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Found 20 papers, 3 papers with code
Bringing the Discussion of Minima Sharpness to the Audio Domain: a Filter-Normalised Evaluation for Acoustic Scene Classification
The correlation between the sharpness of loss minima and generalisation in the context of deep neural networks has been subject to discussion for a long time.
Exploring Meta Information for Audio-based Zero-shot Bird Classification
Advances in passive acoustic monitoring and machine learning have led to the procurement of vast datasets for computational bioacoustic research.
The ACM Multimedia 2023 Computational Paralinguistics Challenge: Emotion Share & Requests
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.
An Overview of Affective Speech Synthesis and Conversion in the Deep Learning Era
Speech is the fundamental mode of human communication, and its synthesis has long been a core priority in human-computer interaction research.
Self-Supervised Attention Networks and Uncertainty Loss Weighting for Multi-Task Emotion Recognition on Vocal Bursts
Vocal bursts play an important role in communicating affect, making them valuable for improving speech emotion recognition.
Dynamic Restrained Uncertainty Weighting Loss for Multitask Learning of Vocal Expression
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
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
In this work, we explore a novel few-shot personalisation architecture for emotional vocalisation prediction.
Depression Diagnosis and Forecast based on Mobile Phone Sensor Data
Previous studies have shown the correlation between sensor data collected from mobile phones and human depression states.
Insights on Modelling Physiological, Appraisal, and Affective Indicators of Stress using Audio Features
Stress is a major threat to well-being that manifests in a variety of physiological and mental symptoms.
Fatigue Prediction in Outdoor Running Conditions using Audio Data
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
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
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)
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A Temporal-oriented Broadcast ResNet for COVID-19 Detection
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.
An Overview & Analysis of Sequence-to-Sequence Emotional Voice Conversion
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.
Dawn of the transformer era in speech emotion recognition: closing the valence gap
Recent advances in transformer-based architectures which are pre-trained in self-supervised manner have shown great promise in several machine learning tasks.
Climate Change & Computer Audition: A Call to Action and Overview on Audio Intelligence to Help Save the Planet
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
In this contribution, we investigate the effectiveness of deep fusion of text and audio features for categorical and dimensional speech emotion recognition (SER).
Fairness and underspecification in acoustic scene classification: The case for disaggregated evaluations
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.
audEERING's approach to the One-Minute-Gradual Emotion Challenge
This paper describes audEERING's submissions as well as additional evaluations for the One-Minute-Gradual (OMG) emotion recognition challenge.
