479 citations · 1.5k across the 53 of their papers we have counts for
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Unsupervised Deep Clustering for Source Separation: Direct Learning from Mixtures using Spatial Information
Efthymios Tzinis, Shrikant Venkataramani, Paris Smaragdis
We present a monophonic source separation system that is trained by only observing mixtures with no ground truth separation information. We use a deep clustering approach which tra…
Multi-View Networks For Multi-Channel Audio Classification
Jonah Casebeer, Zhepei Wang, Paris Smaragdis
In this paper we introduce the idea of multi-view networks for sound classification with multiple sensors. We show how one can build a multi-channel sound recognition model trained…
End-to-end Networks for Supervised Single-channel Speech Separation
Shrikant Venkataramani, Paris Smaragdis
The performance of single channel source separation algorithms has improved greatly in recent times with the development and deployment of neural networks. However, many such netwo…
Multi-View Networks for Denoising of Arbitrary Numbers of Channels
Jonah Casebeer, Brian Luc, Paris Smaragdis
We propose a set of denoising neural networks capable of operating on an arbitrary number of channels at runtime, irrespective of how many channels they were trained on. We coin th…
Performance Based Cost Functions for End-to-End Speech Separation
Shrikant Venkataramani, Ryley Higa, Paris Smaragdis
Recent neural network strategies for source separation attempt to model audio signals by processing their waveforms directly. Mean squared error (MSE) that measures the Euclidean d…
Learning the Base Distribution in Implicit Generative Models
Cem Subakan, Oluwasanmi Koyejo, Paris Smaragdis
Popular generative model learning methods such as Generative Adversarial Networks (GANs), and Variational Autoencoders (VAE) enforce the latent representation to follow simple dist…