Mel Frequency Ceptral Coefficients Research Articles

A Novel Series Arc Fault Detection Method Based on Mel-Frequency Cepstral Coefficients and Fully Connected Neural Network

Arc faults pose challenges to electric safety, which can cause serious fire hazards. However, the commonly used arc fault detection method is prone to nuisance tripping. This paper proposed a hybrid arc fault detection method based on the improved Mel-Frequency Ceptral Coefficients (MFCC) for preprocessing and a neural network model for arc identification called ARC_MFCC. As per the IEC 62606, twelve different loads/scenarios are considered for this research. An arc tangent-based core filter is employed to improve the MFCC to enhance the arc features within a bandwidth of 3 kHz to 7 kHz. A lightweight neural network model of fully connected cascaded with the MFCC-based preprocessing, which can distinguish the arc fault with normal operation under different test conditions. As a verification result, the ARC_ MFCC can achieve an accuracy of 99.34%. Moreover, the proposed method is implemented by Raspberry pie 4B. Test results show an average running time of about 4.2ms per sample, which Ensures that the tripping time can meet IEC 62606.

Regional language Speech Emotion Detection using Deep Neural Network

Speaking is the most basic and efficient mode of human contact. Emotions assist people in communicating and understanding others’ viewpoints by transmitting sentiments and providing feedback.The basic objective of speech emotion recognition is to enable computers to comprehend human emotional states such as happiness, fury, and disdain through voice cues. Extensive Effective Method Coefficients of Mel cepstral frequency have been proposed for this problem. The characteristics of Mel frequency ceptral coefficients(MFCC) and the audio based textual characteristics are extracted from the audio characteristics and the hybrid textural framework characteristics of the video are extracted. Voice emotion recognition is used in a variety of applications such as voice monitoring, online learning, clinical investigations, deception detection, entertainment, computer games, and call centres.

Mad Reading Law Classification Using Mel Frequency Cepstal Coefficient (MFCC) and Hidden Markov Model (HMM)

The COVID-19 pandemic is a disaster that hit the world at this time, all activities are limited. This pandemic has also greatly impacted the process of teaching and evaluating the reading of the Koran which was carried out using the talaqqi and musyafahah methods. Machine Learning research has been developed for the legal classification of Quran recitation. This study aims to be able to classify the law of recitation of recitation, especially in the law of Mad recitation of the letter Maryam verses 1 to 15. This study builds a model using the Mel Frequency Ceptral Coefficient (MFCC) feature extraction with the Hidden Markov Model (HMM) algorithm method. MFCC is used for feature extraction in voice that processes voice data in several stages, including pre-emphasis, frame-blocking, windowing, Fast Fourier Transform, Mel Frequency Wrapping, and Ceptral Liftering. HMM is used in speech recognition with standard sentence percentages. The dataset used in this study is voice data taken from the voice of the Quran reciter that has been recognized and has been affiliated. The test results on the model that has been built have an average percentage of 80% accuracy of the test data.

Recognition of human emotion with spectral features using multi layer-perceptron

For emotion recognition, here the features extracted from prevalent speech samples of Berlin emotional database are pitch, intensity, log energy, formant, mel-frequency ceptral coefficients (MFCC) as base features and power spectral density as an added function of frequency. In these work seven emotions namely anger, neutral, happy, Boredom, disgust, fear and sadness are considered in our study. Temporal and Spectral features are considered for building AER(Automatic Emotion Recognition) model. The extracted features are analyzed using Support Vector Machine (SVM) and with multilayer perceptron (MLP) a class of feed-forward ANN classifiers is/are used to classify different emotional states. We observed 91% accuracy for Angry and Boredom emotional classes by using SVM and more than 96% accuracy using ANN and with an overall accuracy of 87.17% using SVM, 94% for ANN.

Fusion Strategies for Multimodal Biometric System Using Face and Voice Cues

Deployment of biometric systems in the applications of real world includes the most of unimodal biometric systems. Unimodal biometric system based on the information collected from single source. Sometimes single source of information may not identify the individual correctly because of some limitations such as Non-universality, Noisy data, Intra-class variation, Spoof attacks and Intra-class similarities. Various limitations of unimodal biometric systems are overridden by the multimodal biometric systems which involves multiple sources of information. Multimodal systems can be constructed by fusing of information of multiple modalities. This fusion can take place at various steps of processing such as at image acquisition, extraction of features of the traits, matching of test vectors with trained vectors and during decision taking based on classification. This paper presents a system of multimodal biometrics using face and voice biometric traits by including four fusion methods. Fusion takes place at i) feature level using concatenation of face and voice features, ii) score level using method involving the maximum of mode of scores obtained from two matchers, iii) rank level using borda count & iv) decision level fusion using logical conjunction (AND). Fusing of Log Gabor & Local Binary Pattern (LBP) takes place at the facial feature extraction. The voice features are also fused using Mel Frequency Ceptral coefficients (MFCCs) and Linear Predictive Coefficient features (LPC). Computation of similarity between test feature vectors and training vectors is carried out using Euclidian distance during matching process. KNN Classifier is used during decision making. Performance evaluation of these techniques are also carried out using performance measures such as Accuracy, False Acceptance Rate (FAR), False Rejection Rate (FRR) and ROC curves.

Performance Measurement Of Mel Frequency Ceptral Coefficient (MFCC) Method In Learning System Of Al- Qur’an Based In Nagham Pattern Recognition

Most of research have used signal and speech processing in order to recognize makhraj pattern and tajwid reading in Al-Quran by exploring the mel frequency ceptral coefficient (MFCC). However, to our knowledge so far there is no research has been conducted to recognize the chanting of Al-Quran verse using MFCC. This term is also well-known as nagham Al-Quran. The characteristics of nagham Al-Quran pattern is much more complex then makhraj and tajwid pattern. In nagham the wave of the sound has more variation which implies the level of noice is much higher and has sound duration longer. The data testing in this research was taken term by real-time recording. The evaluation measurement in the system performance of nagham Al-Quran pattern is based on true and false detection parameter with accuracy 80%. To measure this accuracy it is necessary to modify the MFCC or to give more data learning process with more variation.

Prediction of Beck Depression Inventory (BDI-II) Score Using Acoustic Measurements in a Sample of Iium Engineering Students

Psychiatrist currently relies on questionnaires and interviews for psychological assessment. These conservative methods often miss true positives and might lead to death, especially in cases where a patient might be experiencing suicidal predisposition but was only diagnosed as major depressive disorder (MDD). With modern technology, an assessment tool might aid psychiatrist with a more accurate diagnosis and thus hope to reduce casualty. This project will explore on the relationship between speech features of spoken audio signal (reading) in Bahasa Malaysia with the Beck Depression Inventory scores. The speech features used in this project were Power Spectral Density (PSD), Mel-frequency Ceptral Coefficients (MFCC), Transition Parameter, formant and pitch. According to analysis, the optimum combination of speech features to predict BDI-II scores include PSD, MFCC and Transition Parameters. The linear regression approach with sequential forward/backward method was used to predict the BDI-II scores using reading speech. The result showed 0.4096 mean absolute error (MAE) for female reading speech. For male, the BDI-II scores successfully predicted 100% less than 1 scores difference with MAE of 0.098437. A prediction system called Depression Severity Evaluator (DSE) was developed. The DSE managed to predict one out of five subjects. Although the prediction rate was low, the system precisely predict the score within the maximum difference of 4.93 for each person. This demonstrates that the scores are not random numbers.

Motorcycle Start-stop System based on Intelligent Biometric Voice Recognition

Current mechanical key in the motorcycle is prone to bulgary, being stolen or misplaced. Intelligent biometric voice recognition as means to replace this mechanism is proposed as an alternative. The proposed system will decide whether the voice is belong to the user or not and the word utter by the user is ‘On’ or ‘Off’. The decision voice will be sent to Arduino in order to start or stop the engine. The recorded voice is processed in order to get some features which later be used as input to the proposed system. The Mel-Frequency Ceptral Coefficient (MFCC) is adopted as a feature extraction technique. The extracted feature is the used as input to the SVM-based identifier. Experimental results confirm the effectiveness of the proposed intelligent voice recognition and word recognition system. It show that the proposed method produces a good training and testing accuracy, 99.31% and 99.43%, respectively. Moreover, the proposed system shows the performance of false rejection rate (FRR) and false acceptance rate (FAR) accuracy of 0.18% and 17.58%, respectively. In the intelligent word recognition shows that the training and testing accuracy are 100% and 96.3%, respectively.

Feature Extraction Using Mfcc

Mel Frequency Ceptral Coefficient is a very common and efficient technique for signal processing. This paper presents a new purpose of working with MFCC by using it for Hand gesture recognition. The objective of using MFCC for hand gesture recognition is to explore the utility of the MFCC for image processing. Till now it has been used in speech recognition, for speaker identification. The present system is based on converting the hand gesture into one dimensional (1-D) signal and then extracting first 13 MFCCs from the converted 1-D signal. Classification is performed by using Support Vector Machine. Experimental results represents that proposed application of using MFCC for gesture recognition have very good accuracy and hence can be used for recognition of sign language or for other household application with the combination for other techniques such as Gabor filter, DWT to increase the accuracy rate and to make it more efficient.

Exploring Sound Signature for Vehicle Detection and Classification Using ANN

This paper attempts to explore the possibility of using sound signatures for vehicle detection and classification purposes. Sound emitted by vehicles are captured for a two lane undivided road carrying moderate traffic. Simultaneous arrival of different types vehicles, overtaking at the study location, sound of horns, random but identifiable back ground noises, continuous high energy noises on the back ground are the different challenges encountered in the data collection. Different features were explored out of which smoothed log energy was found to be useful for automatic vehicle detection by locating peaks. Melfrequency ceptral coefficients extracted from fixed regions around the detected peaks along with the manual vehicle labels are utilised to train an Artificial Neural Network (ANN). The classifier for four broad classes heavy, medium, light and horns was trained. The ANN classifier developed was able to predict categories well.

A Method for Automatic Fall Detection of Elderly People Using Floor Vibrations and Sound—Proof of Concept on Human Mimicking Doll Falls

Falls are a major risk for the elderly people living independently. Rapid detection of fall events can reduce the rate of mortality and raise the chances to survive the event and return to independent living. In the last two decades, several technological solutions for detection of falls were published, but most of them suffer from critical limitations. In this paper, we present a proof of concept to an automatic fall detection system for elderly people. The system is based on floor vibration and sound sensing, and uses signal processing and pattern recognition algorithm to discriminate between fall events and other events. The classification is based on special features like shock response spectrum and mel frequency ceptral coefficients. For the simulation of human falls, we have used a human mimicking doll: "Rescue Randy." The proposed solution is unique, reliable, and does not require the person to wear anything. It is designed to detect fall events in critical cases in which the person is unconscious or in a stress condition. From the preliminary research, the proposed system can detect human mimicking dolls falls with a sensitivity of 97.5% and specificity of 98.6%.

Incorporating a mixed excitation model and postfilter into HMM-based text-to-speech synthesis

In this paper we introduce a mixed excitation model into an HMM-based speech synthesis system with the objective of improving the quality of synthesized speech. In previous work we have proposed a text-to-speech synthesis system that synthesized speech by outputting speech parameters using HMMs that model Mel frequency ceptral coefficients, fundamental frequencies, and duration. In that system we used a simple model as the excitation source model for exciting the synthesis filter (an MLSA filter) whereby we switched between a pulse sequence and white noise for intervals of voiced and unvoiced speech, respectively. When using that type of excitation model, it is not possible to synthesize speech such as voiced fricatives that contains both a periodic and an aperiodic component, and this is a cause of poor synthesized speech quality. Therefore, in this paper we incorporate a mixed excitation model based on a narrowband vocoding method MELP that combines a pulse stream with white noise with a view to realizing high-quality speech synthesis. Since this excitation model can be applied to wideband vocoding as well as narrowband, we anticipate that it will prove effective for speech synthesis. In addition, we introduce a widely used vocoding method, a postfilter, in order to improve the quality of the synthesized speech. In addition, the results of subjective evaluation show the effectiveness of the mixed excitation model and postfilter in this system. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(12): 43–50, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.20354