65 citations · 72 across the 9 of their papers we have counts for
9 papers
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…
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…
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…
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…
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,…
Adversarial Robustness Unhardening via Backdoor Attacks in Federated Learning
Taejin Kim, Jiarui Li, Shubhranshu Singh +2
The delicate equilibrium between user privacy and the ability to unleash the potential of distributed data is an important concern. Federated learning, which enables the training o…
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…
Learning to detect an animal sound from five examples
Inês Nolasco, Shubhr Singh, Veronica Morfi +14
Automatic detection and classification of animal sounds has many applications in biodiversity monitoring and animal behaviour. In the past twenty years, the volume of digitised wil…
Few-shot Bioacoustic Event Detection with Machine Learning Methods
Leah Chowenhill, Gaurav Satyanath, Shubhranshu Singh +1
Few-shot learning is a type of classification through which predictions are made based on a limited number of samples for each class. This type of classification is sometimes refer…