45 results about "Gammatone filter" patented technology

The invention relates to the technical field of speech processing, and particularly provides a disguise speech recognition method based on a Transform model and mixed features, which is carried out from two aspects of feature extraction and model establishment. A resonance peak parameter is calculated by adopting a cepstrum method, a cepstrum coefficient (GFCC) is obtained through a Gammatone filter bank, then the resonance peak, the GFCC and a difference coefficient of the GFCC are combined into a mixed characteristic parameter, and complementary correlation between mixed characteristics is mined. From the perspective of model establishment, the mixed features are used as the input of the model, and the Transform network model is used as the acoustic model of the voiceprint recognition system, so that the feature distribution is better fitted, the classification effect is remarkably improved, and the performance of the camouflage voice voiceprint recognition system is effectively improved. The problem of performance degradation caused by feature redundancy and modal noise in a traditional method is solved.

The invention discloses a transformer fault detection method based on double-flow auditory feature fusion and a random forest, and the method comprises the steps: obtaining a real-time voiceprint signal during the operation of a transformer, carrying out the pre-emphasis, framing and windowing of the real-time voiceprint signal, and obtaining a voiceprint frame; extracting Mel frequency cepstrum coefficient characteristics of the voiceprint frame based on a Mel filter bank; extracting Gammatone frequency cepstrum coefficient characteristics of the voiceprint frame based on a Gammatone filter bank; calculating time domain statistical index characteristics of the voiceprint frames; constructing a joint feature, inputting the joint feature into a pre-trained random forest model, and calculating a Gini importance score so as to obtain an anti-noise robust feature subset; and inputting the anti-noise robust feature subset into a random forest classifier, and outputting a fault type identification result of the transformer by using an integrated voting mechanism of a multi-decision tree. The method can solve the problems that in the prior art, feature extraction is single, noise immunity is poor, attention to transient impact is lacked, and a feature dimension reduction and classification method has defects.