D-Index & Metrics Best Publications
Hirokazu Kameoka

Hirokazu Kameoka

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 35 Citations 4,972 193 World Ranking 6042 National Ranking 94

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Algorithm

Hirokazu Kameoka mainly investigates Speech recognition, Algorithm, Spectrogram, Non-negative matrix factorization and Artificial intelligence. In the subject of general Speech recognition, his work in Speech synthesis is often linked to Generator, thereby combining diverse domains of study. The various areas that Hirokazu Kameoka examines in his Algorithm study include Cluster analysis, Statistical model, Rule-based machine translation, Mixture model and Convolutional neural network.

His Non-negative matrix factorization study is associated with Matrix decomposition. His studies link Pattern recognition with Artificial intelligence. His Pattern recognition research incorporates themes from Iterative method, Source separation and Speech processing.

His most cited work include:

  • Multichannel Extensions of Non-Negative Matrix Factorization With Complex-Valued Data (190 citations)
  • Determined blind source separation unifying independent vector analysis and nonnegative matrix factorization (170 citations)
  • A Multipitch Analyzer Based on Harmonic Temporal Structured Clustering (147 citations)

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

His scientific interests lie mostly in Speech recognition, Artificial intelligence, Pattern recognition, Algorithm and Spectrogram. His Speech recognition research is multidisciplinary, incorporating perspectives in Sequence and Fundamental frequency. Statistical model is closely connected to Natural language processing in his research, which is encompassed under the umbrella topic of Artificial intelligence.

His work on Feature extraction, Mixture model and Semi-supervised learning as part of general Pattern recognition study is frequently linked to Sparse matrix, therefore connecting diverse disciplines of science. Hirokazu Kameoka has researched Algorithm in several fields, including Frequency domain, Fourier transform, Spectral envelope and Linear predictive coding. While the research belongs to areas of Spectrogram, he spends his time largely on the problem of Source separation, intersecting his research to questions surrounding Autoencoder.

He most often published in these fields:

  • Speech recognition (43.06%)
  • Artificial intelligence (37.37%)
  • Pattern recognition (32.03%)

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

  • Speech recognition (43.06%)
  • Spectrogram (25.27%)
  • Algorithm (27.76%)

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

His primary areas of study are Speech recognition, Spectrogram, Algorithm, Naturalness and Source separation. His work on Sound quality as part of general Speech recognition research is frequently linked to Generator, bridging the gap between disciplines. His studies in Spectrogram integrate themes in fields like Speech enhancement, Wiener filter and Cluster analysis.

Hirokazu Kameoka focuses mostly in the field of Algorithm, narrowing it down to matters related to Waveform and, in some cases, Aliasing and Spectral amplitude. His study on Source separation is intertwined with other disciplines of science such as Matrix decomposition and Non-negative matrix factorization. Hirokazu Kameoka combines subjects such as Classifier, Pattern recognition and Generative model with his study of Autoencoder.

Between 2018 and 2021, his most popular works were:

  • Cyclegan-VC2: Improved Cyclegan-based Non-parallel Voice Conversion (64 citations)
  • ATTS2S-VC: Sequence-to-sequence Voice Conversion with Attention and Context Preservation Mechanisms (51 citations)
  • StarGAN-VC2: Rethinking Conditional Methods for StarGAN-Based Voice Conversion (32 citations)

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

  • Artificial intelligence
  • Machine learning
  • Speech recognition

The scientist’s investigation covers issues in Speech recognition, Autoencoder, Speech synthesis, Source separation and Artificial intelligence. His study of Sound quality is a part of Speech recognition. His Source separation study integrates concerns from other disciplines, such as Spectrogram and Pattern recognition.

Hirokazu Kameoka integrates Spectrogram with Matrix decomposition in his research. Hirokazu Kameoka studies Mixture model which is a part of Artificial intelligence. As a part of the same scientific study, Hirokazu Kameoka usually deals with the Separation, concentrating on Blind signal separation and frequently concerns with Algorithm.

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.

Best Publications

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

StarGAN-VC: non-parallel many-to-many Voice Conversion Using Star Generative Adversarial Networks

Hirokazu Kameoka;Takuhiro Kaneko;Kou Tanaka;Nobukatsu Hojo.
spoken language technology workshop (2018)

224 Citations

Determined blind source separation unifying independent vector analysis and nonnegative matrix factorization

Daichi Kitamura;Nobutaka Ono;Hiroshi Sawada;Hirokazu Kameoka.
IEEE Transactions on Audio, Speech, and Language Processing (2016)

223 Citations

A Multipitch Analyzer Based on Harmonic Temporal Structured Clustering

H. Kameoka;T. Nishimoto;S. Sagayama.
IEEE Transactions on Audio, Speech, and Language Processing (2007)

221 Citations

Complex NMF: A new sparse representation for acoustic signals

Hirokazu Kameoka;Nobutaka Ono;Kunio Kashino;Shigeki Sagayama.
international conference on acoustics, speech, and signal processing (2009)

192 Citations

Separation of a monaural audio signal into harmonic/percussive components by complementary diffusion on spectrogram

Nobutaka Ono;Kenichi Miyamoto;Jonathan Le Roux;Hirokazu Kameoka.
european signal processing conference (2008)

179 Citations

Parallel-Data-Free Voice Conversion Using Cycle-Consistent Adversarial Networks

Takuhiro Kaneko;Hirokazu Kameoka.
arXiv: Machine Learning (2017)

173 Citations

Convergence-guaranteed multiplicative algorithms for nonnegative matrix factorization with β-divergence

Masahiro Nakano;Hirokazu Kameoka;Jonathan Le Roux;Yu Kitano.
international workshop on machine learning for signal processing (2010)

151 Citations

A REAL-TIME EQUALIZER OF HARMONIC AND PERCUSSIVE COMPONENTS IN MUSIC SIGNALS

Nobutaka Ono;Kenichi Miyamoto;Hirokazu Kameoka;Shigeki Sagayama.
international symposium/conference on music information retrieval (2008)

130 Citations

CycleGAN-VC: Non-parallel Voice Conversion Using Cycle-Consistent Adversarial Networks

Takuhiro Kaneko;Hirokazu Kameoka.
european signal processing conference (2018)

126 Citations

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