ASMSP-P4: Sound Event Detection and Scene Analysis
Wed, 2 Sep, 14:00 - 15:40 Belgium Time (UTC +2)
Location: Balcony I West
Session Type: Poster
Track: ASMSP - Acoustic, Speech and Music Signal Processing

ASMSP-P4.1: ANALYSIS-DRIVEN PROCEDURAL GENERATION OF AN ENGINE SOUND DATASET WITH EMBEDDED CONTROL ANNOTATIONS
Robin Doerfler, Impulse Audio Lab GmbH, Germany; Lonce Wyse, Universitat Pompeu Fabra, Spain

ASMSP-P4.2: Beyond the Seen: A Generalized Framework for Environmental Sound Deepfake Detection
Krishna Naduvathra Revi, Mrityunjoy Bhattacharya, Shuib Akhthar Shaik Adam, University of Stuttgart, Germany

ASMSP-P4.3: RETAINING MIXTURE REPRESENTATIONS FOR DOMAIN GENERALIZED ANOMALOUS SOUND DETECTION
Phurich Saengthong, Tomoya Nishida, Kota Dohi, Natsuo Yamashita, Yohei Kawaguchi, Hitachi, Japan

ASMSP-P4.4: METRIC ANALYSIS FOR SPATIAL SEMANTIC SEGMENTATION OF SOUND SCENES
Mayank Mishra, Paul Magron, Romain Serizel, Université de Lorraine, France

ASMSP-P4.5: Evaluating the Temporal Detection Capability of Integrated Gradients Applied on Sound Classifier
Martynas Dumpis, Vilnius Gediminas Technical University, Lithuania; Tuomas Virtanen, Tampere University, Finland

ASMSP-P4.6: Multiresolution Neural Network for One-Class Learning of Machine Sounds
Xiran Zhang, Vincent Lostanlen, Mathieu Lagrange, CNRS, China

ASMSP-P4.7: Speech Content Privacy in Environmental Sound Recordings using Segment-wise Waveform Reversal
Modan Tailleur, Mathieu Lagrange, Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes, France, France; Pierre Aumond, Université Gustave Eiffel, CEREMA, UMRAE, Bouguenais, France, France; Vincent Tourre, Nantes Université, École Centrale Nantes, CNRS, LS2N, UMR 6004, Nantes, France, France

ASMSP-P4.8: FSD50K-Solo: Automated Curation of Single-Source Sound Events
Ningyuan Yang, Stony Brook University, United States; Sile Yin, Li-Chia Yang, Bryce Irvin, Xiao Quan, Marko Stamenovic, Shuo Zhang, Bose Corporation, United States

ASMSP-P4.9: Frequency Tracking Features for Domain-Robust and Data-Efficient Deep Siren Identification
Stefano Damiano, Thomas Dietzen, Toon van Waterschoot, KU Leuven, Belgium