70 citations · 164 across the 9 of their papers we have counts for
15 papers
Semi-supervised Time Domain Target Speaker Extraction with Attention
Zhepei Wang, Ritwik Giri, Shrikant Venkataramani +5
In this work, we propose Exformer, a time-domain architecture for target speaker extraction. It consists of a pre-trained speaker embedder network and a separator network based on…
To Dereverb Or Not to Dereverb? Perceptual Studies On Real-Time Dereverberation Targets
Jean-Marc Valin, Ritwik Giri, Shrikant Venkataramani +2
In real life, room effect, also known as room reverberation, and the present background noise degrade the quality of speech. Recently, deep learning-based speech enhancement approa…
Personalized PercepNet: Real-time, Low-complexity Target Voice Separation and Enhancement
Ritwik Giri, Shrikant Venkataramani, Jean-Marc Valin +2
The presence of multiple talkers in the surrounding environment poses a difficult challenge for real-time speech communication systems considering the constraints on network size a…
Self-supervised Learning for Speech Enhancement
Yu-Che Wang, Shrikant Venkataramani, Paris Smaragdis
Supervised learning for single-channel speech enhancement requires carefully labeled training examples where the noisy mixture is input into the network and the network is trained…
Efficient Trainable Front-Ends for Neural Speech Enhancement
Jonah Casebeer, Umut Isik, Shrikant Venkataramani +1
Many neural speech enhancement and source separation systems operate in the time-frequency domain. Such models often benefit from making their Short-Time Fourier Transform (STFT) f…
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…
Two-Step Sound Source Separation: Training on Learned Latent Targets
Efthymios Tzinis, Shrikant Venkataramani, Zhepei Wang +2
In this paper, we propose a two-step training procedure for source separation via a deep neural network. In the first step we learn a transform (and it's inverse) to a latent space…
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…
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…
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…
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…
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…
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.…