Voiceprint Recognition Research Articles

Efficient Neuromorphic Reservoir Computing Using Optoelectronic Memristors for Multivariate Time Series Classification

Reservoir computing (RC) has attracted much attention as a brain-like neuromorphic computing algorithm for time series processing. In addition, the hardware implementation of the RC system can significantly reduce the computing time and effectively apply it to edge computing, showing a wide range of applications. However, many hardware implementations of RC use different hardware to implement standard RC without further expanding the RC architecture, which makes it challenging to deal with relatively complex time series tasks. Therefore, we propose a bidirectional hierarchical light reservoir computing method using optoelectronic memristors as the basis for the hardware implementation. The approach improves the performance of hardware-implemented RC by allowing the memristor to capture multilevel temporal information and generate a variety of reservoir states. Ag[Formula: see text]GQDs[Formula: see text]TiOx[Formula: see text]FTO memristors with negative photoconductivity effects can map temporal inputs nonlinearly to reservoir states and are used to build physical reservoirs to accomplish higher-speed operations. The method’s effectiveness is demonstrated in multivariate time series classification tasks: a predicted accuracy of 98.44[Formula: see text] is achieved in voiceprint recognition and 99.70[Formula: see text] in the mobile state recognition task. Our study offers a strategy for dealing with multivariate time series classification issues and paves the way to developing efficient neuromorphic computing.

CNN based Interpretability Analysis of Voiceprint Recognition

CNN based Interpretability Analysis of Voiceprint Recognition

A focus module-based lightweight end-to-end CNN framework for voiceprint recognition

A focus module-based lightweight end-to-end CNN framework for voiceprint recognition

Unconstrained vocal pattern recognition algorithm based on attention mechanism

Deep learning-based voiceprint recognition methods rely heavily on adequate datasets, especially those closer to the natural environment and more complex under unconstrained conditions. Yet, the data types of open-source speech datasets are too homogeneous nowadays, and there are some differences with the address collected in natural application environments. For few Chinese datasets used, this paper proposes and produces an unconstrained Chinese speech dataset with richer data types closer to those collected in a natural environment. To address the inadequate extraction of acoustic features in the unconstrained speech dataset, a new two-dimensional convolutional residual network structure based on the attention mechanism is designed and applied to acoustic feature extraction. The residual block structure in the residual network is improved by the SE module and the CBAM module to obtain the SE-Cov2d and CSA-Cov2d models respectively. Finally, it is experimentally demonstrated that the attention mechanism can help the network focus on more critical feature information and fuse more differentiated features in feature extraction.

Voiceprint recognition and cloud computing data network security based on scheduling joint optimisation algorithm

Voiceprint recognition and cloud computing data network security based on scheduling joint optimisation algorithm

Voiceprint recognition and cloud computing data network security based on scheduling joint optimisation algorithm

Voiceprint recognition and cloud computing data network security based on scheduling joint optimisation algorithm

Speaker Verification Based on Single Channel Speech Separation

In multi-speaker scenarios, speech processing tasks like speaker identification and speech recognition are susceptible to noise and overlapped voices. As the overlapped voices are a complicated mixture of signals, a target extraction method from this mixture is a good front end solution for further processing like understanding and classifying. The quality of speech separation can be assessed by the noise ratio or subjective scoring and can also be assessed by accuracy of the downstream processing tasks like speaker identification. In order to make the separation model and speaker identification model more adapted to complex multi-speaker speech overlapping scenarios, this research investigates the speech separation model and incorporate with a voiceprint recognition task. This paper proposes a feature-scale single channel speech separation network connected to a back end speaker verification network with MFCCT feature, so the accuracy of speaker identification indicates the quality of speech separation task. The datasets are prepared by synthesizing Voxceleb1 data, and used for training and testing. The results show that using an objective downstream evaluation can effectively improve the overall performance, as the optimized speech separation model significantly reduced the error rate of speaker verification.

A motor bearing fault voiceprint recognition method based on Mel-CNN model

The occurrence of bearing faults is often accompanied by noise signals, and noise sensors have the characteristics of non-contact and flexible arrangement; hence, this paper proposes a bearing fault diagnosis method based on voiceprint features and deep learning. First, the high-frequency component of the motor noise is removed with the help of variational mode decomposition (VMD) to extract the Mel spectrum voiceprint features. Secondly, the Mel voiceprint features are re-extracted with the help of convolutional neural networks (CNN) to fully obtain the high-dimensional abstract features characterizing the bearing faults. Finally, the Mel-CNN model is exploited to achieve bearing fault diagnosis. Applying the Mel-CNN model proposed in this paper to motor noise data with bearing faults, the results show that the Mel spectral features can accurately characterize bearing faults and that the Mel-CNN model outperforms ACDIN, WDCNN, TICNN, the improved LeNet-5 model, and four CNN-derived models.

Research on transformer fault voiceprint recognition based on Mel time-frequency spectrum-convolutional neural network

A series of faults will occur during the operation of the transformer, which will aggravate its vibration and noise signals to a certain extent. In order to realize the detection and recognition of transformer related faults during uninterrupted operation, a voiceprint recognition model based on Mel time spectrum convolution neural network is proposed in this paper. According to the sound signal collected by the transformer in actual operation, Mel time domain spectrum as a data set of deep learning model can be obtained through feature extraction preprocessing and Mel filter dimensionality reduction. On this basis, the Mel time domain spectrum convolution neural network deep learning model is constructed, and Adam is used as the optimizer to optimize the data set through the super parameter optimization combination with multi classification cross entropy as the loss function, so as to realize the voiceprint recognition under four different operating faults of the transformer. The simulation results show that the identification model proposed in this paper is feasible and effective in fault identification.

Research Progress on Wear Degree of Mine Filling Pipeline Based on Voiceprint Recognition

With the increasing demand for mineral resources, the number of mines with favorable mining conditions is gradually decreasing. Human beings are faced with the challenge of having to mine resources in depth, and important engineering problems in deep mining are constantly emerging. On the one hand, it requires a large amount of mineral resources, and on the other hand, sustainable development and green mining are required to protect the environment. Debris flows, etc., seriously threaten the safety of human existence. Therefore, it is critical to know the wear level of the filling line. There are various technologies for non-destructive testing. In order to explore new non-destructive testing technologies that are easy to apply in underground mines, the application of traditional testing technology and new voiceprint recognition technology in mines and the development of future applications are discussed.

Voice Print Recognition Check-in System Based on Resnet

With the development of modern technology and the rise of artificial intelligence, the application scenarios of identity authentication technology are becoming more and more complex, especially the current situation of the spread of the novel coronavirus, the traditional identity authentication technology can no longer meet people's actual needs, and the society urgently needs a secure and convenient identity authentication technology. Voice print recognition technology is an identity recognition method that uses specific feature extraction methods to extract the features that can identify the speaker's identity from the original speech input, and then uses these features to identify the speaker's identity. Aiming at the above problems, this paper proposes a deep learning-based voicing recognition algorithm, which is based on the theoretical knowledge of deep learning. The research work includes the following aspects: providing convolutional network to extract features; The "speech feature template library" is established by massive training. Research on matching and recognition algorithm; Research on check-in system based on voice print recognition. Based on this algorithm, this paper designs and implements a voiceprint recognition check-in system with clear interactive interface. The system has the functions of adding members, refreshing the voice library, checking in with the voice print, clearing the results and so on. The system interface will display the data of the voice print library and the historical check-in record, as well as the current recognition results, accuracy and check-in time. The average recognition rate of the system is about 95%, which can meet the requirement of practical application.

An Operating Status Analysis System of Reactor Equipment Based on Voiceprint Recognition Technology

In order to find transformer (reactor) faults in time, a transformer working condition detection method and verification system based on voiceprint recognition technology was proposed. In this system, 73 groups of transformer audio were collected by the voice sensor on-site, with a total of about 1800 min. The recognition pattern based on a deep learning convolutional neural network was established. Through experiments, it was found that aiming at the additive superposition problem of transformer sound generated by a stable working condition and unstable instantaneous noise, a new method based on the cosine similarity algorithm was proposed to realize the separation detection of sound pattern superposition. The acoustic signals of the iron core under sinusoidal excitation were mainly frequency components of 100 Hz and 200 Hz. Harmonic excitation would aggravate the noise in this frequency band, and the third harmonic excitation had the greatest influence. Due to DC magnetic bias, the hysteresis loop of the iron core was distorted to a certain pole. In addition to the 100 Hz component, the odd harmonics of 150 Hz, 250 Hz, and 350 Hz and even harmonics of 200 Hz, 300 Hz, and 400 Hz also increased obviously. With the increase of direct current content, the performance of the noise signal became more prominent. A transformer working condition detection and verification analysis system was established.

Research on characteristics of acoustic signal of typical partial discharge models

The pulse current method, acoustic and ultrasonic partial discharge (PD) detection, and voiceprint PD detection are commonly used detection methods for the PD detection of power equipment. To study the characteristics of PD signals of typical discharge models based on the principles of the above three detection methods, an acoustic detection experimental system consisting of a needle-tip model and a surface model was built. Acoustic tests were carried out on needle-tip models with different curvature radii and surface discharge models with different lengths of conductive paste. The experimental results showed that acoustic and ultrasonic PD detection and voiceprint PD detection exhibited different sensitivities to the needle-tip discharge models, and the combination of acoustic and ultrasonic PD and voiceprint PD detection was more beneficial for the comprehensive detection of cable PD signals. Based on voiceprint recognition technology, this study drew FFT (Fast Fourier Transformation) diagrams of different types of PD acoustic signals and analyzed the differences in the ultrasonic signal frequency distribution. The frequency band of the voiceprint PD signal of the needle-tip discharge models was concentrated in the range 17–27 kHz, and the frequency band of the voiceprint PD signal of the conductive paste discharge models was concentrated in the range 20–25 kHz. The measurement of voiceprint PD signals in these frequency bands were strengthened when the PD of a cable was detected on-site, which provides the basis for the use of the cable model for on-site PD detection.

Voiceprint Recognition based on Machine Learning Methods

Voiceprint Recognition based on Machine Learning Methods

Core looseness fault identification model based on Mel spectrogram-CNN

In order to realize transformer voiceprint recognition, a transformer voiceprint recognition model based on Mel spectrum convolution neural network is proposed. Firstly, the transformer core looseness fault is simulated by setting different preloads, and the sound signals under different preloads are collected; Secondly, the sound signal is converted into a spectrogram that can be trained by convolutional neural network, and then the dimension is reduced by Mel filter bank to draw Mel spectrogram, which can generate spectrogram data sets under different preloads in batch; Finally, the data set is introduced into convolutional neural network for training, and the transformer voiceprint fault recognition model is obtained. The results show that the training accuracy of the proposed Mel spectrum convolution neural network transformer identification model is 99.91%, which can well identify the core loosening faults.

Anchor voiceprint recognition in live streaming via RawNet-SA and gated recurrent unit

With the sharp booming of online live streaming platforms, some anchors seek profits and accumulate popularity by mixing inappropriate content into live programs. After being blacklisted, these anchors even forged their identities to change the platform to continue live, causing great harm to the network environment. Therefore, we propose an anchor voiceprint recognition in live streaming via RawNet-SA and gated recurrent unit (GRU) for anchor identification of live platform. First, the speech of the anchor is extracted from the live streaming by using voice activation detection (VAD) and speech separation. Then, the feature sequence of anchor voiceprint is generated from the speech waveform with the self-attention network RawNet-SA. Finally, the feature sequence of anchor voiceprint is aggregated by GRU to transform into a deep voiceprint feature vector for anchor recognition. Experiments are conducted on the VoxCeleb, CN-Celeb, and MUSAN dataset, and the competitive results demonstrate that our method can effectively recognize the anchor voiceprint in video streaming.

Secure E - Voting System using Voiceprint

Electronic voting systems are considered to be one of the modern systems that are widely used recently. The great challenge which is facing researchers in this field is how to verify the voter. This paper discussed and implemented online secure E- voting system depends on speech recognition. The proposed system represents a very important stage in identity of the voter personality. The researcher used voiceprint feature (Pitch Detection), where the system included the recognition of voiceprint through the use of (MFCC) method, which proved a great success for sample of society that has been used in the proposed system. The researcher has faced many problems in dealing with human voice as a means of proof. The work required a set of processors for the sound signal (Preprocessing), the addition that the researcher has done is no intensive circumstances or special requirements for the of recording voice but, the system works through very normal circumstances as it has been benefited from merging and propose recording machinery and processing the voice by using hybrid way that using (framing and windowing). The results of voiceprint recognition with a ratio that is more than 98%. These results had a great role in achieving privacy and data integration for the later stages of the proposed system.

A neural network architecture optimizer based on DARTS and generative adversarial learning

Neural network architecture search automatically configures a set of network architectures according to the targeted rules. Thus, it relieves the human-dependent effort and repetitive resources consumption for designing neural network architectures and makes the task of finding the optimum network architecture with better performance much more accessible. Network architecture search methods based on differentiable architecture search (DARTS), however, introduces parameter redundancy. To address this issue, this work presents a novel method for optimizing network architectures that combines DARTS with generative adversarial learning (GAL). We first find the module structures utilizing the DARTS algorithm. Afterwards, the retrieved modules are stacked to derive the initial neural network architecture. Next, the GAL is used to prune some branches of the initial neural network, thereby obtaining the final neural network architecture. The proposed DARTS-GAL method re-optimizes the network architecture searched by DARTS to simplify the network connection and reduce network parameters without compromising network performance. Experimental results on benchmark datasets, i.e., Mixed National Institute of Standards and Technology (MNIST), FashionMNIST, Canadian Institute for Advanced Research10 (CIFAR10), Canadian Institute for Advanced Research100 (CIAFR100), Cats vs Dogs, and voiceprint recognition datasets, indicate that the test accuracies of the DARTS-GAL are higher than those of the DARTS in the majority of the cases. In particular, the proposed solution exhibits an improvement in accuracy by 7.35% on CIFAR10 compared with DARTS, attaining the state-of-the-art result of 99.60%. Additionally, the number of network parameters derived by the DARTS-GAL is significantly lower than that by the DARTS method, with a pruning rate of 62.3% at the highest case.

Research on End-to-end Voiceprint Recognition Model Based on Convolutional Neural Network

Speech signal is a time-varying signal, which is greatly affected by individual and environment. In order to improve the end-to-end voice print recognition rate, it is necessary to preprocess the original speech signal to some extent. An end-to-end voiceprint recognition algorithm based on convolutional neural network is proposed. In this algorithm, the convolution and down-sampling of convolutional neural network are used to preprocess the speech signals in end-to-end voiceprint recognition. The one-dimensional and two-dimensional convolution operations were established to extract the characteristic parameters of Meier frequency cepstrum coefficient from the preprocessed signals, and the classical universal background model was used to model the recognition model of voice print. In this study, the principle of end-to-end voiceprint recognition was firstly analyzed, and the process of end-to-end voice print recognition, end-to-end voice print recognition features and Res-FD-CNN network structure were studied. Then the convolutional neural network recognition model was constructed, and the data were preprocessed to form the convolutional layer in frequency domain and the algorithm was tested.

Prospect of Voiceprint Recognition Based on Deep Learning

As a biometric technology, voiceprint recognition is as unique as human fingerprint and pupil, so voiceprint recognition has great potential in practical application. Aiming at the feature extraction method and voiceprint recognition method, this paper first introduces the principle of voiceprint recognition, traditional MFCC, LPDA and other feature parameters and their performance; secondly, the traditional voiceprint recognition methods such as GMM and GMM-SVM are introduced, as well as their shortcomings and improvement schemes. Aiming at the shortcomings of voiceprint recognition system based on traditional algorithms in accuracy, robustness and real-time, this paper introduces the role of deep learning neural network in different stages of voiceprint recognition, and introduces the characteristics and network structure of some typical algorithms based on deep learning. Finally, according to the advantages and disadvantages of deep learning in voiceprint recognition, the development prospect and challenges of voiceprint recognition technology are analysed.