Search Results for author: Andreas Triantafyllopoulos

Found 20 papers, 3 papers with code

The ACM Multimedia 2023 Computational Paralinguistics Challenge: Emotion Share & Requests

no code implementations28 Apr 2023 Björn W. Schuller, Anton Batliner, Shahin Amiriparian, Alexander Barnhill, Maurice Gerczuk, Andreas Triantafyllopoulos, Alice Baird, Panagiotis Tzirakis, Chris Gagne, Alan S. Cowen, Nikola Lackovic, Marie-José Caraty, Claude Montacié

The ACM Multimedia 2023 Computational Paralinguistics Challenge addresses two different problems for the first time in a research competition under well-defined conditions: In the Emotion Share Sub-Challenge, a regression on speech has to be made; and in the Requests Sub-Challenges, requests and complaints need to be detected.


Dynamic Restrained Uncertainty Weighting Loss for Multitask Learning of Vocal Expression

no code implementations22 Jun 2022 Meishu Song, Zijiang Yang, Andreas Triantafyllopoulos, Xin Jing, Vincent Karas, Xie Jiangjian, Zixing Zhang, Yamamoto Yoshiharu, Bjoern W. Schuller

We propose a novel Dynamic Restrained Uncertainty Weighting Loss to experimentally handle the problem of balancing the contributions of multiple tasks on the ICML ExVo 2022 Challenge.

COVYT: Introducing the Coronavirus YouTube and TikTok speech dataset featuring the same speakers with and without infection

no code implementations20 Jun 2022 Andreas Triantafyllopoulos, Anastasia Semertzidou, Meishu Song, Florian B. Pokorny, Björn W. Schuller

As compared to other existing COVID-19 sound datasets, the unique feature of the COVYT dataset is that it comprises both COVID-19 positive and negative samples from all 65 speakers.

Exploring speaker enrolment for few-shot personalisation in emotional vocalisation prediction

1 code implementation14 Jun 2022 Andreas Triantafyllopoulos, Meishu Song, Zijiang Yang, Xin Jing, Björn W. Schuller

In this work, we explore a novel few-shot personalisation architecture for emotional vocalisation prediction.

Fatigue Prediction in Outdoor Running Conditions using Audio Data

no code implementations9 May 2022 Andreas Triantafyllopoulos, Sandra Ottl, Alexander Gebhard, Esther Rituerto-González, Mirko Jaumann, Steffen Hüttner, Valerie Dieter, Patrick Schneeweiß, Inga Krauß, Maurice Gerczuk, Shahin Amiriparian, Björn W. Schuller

Although running is a common leisure activity and a core training regiment for several athletes, between $29\%$ and $79\%$ of runners sustain an overuse injury each year.

Journaling Data for Daily PHQ-2 Depression Prediction and Forecasting

no code implementations6 May 2022 Alexander Kathan, Andreas Triantafyllopoulos, Xiangheng He, Manuel Milling, Tianhao Yan, Srividya Tirunellai Rajamani, Ludwig Küster, Mathias Harrer, Elena Heber, Inga Grossmann, David D. Ebert, Björn W. Schuller

Digital health applications are becoming increasingly important for assessing and monitoring the wellbeing of people suffering from mental health conditions like depression.

Probing Speech Emotion Recognition Transformers for Linguistic Knowledge

no code implementations1 Apr 2022 Andreas Triantafyllopoulos, Johannes Wagner, Hagen Wierstorf, Maximilian Schmitt, Uwe Reichel, Florian Eyben, Felix Burkhardt, Björn W. Schuller

Large, pre-trained neural networks consisting of self-attention layers (transformers) have recently achieved state-of-the-art results on several speech emotion recognition (SER) datasets.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +2

A Temporal-oriented Broadcast ResNet for COVID-19 Detection

no code implementations31 Mar 2022 Xin Jing, Shuo Liu, Emilia Parada-Cabaleiro, Andreas Triantafyllopoulos, Meishu Song, Zijiang Yang, Björn W. Schuller

Detecting COVID-19 from audio signals, such as breathing and coughing, can be used as a fast and efficient pre-testing method to reduce the virus transmission.

Computational Efficiency

An Overview & Analysis of Sequence-to-Sequence Emotional Voice Conversion

no code implementations29 Mar 2022 Zijiang Yang, Xin Jing, Andreas Triantafyllopoulos, Meishu Song, Ilhan Aslan, Björn W. Schuller

Emotional voice conversion (EVC) focuses on converting a speech utterance from a source to a target emotion; it can thus be a key enabling technology for human-computer interaction applications and beyond.

Voice Conversion

Climate Change & Computer Audition: A Call to Action and Overview on Audio Intelligence to Help Save the Planet

no code implementations10 Mar 2022 Björn W. Schuller, Alican Akman, Yi Chang, Harry Coppock, Alexander Gebhard, Alexander Kathan, Esther Rituerto-González, Andreas Triantafyllopoulos, Florian B. Pokorny

We categorise potential computer audition applications according to the five elements of earth, water, air, fire, and aether, proposed by the ancient Greeks in their five element theory; this categorisation serves as a framework to discuss computer audition in relation to different ecological aspects.

Multistage linguistic conditioning of convolutional layers for speech emotion recognition

no code implementations13 Oct 2021 Andreas Triantafyllopoulos, Uwe Reichel, Shuo Liu, Stephan Huber, Florian Eyben, Björn W. Schuller

In this contribution, we investigate the effectiveness of deep fusion of text and audio features for categorical and dimensional speech emotion recognition (SER).

Speech Emotion Recognition

Fairness and underspecification in acoustic scene classification: The case for disaggregated evaluations

no code implementations4 Oct 2021 Andreas Triantafyllopoulos, Manuel Milling, Konstantinos Drossos, Björn W. Schuller

Although these factors play a well-understood role in the performance of ASC models, most works report single evaluation metrics taking into account all different strata of a particular dataset.

Acoustic Scene Classification Fairness +1

audEERING's approach to the One-Minute-Gradual Emotion Challenge

no code implementations3 May 2018 Andreas Triantafyllopoulos, Hesam Sagha, Florian Eyben, Björn Schuller

This paper describes audEERING's submissions as well as additional evaluations for the One-Minute-Gradual (OMG) emotion recognition challenge.

Emotion Recognition

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