Patents by Inventor Yuma KOIZUMI

Yuma KOIZUMI has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).

  • Publication number: 20210326728
    Abstract: A possible region of encoding results of anomalous samples is limited. An encoder storage unit 14 stores an encoder for projecting an input feature value into a latent space in which the latent space is a closed manifold, a normal distribution obtained by learning normal data and an anomalous distribution obtained by learning anomalous data are held on the manifold, and a decoder for reconstructing the output of the encoder. An encoding unit 15 obtains a reconstruction result output by the decoder when a feature value of target data is input to the encoder. An anomaly score calculation unit 16 calculates an anomaly score of the target data based on distances between the reconstruction result and the normal distribution and distances between the reconstruction result and the anomalous distribution.
    Type: Application
    Filed: July 1, 2019
    Publication date: October 21, 2021
    Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION
    Inventors: Yuta KAWACHI, Yuma KOIZUMI, Noboru HARADA, Shin MURATA
  • Publication number: 20210327456
    Abstract: An anomaly detection technique which realizes high accuracy while reducing cost required for normal model learning is provided. An anomaly detection apparatus includes an anomaly degree estimating unit configured to estimate an anomaly degree indicating a degree of anomaly of anomaly detection target equipment from sound emitted from the anomaly detection target equipment (hereinafter, referred to as anomaly detection target sound) based on association between a first probability distribution indicating distribution of normal sound emitted from one or more pieces of equipment different from the anomaly detection target equipment and normal sound emitted from the anomaly detection target equipment (hereinafter, referred to as normal sound for adaptive learning).
    Type: Application
    Filed: July 4, 2019
    Publication date: October 21, 2021
    Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION
    Inventors: Masataka YAMAGUCHI, Yuma KOIZUMI, Noboru HARADA
  • Publication number: 20210219048
    Abstract: An acoustic signal is separated based on a difference in the distance from a sound source to a microphone. By using a filter obtained by associating a value corresponding to an estimated value of a short-distance acoustic signal which is obtained by using “a predetermined function” from a second acoustic signal derived from signals collected by “a plurality of microphones” and is emitted from a position close to “the plurality of microphones” with a value corresponding to an estimated value of a long-distance acoustic signal which is emitted from a position far from “the plurality of microphones”, a desired acoustic signal representing at least one of a sound emitted from a position close to “a specific microphone” and a sound emitted from a position far from “the specific microphone” is acquired from a first acoustic signal derived from a signal collected by “the specific microphone”.
    Type: Application
    Filed: May 20, 2019
    Publication date: July 15, 2021
    Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION
    Inventors: Yuma KOIZUMI, Sakurako YAZAWA, Kazunori KOBAYASHI
  • Publication number: 20210081805
    Abstract: The present disclosure relates to a method of machine learning regardless of the number of dimensions of the samples. The method provides model learning of a variational auto-encoder that uses AUC optimization criteria. The method includes learning parameters ?{circumflex over (?)} and ?{circumflex over (?)} of the a variational auto-encoder. The variational auto-encoder includes an encoder for constructing a latent variable from an observed variable and a decoder for reconstructing the observed variable. The method uses learning data set defined using based on normal data generated from sounds observed during normal operation and abnormal data generated from sounds observed during abnormal operation. The AUC value is based in part on a reconstruction probability. Incorporating aspects of the reconstruction error into the AUC value prevents the variational auto-encoder from divergence of the abnormality degree regarding the abnormal data.
    Type: Application
    Filed: February 14, 2019
    Publication date: March 18, 2021
    Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION
    Inventors: Yuta KAWACHI, Yuma KOIZUMI, Noboru HARADA
  • Publication number: 20200401943
    Abstract: There is provided a model learning technique for learning a model which performs classification into three values by model learning using an AUC optimization criterion. A model learning unit is included which learns a parameter ?{circumflex over (?)} of a model by using a learning data set based on a criterion which uses a predetermined AUC value, the learning data set being defined using normal data generated from sound observed in a normal state and abnormal data generated from sound observed in an abnormal state, and the AUC value is defined from a difference between an abnormality degree of the normal data and an abnormality degree of the abnormal data using a two-stage step function T(x).
    Type: Application
    Filed: February 13, 2019
    Publication date: December 24, 2020
    Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION
    Inventors: Yuta KAWACHI, Yuma KOIZUMI, Noboru HARADA
  • Publication number: 20200388298
    Abstract: A noise estimation parameter learning device is provided according to which even in a large space causing a problem of the reverberation and the time frame difference, multiple microphones disposed at distant positions cooperate with each other, and a spectral subtraction method is executed, thereby allowing the target sound to be enhanced.
    Type: Application
    Filed: September 12, 2017
    Publication date: December 10, 2020
    Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION
    Inventors: Yuma KOIZUMI, Shoichiro SAITO, Kazunori KOBAYASHI, Hitoshi OHMURO
  • Publication number: 20200209842
    Abstract: Accuracy of unsupervised anomalous sound detection is improved using a small number of pieces of anomalous sound data. A threshold deciding part (13) calculates an anomaly score for each of a plurality of pieces of anomalous sound data, using a normal model learned with normal sound data and an anomaly model expressing the pieces of anomalous sound data, and decides a minimum value among the anomaly scores as a threshold. A weight updating part (14) updates, using a plurality of pieces of normal sound data, the pieces of anomalous sound data and the threshold, weights of the anomaly model so that all the pieces of anomalous sound data are judged as anomalous, and probability of the pieces of normal sound data being judged as anomalous is minimized.
    Type: Application
    Filed: August 24, 2018
    Publication date: July 2, 2020
    Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION
    Inventors: Yuma KOIZUMI, Yuta KAWACHI, Noboru HARADA, Shoichiro SAITO, Akira NAKAGAWA, Shin MURATA
  • Publication number: 20190376840
    Abstract: To provide an anomalous sound detection training technique by which a feature amount extraction function for detecting anomalous sound can be generated irrespective of whether training data for anomalous signals is available or not.
    Type: Application
    Filed: September 14, 2017
    Publication date: December 12, 2019
    Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION
    Inventors: Yuma KOIZUMI, Shoichiro SAITO, Hisashi UEMATSU
  • Publication number: 20190120719
    Abstract: An anomalous sound detection training apparatus includes: a first acoustic feature extraction unit that extracts an acoustic feature of normal sound based on training data for normal sound by using an acoustic feature extractor; a normal sound model updating unit that updates a normal sound model by using the acoustic feature extracted; a second acoustic feature extraction unit that extracts an acoustic feature of anomalous sound based on simulated anomalous sound and extracts the acoustic feature of normal sound based on the training data for normal sound by using the acoustic feature extractor; and an acoustic feature extractor updating unit that updates the acoustic feature extractor by using the acoustic feature of anomalous sound and the acoustic feature of normal sound that have been extracted, in which processing by the units is repeatedly performed.
    Type: Application
    Filed: March 31, 2017
    Publication date: April 25, 2019
    Applicant: NIPPON TELEGRAPH AND TELEPHONE CORPORATION
    Inventors: Yuma KOIZUMI, Shoichiro SAITO, Hisashi UEMATSU, Kenta NIWA, Hiroaki ITO