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Found 5 papers, 2 papers with code
Separate but Together: Unsupervised Federated Learning for Speech Enhancement from Non-IID Data
We propose FEDENHANCE, an unsupervised federated learning (FL) approach for speech enhancement and separation with non-IID distributed data across multiple clients.
Enhancing into the codec: Noise Robust Speech Coding with Vector-Quantized Autoencoders
Audio codecs based on discretized neural autoencoders have recently been developed and shown to provide significantly higher compression levels for comparable quality speech output.
Efficient Trainable Front-Ends for Neural Speech Enhancement
Many neural speech enhancement and source separation systems operate in the time-frequency domain.
Deep Tensor Factorization for Spatially-Aware Scene Decomposition
We propose a completely unsupervised method to understand audio scenes observed with random microphone arrangements by decomposing the scene into its constituent sources and their relative presence in each microphone.
End-to-end Source Separation with Adaptive Front-Ends
We present an auto-encoder neural network that can act as an equivalent to short-time front-end transforms.
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