H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 42 Citations 7,505 184 World Ranking 4107 National Ranking 57


What is she best known for?

The fields of study she is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

The scientist’s investigation covers issues in Blind signal separation, Speech recognition, Source separation, Independent component analysis and Frequency domain. She interconnects Reverberation, Cluster analysis, Artificial intelligence, Algorithm and Pattern recognition in the investigation of issues within Blind signal separation. The concepts of her Speech recognition study are interwoven with issues in Speech enhancement, Microphone array and Noise reduction.

Her Source separation research includes elements of Mixture model and Time–frequency analysis. As a member of one scientific family, she mostly works in the field of Independent component analysis, focusing on Signal processing and, on occasion, Reverberation room, Maximum likelihood and Distortion. Her work carried out in the field of Frequency domain brings together such families of science as Adaptive beamformer, Bin and Permutation.

Her most cited work include:

  • A robust and precise method for solving the permutation problem of frequency-domain blind source separation (539 citations)
  • The fundamental limitation of frequency domain blind source separation for convolutive mixtures of speech (304 citations)
  • Underdetermined Convolutive Blind Source Separation via Frequency Bin-Wise Clustering and Permutation Alignment (282 citations)

What are the main themes of her work throughout her whole career to date?

Her primary scientific interests are in Speech recognition, Blind signal separation, Artificial intelligence, Source separation and Pattern recognition. Her Speech recognition research incorporates elements of Speech enhancement, Noise and Reverberation. Her Blind signal separation study integrates concerns from other disciplines, such as Underdetermined system, Algorithm, Independent component analysis, Frequency domain and Signal processing.

Her studies deal with areas such as Audio signal processing and Computer vision as well as Artificial intelligence. Her Source separation research incorporates themes from Mixture model, Convolution, Noise reduction and Expectation–maximization algorithm. Her Pattern recognition course of study focuses on Noise measurement and Background noise.

She most often published in these fields:

  • Speech recognition (56.97%)
  • Blind signal separation (45.49%)
  • Artificial intelligence (31.56%)

What were the highlights of her more recent work (between 2018-2021)?

  • Speech recognition (56.97%)
  • Speech enhancement (18.85%)
  • Artificial neural network (7.38%)

In recent papers she was focusing on the following fields of study:

Shoko Araki mainly focuses on Speech recognition, Speech enhancement, Artificial neural network, Source separation and Artificial intelligence. Shoko Araki has researched Speech recognition in several fields, including Time domain, Estimator and Reverberation. She has included themes like Intelligibility, Filter bank and Nonlinear algorithms in her Speech enhancement study.

Her biological study spans a wide range of topics, including Noise reduction and Pattern recognition. Her work on Mixture model is typically connected to Complex normal distribution as part of general Pattern recognition study, connecting several disciplines of science. Her Artificial intelligence study incorporates themes from Signal and Computer vision.

Between 2018 and 2021, her most popular works were:

  • All-neural Online Source Separation, Counting, and Diarization for Meeting Analysis (31 citations)
  • Compact Network for Speakerbeam Target Speaker Extraction (26 citations)
  • Improving Speaker Discrimination of Target Speech Extraction With Time-Domain Speakerbeam (15 citations)

In her most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Machine learning

Her main research concerns Speech recognition, Source separation, Artificial neural network, Speech enhancement and Speaker diarisation. Her work in Speech recognition addresses subjects such as Time domain, which are connected to disciplines such as Frequency domain, Waveform and Word error rate. She works mostly in the field of Frequency domain, limiting it down to concerns involving Covariance function and, occasionally, Noise.

Her research integrates issues of Reverberation and Pattern recognition in her study of Source separation. The various areas that Shoko Araki examines in her Pattern recognition study include Noise reduction, Sound and Selection. Artificial neural network is a primary field of her research addressed under Artificial intelligence.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Top Publications

A robust and precise method for solving the permutation problem of frequency-domain blind source separation

H. Sawada;R. Mukai;S. Araki;S. Makino.
IEEE Transactions on Speech and Audio Processing (2004)

726 Citations

The fundamental limitation of frequency domain blind source separation for convolutive mixtures of speech

S. Araki;R. Mukai;S. Makino;T. Nishikawa.
IEEE Transactions on Speech and Audio Processing (2003)

484 Citations

Underdetermined Convolutive Blind Source Separation via Frequency Bin-Wise Clustering and Permutation Alignment

Hiroshi Sawada;Shoko Araki;Shoji Makino.
IEEE Transactions on Audio, Speech, and Language Processing (2011)

371 Citations

Underdetermined blind sparse source separation for arbitrarily arranged multiple sensors

Shoko Araki;Hiroshi Sawada;Ryo Mukai;Shoji Makino.
Signal Processing (2007)

308 Citations

Multichannel Extensions of Non-Negative Matrix Factorization With Complex-Valued Data

H. Sawada;H. Kameoka;S. Araki;N. Ueda.
IEEE Transactions on Audio, Speech, and Language Processing (2013)

257 Citations

The NTT CHiME-3 system: Advances in speech enhancement and recognition for mobile multi-microphone devices

Takuya Yoshioka;Nobutaka Ito;Marc Delcroix;Atsunori Ogawa.
ieee automatic speech recognition and understanding workshop (2015)

213 Citations

Polar coordinate based nonlinear function for frequency-domain blind source separation

Hiroshi Sawada;Ryo Mukai;Shoko Araki;Shoji Makino.
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences (2003)

203 Citations

The signal separation evaluation campaign (2007-2010): Achievements and remaining challenges

Emmanuel Vincent;Shoko Araki;Fabian Theis;Guido Nolte.
Signal Processing (2012)

199 Citations

The 2008 Signal Separation Evaluation Campaign: A Community-Based Approach to Large-Scale Evaluation

Emmanuel Vincent;Shoko Araki;Pau Bofill.
international conference on independent component analysis and signal separation (2009)

170 Citations

The 2010 signal separation evaluation campaign (SiSEC2010): audio source separation

Shoko Araki;Alexey Ozerov;Vikrham Gowreesunker;Hiroshi Sawada.
international conference on latent variable analysis and signal separation (2010)

164 Citations

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking h-index is inferred from publications deemed to belong to the considered discipline.

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