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Aug 6 - Audio and AI Meetup
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torsdag 6 augusti 2026 · 18:00
Vad händer?
Join us on Aug 6 for a special edition of the AI, ML, and Computer Vision Meetup focused on audio use cases!
Date, Time and Location
Do Speech Models Actually Understand Speech? Evaluating Speech LLMs Under Realistic Spoken Instruction Conditions
Speech Large Language Models (SLLMs) are increasingly capable; but are we evaluating them the right way? Most benchmarks rely on text prompts, yet real users interact with these systems through speech, a modality that introduces noise, disfluencies, and stylistic variation that text simply doesn't capture.
In this talk, we present findings from a systematic study across 11 tasks, 12 languages, and five prompt styles, examining how prompt modality, language, and task type shape SLLM performance.
*About the Speaker*
Maike Züfle is a PhD student at the Karlsruhe Institute of Technology (KIT), working in Prof. Jan Niehues's group on interactive speech systems for more natural human–machine communication.
AI based Audio Forensics
In this presentation, attendees will discover several modules developed by Gradiant for the detection and analysis of synthetically generated or manipulated audio. The session will be delivered by one of the developers involved in the design and implementation of these technologies, providing first-hand insight into their capabilities and underlying methodology.
The presentation will cover the traceability module, which helps identify the origin of AI-generated content. It will also cover the segment detection tool, designed to locate manipulated regions within an audio recording, as well as the complete audio detection tool, which assesses whether an entire recording has been synthetically generated.
*About the Speaker*
Daniel Paniagua Ares is a research engineer at Gradiant. Graduated in computer engineering from the FIC and with a master's degree in AI from the VIU.
Curating, Searching, and Evaluating Audio Datasets in FiftyOne
In this talk, we'll start with the ESC-50 environmental-sound dataset to show how FiftyOne represents audio: browsing clips in the tabular view, rendering spectrograms directly in the sample grid with a custom renderer, and turning sounds into searchable vectors with CLAP embeddings. Then we'll demo a similarity-search panel that lets you query an entire audio collection by example clip or a natural-language prompt to quickly find matching sounds.
We'll conclude with a live research problem: Audio Moment Retrieval from the DCASE 2026 Challenge, where the goal is to localize the exact moment in a long recording that matches a text query. We'll frame this as temporal detection, evaluate predictions, and visualize ground-truth vs. predicted moments on an interactive timeline to intuitively expose model failure modes.
Attendees will leave with a concrete blueprint and open code for applying visual data-centric AI practices to their own audio and multimodal datasets.
*About the Speaker*
John Duncan is a Machine Learning Engineer, Customer Success at Voxel51. His research interests include vision, LiDAR, and audio perception for robots and intelligent systems.
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