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Digital Music Audio Engineering

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Digital Music Audio Engineering

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Research Frontiers in Neural Network Audio Source Separation

Development of deep learning architectures for isolating individual instruments and vocal tracks from mixed audio signals using convolutional and recurrent neural networks.

Implicit Harmonic Structure Learning in Polyphonic Separation

Cross-Domain Generalization for Unseen Instrument Isolation

Temporal Coherence and Phase Reconstruction in Neural Separation

Perceptually-Informed Loss Landscapes for Source Disentanglement

Few-Shot Adaptation in Real-Time Audio Source Extraction

Semantic Embeddings and Musical Context in Source Separation

Artifact Suppression Through Adversarial Audio Refinement

Sparse Representation Learning for Efficient Neural Demixing

Binaural Cue Preservation in Spatial Audio Decomposition

Uncertainty Quantification in Ambiguous Musical Source Attribution

All Digital Music & Audio Engineering PhD categories