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Pitch

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AI Entrepreneurs Lab

EIT AI & Robotics Community, operated by 28DIGITAL

…customer needs, business models and pitches with experienced entrepreneurs, industry feedback and investor introductions. The programme runs in person in Paris, Brussels and Amsterdam from 9–13 November 2026, with up to 15 participants. Individuals or teams of two may apply with a pre-launch venture concept whose core is AI or robotics. Up to five €10,000 prizes are awarded competitively at Demo Day; selected participants may also receive travel and accommodation support. Apply by 21 October 2026 at the advertised 17:00 CET cutof

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Grant · Artificial Intelligence

Trade-offs between pitch and tempo matching in counter-singing nightingales

Juan Sebastian Calderon Garcia, Giacomo Costalunga, Tim P. Vogels, Daniela Vallentin · FENS Forum 2024

…frequency of the song, i.e., the pitch. While recent research has shown nightingales’ ability to adjust acoustic structure to match pitch, the flexibility of their temporal structure remains unexplored. Here, we show that nightingales exhibit a consistent rhythmic pattern in their whistle songs, with a shift in pitch distribution toward lower frequenci

ePoster · Ethology

Prosody in the voice, face, and hands changes which words you hear

Hans Rutger Bosker · Donders Institute of Radboud University

…information - cued through pitch, intensity, and duration - also known as the prosody of speech. In this contribution, I will argue that prosody shapes low-level speech perception, changing which speech sounds we hear. Perhaps the most notable example of how prosody guides word recognition is the phenomenon of lexical stress, whereby suprasegmental F0, intensity, and duration cues can distinguish otherwise segmentally identical words, such as "PLAto" vs. "plaTEAU" i

Tue, May 23, 2023 · 18:00 UTC

Seminar · Phonetics

Decoding the neural processing of speech

Tobias Reichenbach · Friedrich-Alexander-University

Understanding speech in noisy backgrounds requires selective attention to a particular speaker. Humans excel at this challenging task, while current speech recognition technology still struggles when background noise is loud. The neural mechanisms by which we process speech remain, however, poorly understood, not least due to the complexity of natural speech. Here we describe recent progress obtained through applying machine-learning to neuroimaging data of humans listening to speech in different types of background noise. In particular, we develop statistical models to relate characteristic f

Tue, Mar 23, 2021 · 11:00 UTC

Seminar · Neuroscience