Search Results for author: Ilya Kavalerov

Found 7 papers, 4 papers with code

Harmonicity Plays a Critical Role in DNN Based Versus in Biologically-Inspired Monaural Speech Segregation Systems

no code implementations8 Mar 2022 Rahil Parikh, Ilya Kavalerov, Carol Espy-Wilson, Shihab Shamma

We evaluate their performance with mixtures of natural speech versus slightly manipulated inharmonic speech, where harmonics are slightly frequency jittered.

Adversarial Attack Speech Separation

Exploring the high dimensional geometry of HSI features

1 code implementation1 Mar 2021 Wojciech Czaja, Ilya Kavalerov, Weilin Li

We explore feature space geometries induced by the 3-D Fourier scattering transform and deep neural network with extended attribute profiles on four standard hyperspectral images.

Attribute Vocal Bursts Intensity Prediction

Cortical Features for Defense Against Adversarial Audio Attacks

1 code implementation30 Jan 2021 Ilya Kavalerov, Ruijie Zheng, Wojciech Czaja, Rama Chellappa

We propose using a computational model of the auditory cortex as a defense against adversarial attacks on audio.

A study of quality and diversity in K+1 GANs

no code implementations NeurIPS Workshop ICBINB 2020 Ilya Kavalerov, Wojciech Czaja, Rama Chellappa

We study the K+1 GAN paradigm which generalizes the canonical true/fake GAN by training a generator with a K+1-ary classifier instead of a binary discriminator.


cGANs with Multi-Hinge Loss

3 code implementations9 Dec 2019 Ilya Kavalerov, Wojciech Czaja, Rama Chellappa

We propose a new algorithm to incorporate class conditional information into the critic of GANs via a multi-class generalization of the commonly used Hinge loss that is compatible with both supervised and semi-supervised settings.

Conditional Image Generation

Three-Dimensional Fourier Scattering Transform and Classification of Hyperspectral Images

2 code implementations17 Jun 2019 Ilya Kavalerov, Weilin Li, Wojciech Czaja, Rama Chellappa

Recent developments in machine learning and signal processing have resulted in many new techniques that are able to effectively capture the intrinsic yet complex properties of hyperspectral imagery.

Anomaly Detection Classification +2

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