Von: ak, sekretaria via ak discourse
Datum: Fri, 20 Feb 2026
Betreff: [ak-discourse] Einladung zum Forschungskolloquium am 24.02.2026 / 18:00 Uhr
Liebe Freunde der Audiokommunikation,
am kommenden Dienstag den 24.02.2026, um 18:00 Uhr im Raum EN 324 präsentiert Annika Frommholz ihre Forschungsarbeit zum Thema:
„Predicting Perceived Semantic Expression of Functional Sounds using Unsupervised Feature Extraction and Ensemble Learning“
Dazu möchten wir Sie herzlich einladen. Die Kurzzusammenfassung finden Sie am Ende dieser E-Mail.
Der Zoom-Link für unsere auswärtigen Gäste lautet:
tu-berlin.zoom.us/j/65356423211
Meeting-ID: 653 5642 3211
Kenncode: 141962
Mit herzlichen Grüßen
Kirill Andreev & Stefan Weinzierl
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Annika Frommholz: Predicting Perceived Semantic Expression of Functional Sounds using Unsupervised Feature Extraction and Ensemble Learning
Functional sounds – typically brief, nonverbal audio cues used in the interfaces of electronic devices – play a critical role in human–machine interaction, yet remain largely unexplored within Music Information Retrieval. This study proposes a data-driven framework that uses musically informed audio features to predict the perceived semantic expression of functional sounds. Our three-stage pipeline first uses unsupervised feature extraction to transform 805 functional sounds into high-level topic distributions for timbre, chroma, and loudness using Gaussian Mixture Models and Latent Dirichlet Allocation. Second, these features train multi-output regression models to predict 19 perceptual dimensions from the FBMUX framework, with a Random Forest Regressor achieving the best performance. Finally, a listening experiment assesses how well the model predictions align with user perceptions. Interpretability analyses further reveal how individual features contribute to model predictions. This work contributes to MIR by expanding
its scope to the domain of functional, non-musical audio. It presents a novel application of MIR techniques, demonstrating that structured, musically informed descriptors can support perceptual modelling in domains with limited data and high subjective variance. It contributes a transferable approach and highlights the potential of MIR to inform human–machine interaction and sound design.
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Kirill Andreev
Er/ihm – he/him
Anrede/Form of address: Herr – Mr.
Sekretariat
Office
Technische Universität Berlin
Fakultät I / Inst. f. Sprache u. Kommunikation
FG Audiokommunikation / Sekr. EN-8
Faculty I / Institute for Language & Communication
Audio Communication Group / Office No. EN-8
Einsteinufer 17, 10587 Berlin
GERMANY
+49 (0)30 314-22236