Bionic optimization of MFCC features based on speaker fast recognition

Lin, Zhaodong; Di, Changán; Chen, Xiong

doi:10.1016/j.apacoust.2020.107682

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…As sound is a type of volatile signal that contains a lot of noise, direct sound recognition is inefficient. 44,45 Additionally, the 2D-CNN is best suited to extracting features from images; its ability to recognize sound pressure signals is weak. Therefore, an acoustic wave spectrograms classification network (AWCNet), which consists of MFCCs, STFTs, and a CNN, is proposed in this study, as shown in Figure 9.…”

Section: Data Preparationmentioning

confidence: 99%

A CNN-integrated percussion method for detection of FRP–concrete interfacial damage with FEM reconstruction

Kong

et al. 2022

Structural Health Monitoring

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Reinforced concrete (RC) structures are commonly strengthened using externally bonded fiber-reinforced polymer (FRP) sheets. The bond between the FRP and concrete is a crucial factor affecting the strengthening effect, and debonding along the FRP–concrete interface is usually accompanied by the fracture of the underlying concrete. Therefore, it is necessary to identify the interface damage of FRP-to-concrete joints and conduct mechanical analysis. However, debonding is invisible damage that occurs inside the underlying FRP layer, which makes damage detection more difficult. To this end, this study fuses a percussion method with a deep learning framework to address the detection of such invisible lesions. Meanwhile, the visualization study provides guidance for later maintenance work. To further illustrate the hazard of the identified lesions, three-dimensional reconstruction for finite element modeling (FEM) with detected damage information based on percussion is proposed to elucidate the mechanical degradation caused by the fracture of underlying concrete. Lastly, the results of this study demonstrate that the detection, visualization, and FEM reconstruction of FRP–concrete interface damage using percussion signals has considerable application potential and is worthy of further study.

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Section: Data Preparationmentioning

confidence: 99%

A CNN-integrated percussion method for detection of FRP–concrete interfacial damage with FEM reconstruction

Kong

et al. 2022

Structural Health Monitoring

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“…In the current deep learning framework, audio environment hidden information feature extraction methods are mainly divided into two categories: traditional feature representation [6][7][8] and automatic learning audio feature representation based on deep network [9,10]. MFCC [11], spectrograms, acoustic event histograms [12], and gradient histograms based on time-frequency learning [13] are the most commonly used traditional methods for acoustic feature representation.…”

Section: Introductionmentioning

confidence: 99%

Audio Information Camouflage Detection for Social Networks

Lou

Zuo

et al. 2021

Front. Phys.

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Sending camouflaged audio information for fraud in social networks has become a new means of social networks attack. The hidden acoustic events in the audio scene play an important role in the detection of camouflaged audio information. Therefore, the application of machine learning methods to represent hidden information in audio streams has become a hot issue in the field of network security detection. This study proposes a heuristic mask for empirical mode decomposition (HM-EMD) method for extracting hidden features from audio streams. The method consists of two parts: First, it constructs heuristic mask signals related to the signal’s structure to solve the modal mixing problem in intrinsic mode function (IMF) and obtains a pure IMF related to the signal’s structure. Second, a series of hidden features in environment-oriented audio streams is constructed on the basis of the IMF. A machine learning method and hidden information features are subsequently used for audio stream scene classification. Experimental results show that the hidden information features of audio streams based on HM-EMD are better than the classical mel cepstrum coefficients (MFCC) under different classifiers. Moreover, the classification accuracy achieved with HM-EMD increases by 17.4 percentage points under the three-layer perceptron and by 1.3% under the depth model of TridentResNet. The hidden information features extracted by HM-EMD from audio streams revealed that the proposed method could effectively detect camouflaged audio information in social networks, which provides a new research idea for improving the security of social networks.

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A short utterance speaker recognition method with improved cepstrum–CNN

Chang

2022

SN Appl. Sci.

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In this study, an improved cepstrum-convolutional neural network is proposed, which can solve the problem of low recognition accuracy of 1-s short utterance in speaker recognition technology. The audio feature Mel frequency cepstrum coefficient is extracted by using the improved cepstrum algorithm and the data of the two-dimensional acoustic feature vector matrix is preprocessed to convert the two-dimensional feature matrix into a three-dimensional tensor as the input data of the two-dimensional convolutional neural network model. Experiments are carried out on an Arabic digital English pronunciation dataset with an audio duration of less than one second in a specific experimental environment. Moreover, the performance of this model is evaluated by accuracy and F1-score. The simulation results show that the accuracy of our proposed model for speech recognition is as high as 100% and 99.60% on the training and test sets, respectively, as well as the F1- score, is 0.9985. It can be seen that the recognition method of this model solves the problem of accuracy degradation of short utterance speaker recognition due to the short duration of the corpus and improves the accuracy of short speech voice recognition. The model is simple but effective, generalization, superior, and has higher practical application value.Article Highlights. It is interesting to study how to improve the accuracy of 1-s short utterance speaker recognition. The improved cepstrum algorithm can solve the problem of not extracting enough discernible acoustic features. This paper proposed model obtained 100% accuracy on a spoken Arabic digit dataset with an audio duration about 0.3 s.

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Bionic optimization of MFCC features based on speaker fast recognition

Cited by 8 publications

References 14 publications

A CNN-integrated percussion method for detection of FRP–concrete interfacial damage with FEM reconstruction

A CNN-integrated percussion method for detection of FRP–concrete interfacial damage with FEM reconstruction

Audio Information Camouflage Detection for Social Networks

A short utterance speaker recognition method with improved cepstrum–CNN

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