempted to develop an automatic registration system because of the coronavirus. This system should check :
1- If the visitor known or unknown
2- If the visitor wear a mask
3- Estimate the age and gender of the visitor
4- Estimate the face emotion of the visitor
- 44,091 or 75% full for a game with a non-traditional club/low supporter base (GWS) on a Friday night with no public transport/early start time.
- Sold out (55,656 fans) Dreamtime game between 2 non local teams. Sold out 24 hours.
- 22,077 watching Geelong v Collingwood in 2020 when capacity was half due to Covid restrictions. Works out to 75% of capacity.
- 12,304 to watch Carlton v Hawthorn at 3:40pm AWST on a Friday! during Covid scheduling.
State / Population / Teams
- Victoria / 6.6m / 10
- WA / 2.7m / 2
Watch Melbourne v Geelong sell out and don't even have to mention the Grand Final.
I think Tasmania needs and deserves a team.
The future isn't just Tasmania but equally or probably more deserving of a new AFL team is WA.
Facebook and YouTube are full of videos where the news or facts presented are totally false. I feel that a lot these can be very easily identified as potentially fake by the human brain - there is something about the tone or the manner in which the person is speaking that makes me suspicious of the video's credibility. Do you guys feel the same way? Does fake news audio actually have a distinctive emotion?
Fake news detection through emotion recognition is a well researched idea when it comes to natural language processing. But I haven't found any research which analyses the tone/emotion in the audio to determine if it is fake news. So is it not possible? Or was my search not thorough enough.
We are pleased to announce the third year of the Emotion and Theme Recognition in Music task held within the MediaEval 2021 evaluation campaign.
The Benchmarking Initiative for Multimedia Evaluation (MediaEval) organizes an annual cycle of scientific evaluation tasks in the area of multimedia access and retrieval. In our task, we invite the participants to try their skills at predicting mood and theme tags associated with music recordings using audio analysis and machine learning algorithms.
The task is framed as an auto-tagging problem with tags specific to moods and themes (e.g., happy, dark, epic, melodic, love, film, space). The task uses the MTG-Jamendo dataset: https://mtg.github.io/mtg-jamendo-dataset/, presented at the Machine Learning for Music Discovery Workshop at ICML 2019: https://hdl.handle.net/10230/42015
All interested researchers are warmly welcomed to participate. The deadline for all submissions for the challenge is November 5. Participants will be able to present their results at the MediaEval Multimedia Benchmark Workshop, to be held on December 6-8 in Bergen, Norway with opportunity for online participation: https://multimediaeval.github.io/
The registration for the task is available at the MediaEval website. A full description of the task is available here: https://multimediaeval.github.io/2021-Emotion-and-Theme-Recognition-in-Music-Task/
