Short-time Zero-crossing Rate Research Articles

Sex identification of ducklings based on acoustic signals

Sex identification of ducklings is a critical step in the poultry farming industry, and accurate sex identification is beneficial for precise breeding and cost savings. In this study, a method for identifying the sex of ducklings based on acoustic signals was proposed. In the first step, duckling vocalizations were collected and an improved spectral subtraction method and high-pass filtering were applied to reduce the influence of noise. Then, duckling vocalizations were automatically detected by using a double-threshold endpoint detection method with three parameters: short-time energy (STE), short-time zero-crossing rate (ZCR), and duration (D). Following the extraction of Mel-Spectrogram features from duckling vocalizations, an improved Res2Net deep learning algorithm was used for sex classification. This algorithm was introduced with the Squeeze-and-Excitation (SE) attention mechanism and Ghost module to improve the bottleneck of Res2Net, thereby improving the model accuracy and reducing the number of parameters. The ablative experimental results showed that the introduction of the SE attention mechanism improved the model accuracy by 2.01%, while the Ghost module reduced the number of model parameters by 7.26M and the FLOPs by 0.85G. Moreover, this algorithm was compared with five state-of-the-art (SOTA) algorithms, and the results showed that the proposed algorithm has the best cost-effectiveness, with accuracy, recall, specificity, number of parameters, and FLOPs of 94.80%, 94.92%, 94.69%, 18.91M, and 3.46G, respectively. After that, the vocalization detection score and the average confidence strategy were used to predict the sex of individual ducklings, and the accuracy of the proposed model reached 96.67%. In conclusion, the method proposed in this study can effectively detect the sex of ducklings and serve as a reference for automated sex identification of ducklings.

Sex Detection of Chicks Based on Audio Technology and Deep Learning Methods.

The sex detection of chicks is an important work in poultry breeding. Separating chicks of different sexes early can effectively improve production efficiency and commercial benefits. In this paper, based on the difference in calls among one-day-old chicks of different sexes, a sex detection method based on chick calls is designed. Deep learning methods were used to classify the calls of chicks and detect their sex. This experiment studies three different varieties of chicks. The short-time zero-crossing rate was used to automatically detect the endpoints of chick calls in audio. Three kinds of audio features were compared: Spectrogram, Cepstrogram and MFCC+Logfbank. The features were used as the input in neural networks, and there were five kinds of neural networks: CNN, GRU, CRNN, TwoStream and ResNet-50. After the cross-comparison experiment of different varieties of chicks, audio features and neural networks, the ResNet-50 neural network trained with the MFCC+Logfbank audio features of three yellow chick calls had the highest test accuracy of 83% when testing Three-yellow chicks' calls. The GRU neural network trained with the Spectrogram audio features of native chick calls had the highest test accuracy of 76.8% when testing Native chicks' calls. The ResNet-50 neural network trained with Spectrogram audio features of flaxen-yellow chick calls had the highest test accuracy of 66.56%when testing flaxen-yellow chick calls. Multiple calls of each chick were detected, and the majority voting method was used to detect the sex of the chicks. The ResNet-50 neural network trained with the Spectrogram of three yellow chick calls had the highest sex detection accuracy of 95% when detecting the three yellow chicks' sex. The GRU neural network trained with the Spectrogram and cepstrogram of native chick calls and the CRNN network trained with the Spectrogram of native chick calls had the highest sex detection accuracy of 90% when detecting the native chicks' sex. The Twostream neural network trained with MFCC+Logfbank of flaxen-yellow chick calls and the ResNet-50 network trained with the Spectrogram of flaxen-yellow chick calls had the highest sex detection accuracy of 80% when detecting the flaxen-yellow chicks' sex. The results of the cross-comparison experiment show that there is a large diversity between the sex differences in chick calls of different breeds. The method is more applicable to chick sex detection in three yellow chicks and less so in native chicks and flaxen-yellow chicks. Additionally, when detecting the sex of chicks of a similar breed to the training chicks, the method obtained better results, while detecting the sex of chicks of other breeds, the detection accuracy was significantly reduced. This paper provides further perspectives on the sex detection method of chicks based on their calls and help and guidance for future research.

Automatic Segmentation of Nonstationary EM Emission of Electronics Product

Characterizing complex and multi-functional devices is a very challenging task. One problem in statistical near field analysis on complex electronic products is the emergence of nonstationary electromagnetic (EM) signals. Such emergence will lead to an incorrect decision if the signal is used as an input to propagation analysis. The most appropriate approach to this problem seems to be one based on the segmentation of the nonstationary time series obtained from measurements into an ensemble of piecewise stationary signals. In this paper, we propose three approaches for automatic segmentation of nonstationary EM emission signals: short-time energy (STE), short-time zero-crossing rate (STZCR), and short-time kurtosis (STK). Test results show that STE is the best in terms of success in segmenting the nonstationary signals to achieve piecewise stationary time series and being less computationally intensive.

Quality control of microseismic P-phase arrival picks in coal mine based on machine learning

Microseismic events generally contain strong noise-polluting and unobvious P-phase oscillating channel waveforms. The automatic P-phase arrival picking accuracy of these channel waveforms tends to be low, or even are false. Currently, unusable P-picks are not screened out automatically before geophysics inversions in most microseismic data processing software. Therefore, manual interventions are needed to remove or correct the unusable P-picks. However, rapidly increasing monitoring data causes manual handling to be time-consuming and lagging. Supervised machine learning (ML) is applied to distinguish useable and unusable P-picks automatically. Big data analysis revealed that the waveform features, including signal-to-noise ratio, signal-to-noise variance ratio, P-wave starting-up slope, and peak amplitude have impact on P-pick accuracy. In contrast, the effect of the short-time zero-crossing rate on the P-pick accuracy is not as obvious. Five P-pick quality control models were trained based on traditional machine learning approaches, including discriminant analysis, logistic regression, k-nearest neighbor, support vector machine, and Naive Bayes classifier. For these five models, the input data are P-pick labels and waveform features. In addition, another P-pick quality control model was trained based on convolutional neural network. While, the input data are P-pick images and labels. The training sets used in all six machine learning models are uniform. The testing experiments with uniform testing set show that the support vector machine generated best the performance among traditional machine learning approaches, with 82.81% accuracy. However, the convolutional neural network model generated outstanding performance in recognizing P-pick, with 91.71% accuracy. The automatic P-pick quality control method proposed in this study can facilitate the precision and efficiency of the automatic processing of microseismic signals.

A multi-time scale vibration surveillance system for third-party threats on urban pipeline

Third-party interference caused by construction activities have seriously jeopardized the security of underground pipelines. Following the process of signal collection—feature extraction and selection—multi-time scale identifying— combining results by voting, this paper proposes a multi-time scale surveillance system for interference prevention of thirdparty threats on the nearby pipeline by using ground vibration monitors. The system focuses on the two major urban construction activities induced by excavator breaking hammers and road cutters, and presents excellent performance under the noise of traffic and pedestrian. Three features including the short-time zero-crossing rate, subset differential parameter and the Mel frequency cepstrum coefficients are selected by the analysis of the maximal information coefficient and feature importance for identifying the patterns of different third-party activities. The crucial part of the surveillance system consists of the two random forest-based classifiers trained by 0.5 s samples and 8 s samples respectively, and the alarm depends on the voting of the two classifiers, which brings the perspectives on different time scales for decision making. In the test, 96.14% of the threat vibration signals can be detected, while only 0.45% of the environmental noise signals cause false alarms.

Efficient Noisy Sound-Event Mixture Classification Using Adaptive-Sparse Complex-Valued Matrix Factorization and OvsO SVM.

This paper proposes a solution for events classification from a sole noisy mixture that consist of two major steps: a sound-event separation and a sound-event classification. The traditional complex nonnegative matrix factorization (CMF) is extended by cooperation with the optimal adaptive L1 sparsity to decompose a noisy single-channel mixture. The proposed adaptive L1 sparsity CMF algorithm encodes the spectra pattern and estimates the phase of the original signals in time-frequency representation. Their features enhance the temporal decomposition process efficiently. The support vector machine (SVM) based one versus one (OvsO) strategy was applied with a mean supervector to categorize the demixed sound into the matching sound-event class. The first step of the multi-class MSVM method is to segment the separated signal into blocks by sliding demixed signals, then encoding the three features of each block. Mel frequency cepstral coefficients, short-time energy, and short-time zero-crossing rate are learned with multi sound-event classes by the SVM based OvsO method. The mean supervector is encoded from the obtained features. The proposed method has been evaluated with both separation and classification scenarios using real-world single recorded signals and compared with the state-of-the-art separation method. Experimental results confirmed that the proposed method outperformed the state-of-the-art methods.

Fault Diagnosis of High-Voltage Circuit Breakers Using Mechanism Action Time and Hybrid Classifier

Accurate fault diagnosis of high-voltage circuit breakers is crucial for the safety of power grids. Mechanism action time, though can reflect the state of high-voltage circuit breakers, is usually difficult to obtain, as multiple sensors are required for acquisition. To solve this problem, a novel method that can extract mechanism action time from vibration signals was proposed in this paper. The method involved enhancement of vibration signals using short-time zero-crossing rate and determination of mechanism action time by the double threshold method. Then, time parameters were utilized to calculate singular spectrum entropy as the input vector for a classifier. Finally, a hybrid classifier that involved support vector data description and extreme learning machine was developed to identify faults. The hybrid classifier can not only classify known mechanical states but also detect unknown mechanical faults of high-voltage circuit breakers. The effectiveness of the proposed method was verified using a 35-kV high-voltage circuit breaker.

An endpoint-detection algorithm of surface electromyography insensitive to electrocardiogram interference

Surface electromyography (sEMG) has been widely used in the study of clinical medicine, rehabilitation medicine, sports, etc., and its endpoints should be detected accurately before analyzing. However, endpoint detection is vulnerable to electrocardiogram (ECG) interference when the sEMG recorders are placed near the heart. In this paper, an endpoint-detection algorithm which is insensitive to ECG interference is proposed. In the algorithm, endpoints of sEMG are detected based on the short-time energy and short-time zero-crossing rates of sEMG. The thresholds of short-time energy and short-time zero-crossing rate are set according to the statistical difference of short-time zero-crossing rate between sEMG and ECG, and the statistical difference of short-time energy between sEMG and the background noise. Experiment results on the sEMG of rectus abdominis muscle demonstrate that the algorithm detects the endpoints of the sEMG with a high accuracy rate of 95.6%.

A Method for Voiced/Unvoiced Classification of Noisy Speech by Analyzing Time-Domain Features of Spectrogram Image

This paper presents a voiced/unvoiced classification algorithm of the noisy speech signal by analyzing two acoustic features of the speech signal. Short-time energy and short-time zero- crossing rates are one of the most distinguishable time domain features of a speech signal to classify its voiced activity into voiced/unvoiced segment. A new idea is developed where frame by frame processing has done in narrow band speech signal using spectrogram image. Two time domain features, short-time energy (STE) and short-time zero-crossing rate (ZCR) are used to classify its voiced/unvoiced parts. In the first stage, each frame of the analyzing spectrogram is divided into three separate sub bands and examines their short-time energy ratio pattern. Then an energy ratio pattern matching look up table is used to classify the voicing activity. However, this method successfully classifies patterns 1 through 4 but fails in the rest of the patterns in the look up table. Therefore, the rest of the patterns are confirmed in the second stage where frame wise short-time average zero- crossing rate is compared with a threshold value. In this study, the threshold value is calculated from the short-time average zero-crossing rate of White Gaussian Noise (wGn). The accuracy of the proposed method is evaluated using both male and female speech waveforms under different signal-to-noise ratios (SNRs). Experimental results show that the proposed method achieves better accuracy than the conventional methods in the literature.

Two-Feature Voiced/Unvoiced Classifier Using Wavelet Transform

This paper proposes a new wavelet-based algorithm for voice/unvoiced classification of speech segments. The classification process is based on: 1) statistical analysis of the energy-frequency distribution of the speech signal using wavelet transform, and 2) estimation of the short-time zero-crossing rate of the signal. First, the ratio of the average energy in the low-frequency wavelet subbands to that of highest-frequency wavelet subband is computed for each time segment of the pre-emphasised speech using a 4-level dyadic wavelet transform, and compared to a pre-determined threshold. This is followed by measuring the zero- crossing rate of the segment and comparing it to a threshold determined by a continually up-dated value of the median of the zero-crossing rates of the speech signal. An experimentally verified criterion based on the re- sults of the above two comparison processes is then applied to obtain the classification decision. The perform- ance of the algorithm has been evaluated on speech data taken from the TIMIT database, and is shown to yield high classification accuracy and robustness to additive noise. technique for V/UV speech classification using two features of the speech signal: (a) the frequency distribution of the average energy and (b) zero-crossing rate, for each speech segment. The performance of the proposed technique has been evaluated using a large database of both clean speech and speech degraded with additive noise. Following this introduction, the second Section gives an overview of the discrete wavelet transform (DWT). The de- tailed description of the proposed V/UV classification algo- rithm is given next. In the forth Section, the various experi- mental results of the performance evaluation of the algorithm are illustrated and discussed. The article concludes by giving a summary of the proposed algorithm and findings of the evaluation process.

End-point detection of microphone-array speech signals

This paper describes algorithms for automatic end-point detection of microphone-array speech signals. Microphone arrays provide a hands-free sound pickup. The captured sound typically has a higher signal-to-noise ratio (SNR) than that captured with conventional microphones used at distances, such as in teleconferencing environments. However, due to multipath distortion (room reverberation) and ambient noise, the detection of starting/ending points of array speech is more difficult than that of close-talking speech. In this paper, short-time energy and short-time zero-crossing rate are computed for the original speech waveform and its high-pass filtered and low-pass filtered versions. These six functions are then utilized in different combinations to determine the end points. Speech data used in the experiments are collected in a hard-walled laboratory room, having a reverberation time of approximately 0.5 s with a one-dimensional beamforming line array. From the experiments, it is found that the high-pass filtered signal gives a more reliable estimate of end points than does the low-pass filtered counterpart. This result is consistent with the fact that reverberation and noise in rooms are typically more prominent at low frequencies and are relatively moderate at mid- and high frequencies. The detection algorithms have been integrated into a dynamic-time-warping- (DTW) based speech recognizer. Recognition performance of the system is evaluated for both array speech and for close-talking speech.