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Michael I. Mandel

Brooklyn College

9 papers hereh-index 306.6k citations117 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author1
  • last author8

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.SD4
  • eess.AS4
  • cs.LG1
affiliations
  • Brooklyn College
  • CUNY Graduate Center
HomepageORCID 0000-0003-2073-5357
same name
  • Michael I. Mandel — 3 papers, h 5

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20112020
most citedAutotagging music with conditional restricted Boltzmann machines

16 citations · 26 across the 6 of their papers we have counts for

collaborators
Showing eess.ASShow all

4 papers · 1 filter

eess.AS2020

WPD++: An Improved Neural Beamformer for Simultaneous Speech Separation and Dereverberation

Zhaoheng Ni, Yong Xu, Meng Yu +4

This paper aims at eliminating the interfering speakers' speech, additive noise, and reverberation from the noisy multi-talker speech mixture that benefits automatic speech recogni…

eess.AS2019★ 5 cited

Onssen: an open-source speech separation and enhancement library

Zhaoheng Ni, Michael I Mandel

Speech separation is an essential task for multi-talker speech recognition. Recently many deep learning approaches are proposed and have been constantly refreshing the state-of-the…

eess.AS2019

Mask-dependent Phase Estimation for Monaural Speaker Separation

Zhaoheng Ni, Michael I Mandel

Speaker separation refers to isolating speech of interest in a multi-talker environment. Most methods apply real-valued Time-Frequency (T-F) masks to the mixture Short-Time Fourier…

eess.AS2019★ 4 cited

Speech denoising by parametric resynthesis

Soumi Maiti, Michael I Mandel

This work proposes the use of clean speech vocoder parameters as the target for a neural network performing speech enhancement. These parameters have been designed for text-to-spee…

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