Search Results for author:
Found 8 papers, 8 papers with code
Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warping
Adversarial examples generated by a surrogate model typically exhibit limited transferability to unknown target systems.
Multi-level graph learning for audio event classification and human-perceived annoyance rating prediction
Specifically, this paper proposes a lightweight multi-level graph learning (MLGL) based on local and global semantic graphs to simultaneously perform audio event classification (AEC) and human annoyance rating prediction (ARP).
AI-based soundscape analysis: Jointly identifying sound sources and predicting annoyance
(4) Generalization tests show that the proposed model's ARP in the presence of model-unknown sound sources is consistent with expert expectations and can explain previous findings from the literature on sound-scape augmentation.
Audio Event-Relational Graph Representation Learning for Acoustic Scene Classification
The results show the feasibility of recognizing diverse acoustic scenes based on the audio event-relational graph.
Acoustic Scene Classification
Graph Representation Learning
+1
Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation Learning
Sound events in daily life carry rich information about the objective world.
Multi-dimensional Edge-based Audio Event Relational Graph Representation Learning for Acoustic Scene Classification
Experiments on a polyphonic acoustic scene dataset show that the proposed ERGL achieves competitive performance on ASC by using only a limited number of embeddings of audio events without any data augmentations.
Acoustic Scene Classification
Graph Representation Learning
+1
Rule-embedded network for audio-visual voice activity detection in live musical video streams
Detecting anchor's voice in live musical streams is an important preprocessing for music and speech signal processing.
Sound Multimedia Audio and Speech Processing
Audio Tagging With Connectionist Temporal Classification Model Using Sequential Labelled Data
To use the order information of sound events, we propose sequential labelled data (SLD), where both the presence or absence and the order information of sound events are known.
