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20222025
most citedFew-shot bioacoustic event detection at the DCASE 2023 challenge

6 citations · 7 across the 6 of their papers we have counts for

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cs.SD2025

Robust Neural Audio Fingerprinting using Music Foundation Models

Shubhr Singh, Kiran Bhat, Xavier Riley +3

The proliferation of distorted, compressed, and manipulated music on modern media platforms like TikTok motivates the development of more robust audio fingerprinting techniques to…

cs.SD2025

LHGNN: Local-Higher Order Graph Neural Networks For Audio Classification and Tagging

Shubhr Singh, Emmanouil Benetos, Huy Phan +1

Transformers have set new benchmarks in audio processing tasks, leveraging self-attention mechanisms to capture complex patterns and dependencies within audio data. However, their…

cs.SD20241 cited

ST-ITO: Controlling Audio Effects for Style Transfer with Inference-Time Optimization

Christian J. Steinmetz, Shubhr Singh, Marco Comunità +4

Audio production style transfer is the task of processing an input to impart stylistic elements from a reference recording. Existing approaches often train a neural network to esti…

cs.SD2024

GraFPrint: A GNN-Based Approach for Audio Identification

Aditya Bhattacharjee, Shubhr Singh, Emmanouil Benetos

This paper introduces GraFPrint, an audio identification framework that leverages the structural learning capabilities of Graph Neural Networks (GNNs) to create robust audio finger…

cs.SD2023

ATGNN: Audio Tagging Graph Neural Network

Shubhr Singh, Christian J. Steinmetz, Emmanouil Benetos +2

Deep learning models such as CNNs and Transformers have achieved impressive performance for end-to-end audio tagging. Recent works have shown that despite stacking multiple layers,…

cs.SD20236 cited

Few-shot bioacoustic event detection at the DCASE 2023 challenge

Ines Nolasco, Burooj Ghani, Shubhr Singh +12

Few-shot bioacoustic event detection consists in detecting sound events of specified types, in varying soundscapes, while having access to only a few examples of the class of inter…