70 citations · 164 across the 9 of their papers we have counts for
6 papers · 1 filter
End-to-end Non-Negative Autoencoders for Sound Source Separation
Shrikant Venkataramani, Efthymios Tzinis, Paris Smaragdis
Discriminative models for source separation have recently been shown to produce impressive results. However, when operating on sources outside of the training set, these models can…
A Style Transfer Approach to Source Separation
Shrikant Venkataramani, Efthymios Tzinis, Paris Smaragdis
Training neural networks for source separation involves presenting a mixture recording at the input of the network and updating network parameters in order to produce an output tha…
Class-conditional embeddings for music source separation
Prem Seetharaman, Gordon Wichern, Shrikant Venkataramani +1
Isolating individual instruments in a musical mixture has a myriad of potential applications, and seems imminently achievable given the levels of performance reached by recent deep…
Neural Network Alternatives to Convolutive Audio Models for Source Separation
Shrikant Venkataramani, Y. Cem Subakan, Paris Smaragdis
Convolutive Non-Negative Matrix Factorization model factorizes a given audio spectrogram using frequency templates with a temporal dimension. In this paper, we present a convolutio…
End-to-end Source Separation with Adaptive Front-Ends
Shrikant Venkataramani, Jonah Casebeer, Paris Smaragdis
Source separation and other audio applications have traditionally relied on the use of short-time Fourier transforms as a front-end frequency domain representation step. The unavai…
A Neural Network Alternative to Non-Negative Audio Models
Paris Smaragdis, Shrikant Venkataramani
We present a neural network that can act as an equivalent to a Non-Negative Matrix Factorization (NMF), and further show how it can be used to perform supervised source separation.…