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Geng et al., 2019 - Google Patents

Mechanical fault diagnosis of power transformer by GFCC time-frequency map of acoustic signal and convolutional neural network

Geng et al., 2019

Document ID
13925970498387283574
Author
Geng Q
Wang F
Zhou D
Publication year
Publication venue
2019 IEEE Sustainable Power and Energy Conference (iSPEC)

External Links

Snippet

To carefully describe the mechanical condition information from transformer acoustic signals and then identify its typical mechanical faults, the combination of gammatone filter cepstral coefficient (GFCC) time-frequency graph of acoustic signals and Convolution Neural …
Continue reading at ieeexplore.ieee.org (other versions)

Classifications

    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Processing of the speech or voice signal to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L17/00—Speaker identification or verification
    • G10L17/04—Training, enrolment or model building

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