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Short-term feeding behaviour sound classification method for sheep using LSTM networks

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International Journal of Agricultural and Biological Engineering 2021年第2期14卷 43-54页

作者： Guanghui Duan Shengfu Zhang Mingzhou Lu Cedric Okinda Mingxia Shen Tomas Norton College of Artificial Intelligence Nanjing Agricultural UniversityNanjing 210031China Computer College Qinghai Nationalities UniversityXining 810007China M3-BIORES-Measure Model&Manage BioresponsesKU LeuvenHeverlee/Leuven 3001Belgium

A deep learning approach using long-short term memory(LSTM)networks was implemented in this study to classify the sound of short-term feeding behaviour of sheep,including biting,chewing,bolus regurgitation,and rumination *** original acoustic signal was split into sound episodes using an endpoint detection method,where the thresholds of short-term energy and average zero-crossing rate were utilized.A discrete wavelet transform(DWT),mel-frequency cepstral,and principal-component analysis(PCA)were integrated to extract the dimensionally reduced DWT based mel-frequency cepstral coefficients(denoted by PW_MFCC)for each sound ***,LSTM networks were employed to train classifiers for sound episode category *** performances of the LSTM classifiers with original mel-frequency cepstral coefficients(MFCC),DWT based MFCC(denoted by W_MFCC),and PW_MFCC as the input feature coefficients were *** results demonstrated that the introduction of DWT improved the classifier performance effectively,and PCA reduced the computational overhead without degrading classifier *** overall accuracy and comprehensive F1-score of the PW_MFCC based LSTM classifier were 94.97%and 97.41%,*** classifier established in this study provided a foundation for an automatic identification system for sick sheep with abnormal feeding and rumination behaviour pattern.

关键词： sheep behaviour short-term feeding behaviour acoustic analysis mel-frequency cepstral coefficients long-short term memory networks

Robust Speech Recognition System Using Conventional and Hybrid Features of MFCC,LPCC,PLP,RASTA-PLP and Hidden Markov Model Classifier in Noisy Conditions

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Journal of Computer and Communications 2015年第6期3卷 1-9页

作者： Veton Z.Kepuska Hussien A.Elharati Electrical&Computer Engineering Department Florida Institute of TechnologyMelbourneFLUSA

In recent years, the accuracy of speech recognition (SR) has been one of the most active areas of research. Despite that SR systems are working reasonably well in quiet conditions, they still suffer severe performance degradation in noisy conditions or distorted channels. It is necessary to search for more robust feature extraction methods to gain better performance in adverse conditions. This paper investigates the performance of conventional and new hybrid speech feature extraction algorithms of mel frequency Cepstrum Coefficient (MFCC), Linear Prediction Coding Coefficient (LPCC), perceptual linear production (PLP), and RASTA-PLP in noisy conditions through using multivariate Hidden Markov Model (HMM) classifier. The behavior of the proposal system is evaluated using TIDIGIT human voice dataset corpora, recorded from 208 different adult speakers in both training and testing process. The theoretical basis for speech processing and classifier procedures were presented, and the recognition results were obtained based on word recognition rate.

关键词： Speech Recognition Noisy Conditions Feature Extraction mel-frequency cepstral coefficients Linear Predictive Coding coefficients Perceptual Linear Production RASTA-PLP Isolated Speech Hidden Markov Model

Environmental Sound Classification Using Deep Learning

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Instrumentation 2020年第3期7卷 15-22页

作者： SHANTHAKUMAR S SHAKILA S SUNETH Pathirana JAYALATH Ekanayake Dept.of Computer Science and Informatics Faculty of Applied SciencesUva Wellassa UniversityPassara Rd.Badulla90000Sri Lanka

Perhaps hearing impairment individuals cannot identify the environmental sounds due to noise around ***,very little research has been conducted in this ***,the aim of this study is to categorize sounds generated in the environment so that the impairment individuals can distinguish the sound *** that end first we define nine sound classes--air conditioner,car horn,children playing,dog bark,drilling,engine idling,jackhammer,siren,and street music--typically exist in the *** we record 100 sound samples from each category and extract features of each sound category using mel-frequency cepstral coefficients(MFCC).The training dataset is developed using this set of features together with the class variable;sound *** classification is a complex task and hence,we use two Deep Learning techniques;Multi Layer Perceptron(MLP)and Convolution Neural Network(CNN)to train classification *** models are tested using a separate test set and the performances of the models are evaluated using precision,recall and *** results show that the CNN model outperforms the ***,the MLP also provided a decent accuracy in classifying unknown environmental sounds.

关键词： mel-frequency cepstral coefficients MFCC Multi-Layer Perceptron MLP Convolutional Neural Network CNN

Wake-Up-Word Feature Extraction on FPGA

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World Journal of Engineering and Technology 2014年第1期2卷 1-12页

作者： Veton ZKepuska Mohamed meljhani Brian HHight Electrical&Computer Engineering Department Florida Institute of TechnologyMelbourneUSA

Wake-Up-Word Speech Recognition task (WUW-SR) is a computationally very demand, particularly the stage of feature extraction which is decoded with corresponding Hidden Markov Models (HMMs) in the back-end stage of the WUW-SR. The state of the art WUW-SR system is based on three different sets of features: mel-frequency cepstral coefficients (MFCC), Linear Predictive Coding coefficients (LPC), and Enhanced mel-frequency cepstral coefficients (ENH_MFCC). In (front-end of Wake-Up-Word Speech Recognition System Design on FPGA) [1], we presented an experimental FPGA design and implementation of a novel architecture of a real-time spectrogram extraction processor that generates MFCC, LPC, and ENH_MFCC spectrograms simultaneously. In this paper, the details of converting the three sets of spectrograms 1) mel-frequency cepstral coefficients (MFCC), 2) Linear Predictive Coding coefficients (LPC), and 3) Enhanced mel-frequency cepstral coefficients (ENH_MFCC) to their equivalent features are presented. In the WUW- SR system, the recognizer’s frontend is located at the terminal which is typically connected over a data network to remote back-end recognition (e.g., server). The WUW-SR is shown in Figure 1. The three sets of speech features are extracted at the front-end. These extracted features are then compressed and transmitted to the server via a dedicated channel, where subsequently they are decoded.

关键词： Speech Recognition System Feature Extraction mel-frequency cepstral coefficients Linear Predictive Coding coefficients Enhanced mel-frequency cepstral coefficients Hidden Markov Models Field-Programmable Gate Arrays

Application of Hidden Markov Models in Speech Command Recognition

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Journal of Mechanics Engineering and Automation 2020年第2期10卷 41-45页

作者： Shing-Tai Pan Zong-Hong Huang Sheng-Syun Yuan Xu-Yu Li Yu-De Su Jia-Hua Li Department of Computer Science and Information Engineering National University of KaohsiungKaohsiung 811TaiwanR.O.C.

In this study,vector quantization and hidden Markov models were used to achieve speech command ***-emphasis,a hamming window,and mel-frequency cepstral coefficients were first adopted to obtain feature ***,vector quantization and HMMs(hidden Markov models)were employed to achieve speech command *** recorded speech length was three Chinese characters,which were used to test the *** phrases pronounced mixing various human voices were recorded and used to test the *** recorded phrases were then used for speech command recognition to demonstrate whether the experiment results were satisfactory.

关键词： HMMs mel-frequency cepstral coefficients speech command recognition vector quantization