His main research concerns Speech recognition, Algorithm, Non-negative matrix factorization, Spectrogram and Source separation. His study in the field of Hidden Markov model is also linked to topics like Musical acoustics. His Algorithm research includes elements of Amplitude, Independent component analysis, Nonnegative matrix and Blind signal separation.
He interconnects Artificial intelligence and Pattern recognition in the investigation of issues within Non-negative matrix factorization. Nobutaka Ono combines subjects such as Speech enhancement, Mixing, Harmonic and Audio signal with his study of Spectrogram. His Source separation study combines topics in areas such as Audio signal processing and Time–frequency analysis.
The scientist’s investigation covers issues in Speech recognition, Artificial intelligence, Algorithm, Acoustics and Pattern recognition. His study in Speech recognition is interdisciplinary in nature, drawing from both Noise reduction, Noise and Harmonic. His work in Artificial intelligence addresses subjects such as Non-negative matrix factorization, which are connected to disciplines such as Sparse matrix.
His Algorithm research is multidisciplinary, incorporating perspectives in Low-rank approximation, Independent component analysis, Matrix analysis and Blind signal separation. Nobutaka Ono has included themes like Microphone array, Noise-canceling microphone, Microphone and Signal processing in his Acoustics study. His study focuses on the intersection of Spectrogram and fields such as Source separation with connections in the field of Audio signal processing and Time–frequency analysis.
His primary areas of investigation include Algorithm, Artificial intelligence, Blind signal separation, Speech recognition and Speech enhancement. His work on Covariance matrix is typically connected to Convergence as part of general Algorithm study, connecting several disciplines of science. His studies deal with areas such as Computer vision and Pattern recognition as well as Artificial intelligence.
Noise measurement is closely connected to Non-negative matrix factorization in his research, which is encompassed under the umbrella topic of Blind signal separation. His Speech recognition study frequently draws connections between related disciplines such as Contrast. His Speech enhancement research incorporates themes from Microphone array, Microphone, Time–frequency analysis, Beamforming and Synchronization.
His primary areas of study are Algorithm, Blind signal separation, Speech recognition, Artificial intelligence and Non-negative matrix factorization. He is interested in Covariance matrix, which is a branch of Algorithm. Within one scientific family, Nobutaka Ono focuses on topics pertaining to Low-rank approximation under Blind signal separation, and may sometimes address concerns connected to Multivariate normal distribution.
The various areas that Nobutaka Ono examines in his Speech recognition study include Speech enhancement and Synchronization. His Artificial intelligence study combines topics from a wide range of disciplines, such as Computer vision and Pattern recognition. In his research on the topic of Pattern recognition, Source separation, Generative model, Sparse matrix, Matrix decomposition and Regularization is strongly related with Spectrogram.
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Stable and fast update rules for independent vector analysis based on auxiliary function technique
workshop on applications of signal processing to audio and acoustics (2011)
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)
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)
The 2016 Signal Separation Evaluation Campaign
Antoine Liutkus;Fabian-Robert Stöter;Zafar Rafii;Daichi Kitamura.
international conference on latent variable analysis and signal separation (2017)
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)
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)
Multipitch Analysis with Harmonic Nonnegative Matrix Approximation.
Stanislaw Andrzej Raczynski;Nobutaka Ono;Shigeki Sagayama.
international symposium/conference on music information retrieval (2007)
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)
Blind alignment of asynchronously recorded signals for distributed microphone array
Nobutaka Ono;Hitoshi Kohno;Nobutaka Ito;Shigeki Sagayama.
workshop on applications of signal processing to audio and acoustics (2009)
HMM-based approach for automatic chord detection using refined acoustic features
Yushi Ueda;Yuki Uchiyama;Takuya Nishimoto;Nobutaka Ono.
international conference on acoustics, speech, and signal processing (2010)
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