7 accepted papers
In speech deepfake detection, one of the critical aspects is developing detectors able to generalize on unseen data and distinguish fake signals across different datasets. Common approaches to this challenge involve incorporating diverse data into the training process or fine-tuning models on unseen…
Speech emotion retrieval is an important technique for large-scale and high-quality data collection. Conventional approach using ensemble of classification models might limit the retrieved emotion diversity and/or underperform in out-of-domain acoustic conditions. Natural language is diverse and agn…
In the design of traffic monitoring solutions for optimizing the urban mobility infrastructure, acoustic vehicle counting models have received attention due to their cost effectiveness and energy efficiency. Although deep learning has proven effective for visual traffic monitoring, its use has not b…
Conventional audio classification relied on predefined classes, lacking the ability to learn from free-form text. Recent methods unlock learning joint audio-text embeddings from raw audio-text pairs describing audio in natural language. Despite recent advancements, there is little exploration of sys…
Several approaches have been proposed to pre-train an audio encoder to learn fundamental audio knowledge. These training frameworks range from supervised learning to self-supervised learning with a contrastive objective under multi-modal supervision. However, these approaches are constrained to a si…
Design and execution of high fidelity acoustic sensing in complex environments poses a number of practical challenges, from accurately measuring the geometry of the setup and estimating the channel response, to time synchronization amongst the sources and receivers. When acoustic experiments are per…
Automatic audio-visual urban traffic understanding is a growing area of research with many potential applications of value to industry, academia, and the public sector. Yet, the lack of well-curated resources for training and evaluating models to research in this area hinders their development. To a…