Mel Frequency Cepstral Coefficient Method Research Articles

Application and Improvement of MFCC in Gesture Recognition with Surface Electromyography

As a physiological signal reflecting the state of muscle activation, surface electromyography (sEMG) plays a vital role in the assessment of neuromuscular health, human–computer interaction, and gait analysis. Inspired by the audio signal analysis outcome that features extracted with Mel Frequency Cepstral Coefficient (MFCC) empower better representation, this paper proposes a comparative study of a gesture recognition method by using and improving with the MFCC features of sEMG. Comparing and combining with the conventional time-domain and frequency-domain features, different learning-based techniques are deployed to evaluate the performance of the proposed approach on the NinaPro datasets. The proposed approach was evaluated on the NinaPro-DB1 and NinaPro-DB2 datasets, achieving the improvements of 3.42% and 3.67%, respectively, in terms of the highest accuracy using the standard MFCC method. Correspondingly, when combined with the improved MFCC, the accuracy was further increased, reaching the maximum values of 89.82% and 87.82%, respectively, on the two datasets. The impact on the performance reveals the effectiveness of MFCC, and the results show that the proposed method has the potential to realize high-precision gesture recognition.

Detection method for electric impact drill parts defect based on auditory peripheral model

The accurate non-contact detection for electric impact drill parts defect can significantly improve the production efficiency. In this paper, an acoustic-based method is proposed to detect parts defect by mimicking the physiological auditory perceptual principles. The method firstly combined Gammatone filter banks and Meddis model to simulate cochlear processing mechanism to calculate the time–frequency information of the electric impact drill working acoustic signal. Then the subband energy-entropy differential ratio was described to extract the time–frequency features. Based on the optimized features by the principal component analysis, the parts defect detection was achieved by the support vector machine. The results with collected electric impact drill acoustic data show that the detection accuracy by this method can reach 97.8 %, which is 5.12 % higher than the Mel frequency cepstral coefficient method and 7.56 % higher than the time–frequency domain feature method. The proposed method can provide a new idea for non-contact part defect detection of electric impact drill in factories.

SPEECH TO TEXT BAHASA SASAK MENGGUNAKAN EXTRAKSI FITUR MEL-FREQUENCY CEPSTRAL COEFFICIENTS DAN KLASIFIKASI CONVOLUTIONAL NEURAL NETWORKS

Artificial intelligence technology allows digital signals to be processed by computers. Currently speech to text is available only in Indonesian and English versions. Speech to text is a system that performs commands from human voice input and is then translated into words. The development of speech to text in regional languages ​​is needed because it can be a bridge between culture and technological progress. From 5 research literature, it was found that the mel-frequency cepstral coefficients (MFCC) and convolutional neural networks (CNN) methods are a combination of the commonly used voice signal analysis methods and get accuracy between 70.00% to 99.00%. This study uses the CNN and MFCC methods in the speech to text field to recognize the Sasak language and convert it into text. The result of this research is a real time conversion system from voice to text in Sasak language. The analysis carried out includes determining the best amount of training data, testing the training data on the number of votes based on accuracy, the sensitivity of the algorithm to words that have similar prefixes using the MFCC method as feature extraction and CNN as a classifier for the voice dataset. This study aims to obtain the accuracy of the dataset used and the sensitivity of the algorithm to sentences that have similarities. In this study got 2 results. The first result is the result of training with the accuracy of CNN training is 90% and loss is 0.5%. The second result is the result of an experiment using 3 voice samples for each word on the dateset with 43 correct words, 6 correct words 2, 1 correct word 1 and none of the words incorrect. So it has a percentage of 86% all correct, 12% correct 2, and 2% correct 1, and 0% all wrong.

A learning framework of modified deep recurrent neural network for classification and recognition of voice mood

SUMMARYRecognition of human emotions is a basic requirement in many real‐time applications. Detection of exact emotions through voice provides relevant information for various purposes. Several computational methods have been employed for the clear analysis of human emotions. Most of the previous approaches face complexities due to certain drawbacks like degraded signal quality, a requirement of high storage space, increased computational complexity, and deteriorated outcomes of classification accuracy. The proposed work was implemented to gather the accurate classification result of embedded emotions and minimize the computational complexities of MDDTRNN (modified deep duck and traveler recurrent neural network). The proposed work includes four steps: preprocessing, feature extraction, feature selection, and classification. In feature extraction, the spectral and frequency features are extracted using the adopting boosted MFCC (Mel frequency cepstral coefficients) method to improve training speed. In feature selection, the best features are selected using an algorithm of AAVOA (adaptive African vulture optimization algorithm). To provide optimal emotion results, the classification step is undertaken by the MDDTRNN technique. The proposed work shows better classification outcomes of emotions when compared to the existing approaches by holding the accuracy of (95.86%), precision as (93.79%), specificity as (94.28%), sensitivity as (92.89%) and the error rate is attained to be 5.266 in terms of IEMOCAP dataset. The accuracy result (96.27%), precision (94.83%), specificity (93.16%), sensitivity (94%) and the error rate is achieved to be 4.982 in terms of the EMODB dataset.

Satranç Oyunu için CNN Kullanılarak Türkçe Komutları Tanıma

A platform has been created that allows playing chess with Turkish voice commands. The aim of this study is to enable individuals with limited movement abilities as a result of congenital reasons or a certain disease or accident to play chess and perform a social activity without the help of another person, and to be rehabilitated at the same time. It consists of three parts: Chess module, Human-computer interaction module and Artificial Intelligence module. 29 words have been determined to provide movement in the game on the platform. Voice recordings from 151 people, 86 men and 65 women, were used. Feature selection was made on 43790 voice recordings by using mel frequency cepstral coefficients (MFCC) and gammatone cepstral coefficients (GTCC) methods. The results obtained were classified using the traditional CNN model. The data obtained after using MFCC and GTCC methods were used as inputs in the CNN model. In addition, the data obtained by the two methods were combined and trained in the model. Depending on the methods used in the created model, 83% to 85.9% results were obtained. It was determined that the results obtained using the MFCC method were more successful.

Frequency domain analysis of MFCC feature extraction in children’s speech recognition system

Abstract —The research on speech recognition systems currently focuses on the analysis of robust speech recognition systems. When the speech signals are combined with noise, the recognition system becomes distracted, struggling to identify the speech sounds. Therefore, the development of a robust speech recognition system continues to be carried out. The principle of a robust speech recognition system is to eliminate noise from the speech signals and restore the original information signals. In this paper, researchers conducted a frequency domain analysis on one stage of the Mel Frequency Cepstral Coefficients (MFCC) process, the Fast Fourier Transform (FFT), in children's speech recognition system. The FTT analysis in the feature extraction process determined the effect of frequency value characteristics utilized in the FFT output on the noise disruption. The analysis method was designed into three scenarios based on the value of the employed FFT points. The differences between scenarios were based on the number of shared FFT points. All FFT points were divided into four, three, and two parts in the first, second, and third scenarios, respectively. This study utilized children's speech data from the isolated TIDIGIT English digit corpus. As comparative data, the noise was added manually to simulate real-world conditions. The results showed that using a particular frequency portion following the scenario designed on MFCC affected the recognition system performance, which was relatively significant on the noisy speech data. The designed method in the scenario 3 (C1) version generated the highest accuracy, exceeded the accuracy of the conventional MFCC method. The average accuracy in the scenario 3 (C1) method increased by 1% more than all the tested noise types. Using various noise intensity values (SNR), the testing process indicates that scenario 3 (C1) generates a higher accuracy than conventional MFCC in all tested SNR values. It proves that the selection of specific frequency utilized in MFCC feature extraction significantly affects the recognition accuracy in a noisy speech.

Automatic Speaker Recognition Using Mel-Frequency Cepstral Coefficients Through Machine Learning

Automatic speaker recognition (ASR) systems are the field of Human-machine interaction and scientists have been using feature extraction and feature matching methods to analyze and synthesize these signals. One of the most commonly used methods for feature extraction is Mel Frequency Cepstral Coefficients (MFCCs). Recent researches show that MFCCs are successful in processing the voice signal with high accuracies. MFCCs represents a sequence of voice signal-specific features. This experimental analysis is proposed to distinguish Turkish speakers by extracting the MFCCs from the speech recordings. Since the human perception of sound is not linear, after the filterbank step in the MFCC method, we converted the obtained log filterbanks into decibel (dB) features-based spectrograms without applying the Discrete Cosine Transform (DCT). A new dataset was created with converted spectrogram into a 2-D array. Several learning algorithms were implemented with a 10-fold cross-validation method to detect the speaker. The highest accuracy of 90.2% was achieved using Multi-layer Perceptron (MLP) with tanh activation function. The most important output of this study is the inclusion of human voice as a new feature set.

The Comparison of Audio Analysis Using Audio Forensic Technique and Mel Frequency Cepstral Coefficient Method (MFCC) as the Requirement of Digital Evidence

Audio forensics is the application of science and scientific methods in handling digital evidence in the form of audio. In this regard, the audio supports the disclosure of various criminal cases and reveals the necessary information needed in the trial process. So far, research related to audio forensics is more on human voices that are recorded directly, either by using a voice recorder or voice recordings on smartphones, which are available on Google Play services or iOS Store. This study compares the analysis of live voices (human voices) with artificial voices on Google Voice and other artificial voices. This study implements the audio forensic analysis, which involves pitch, formant, and spectrogram as the parameters. Besides, it also analyses the data by using feature extraction using the Mel Frequency Cepstral Coefficient (MFCC) method, the Dynamic Time Warping (DTW) method, and applying the K-Nearest Neighbor (KNN) algorithm. The previously made live voice recording and artificial voice are then cut into words. Then, it tests the chunk from the voice recording. The testing of audio forensic techniques with the Praat application obtained similar words between live and artificial voices and provided 40,74% accuracy of information. While the testing by using the MFCC, DTW, KNN methods with the built systems by using Matlab, obtained similar word information between live voice and artificial voice with an accuracy of 33.33%.

The Implementation Of Mfcc Feature Extraction And Selection of Cepstral Coefficient for Qur’an Recitation in TPA (Qur’an Learning Center) Nurul Huda Plus Purbayan

There are two approaches to Qur’an recitation, namely talaqqi and qira'ati. Both approaches use the science of recitation containing knowledge of the rules and procedures for reading the Qur'an properly. Talaqqi requires the teacher and students to sit facing each other while qira'ati is the recitation of the Qur'an with rhythms and tones. Many studies have developed an automatic speech recognition system for Qur’an recitation to help the learning process. Feature extraction model using Mel Frequency Cepstral Coefficient (MFCC) and Linear Predictive Code (LPC). The MFCC method has an accuracy of 50% to 60% while the accuracy of Linear Predictive Code (LPC) is only 45% to 50%, so the non-linear MFCC method has higher accuracy than the linear approach method. The cepstral coefficient feature that is used starts from 0 to 23 or 24 cepstral coefficients. Meanwhile, the frame taken consists of 0 to 10 frames or eleven frames. Voting for 300 recorded voice samples was tested against 200 voice recordings, both male and female voices. The frequency used was 44.100 kHz stereo 16 bit. This study aims to obtain good accuracy by selecting the right feature on the cepstral coefficient using MFCC feature extraction and matching accuracy through the selection of the cepstral coefficient feature with Dominant Weight Normalization (NBD) at TPA Nurul Huda Plus Purbayan. Accuracy results showed that the MFCC method with the selection of the 23rd cepstral coefficient has a higher accuracy rate of 90.2% compared to the others. It can be concluded that the selection of the right features on the 23rd cepstral coefficient affects the accuracy of the voice of Qur’an recitation.

Comparison of Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction, With and Without Framing Feature Selection, to Test the Shahada Recitation

Voice research for feature extraction using MFCC. Introduction with feature extraction as the first step to get features. Features need to be done further through feature selection. The feature selection in this research used the Dominant Weight feature for the Shahada voice, which produced frames and cepstral coefficients as the feature extraction. The cepstral coefficient was used from 0 to 23 or 24 cepstral coefficients. At the same time, the taken frame consisted of 0 to 10 frames or eleven frames. Voting as many as 300 samples of recorded voices were tested on 200 voices of both male and female voice recordings. The frequency used was 44.100 kHz 16-bit stereo. This research aimed to gain accuracy by selecting the right features on the frame using MFCC feature extraction and matching accuracy with frame feature selection using the Dominant Weight Normalization (NBD). The accuracy results obtained that the MFCC method with the selection of the 9th frame had a higher accuracy rate of 86% compared to other frames. The MFCC without feature selection had an average of 60%. The conclusion was that selecting the right features in the 9th frame impacted the accuracy of the voice of shahada recitation.

Identifying COVID-19 by using spectral analysis of cough recordings: a distinctive classification study.

Sound signals from the respiratory system are largely taken as tokens of human health. Early diagnosis of respiratory tract diseases is of great importance because, if delayed, it exerts irreversible effects on human health. The Coronavirus pandemic, which is deeply shaking the world, has revealed the importance of this diagnosis even more. During the pandemic, it has become the focus of researchers to differentiate symptoms from similar diseases such as influenza. Among these symptoms, the difference in cough sound played a distinctive role in research. Clinical data collected under the supervision of doctors in a reliable environment were used as the dataset consisting of 16 subjects suspected of COVID-19 with a specific patient demographic. Using the polymerase chain reaction test, the suspected subjects were divided into two groups as negative and positive. The negative and positive labels represent the patients with non-COVID and with a COVID-19 cough, respectively. Using the 3D plot or waterfall representation of the signal frequency spectrum, the salient features of the cough data are revealed. In this way, COVID-19 can be differentiated from other coughs by applying effective feature extraction and classification techniques. Power spectral density based on short-time Fourier transform and mel-frequency cepstral coefficients (MFCC) were chosen as the efficient feature extraction method. From among the classification techniques, the support vector machine (SVM) algorithm was applied to the processed signals in order to identify and classify COVID-19 cough. In terms of results evaluation, the cough of subjects with COVID-19 was detected with 95.86% classification accuracy thanks to the radial basis function (RBF) kernel function of SVM and the MFCC method. The diagnosis of COVID-19 coughs was performed with 98.6% and 91.7% sensitivity and specificity, respectively.

Classification of Javanese Script Hanacara Voice Using Mel Frequency Cepstral Coefficient MFCC and Selection of Dominant Weight Features

This study investigates the sound of Hanacaraka in Javanese to select the best frame feature in checking the reading sound. Selection of the right frame feature is needed in speech recognition because certain frames have accuracy at their dominant weight, so it is necessary to match frames with the best accuracy. Common and widely used feature extraction models include the Mel Frequency Cepstral Coefficient (MFCC). The MFCC method has an accuracy of 50% to 60%. This research uses MFCC and the selection of Dominant Weight features for the Javanese language script sound Hanacaraka which produces a frame and cepstral coefficient as feature extraction. The use of the cepstral coefficient ranges from 0 to 23 or as many as 24 cepstral coefficients. In comparison, the captured frame consists of 0 to 10 frames or consists of eleven frames. A sound sampling of 300 recorded voice sampling was tested on 300 voice recordings of both male and female voice recordings. The frequency used is 44,100 kHz 16-bit stereo. The accuracy results show that the MFCC method with the ninth frame selection has a higher accuracy rate of 86% than other frames.

Good Morning to Good Night Greeting Classification Using Mel Frequency Cepstral Coefficient (MFCC) Feature Extraction and Frame Feature Selection

Purpose:Select the right features on the frame for good accuracyDesign/methodology/approach:Extraction of Mel Frequency Cepstral Coefficient (MFCC) Features and Selection of Dominant Weight Normalized (DWN) FeaturesFindings/result:The accuracy results show that the MFCC method with the 9th frame selection has a higher accuracy rate of 85% compared to other frames.Originality/value/state of the art:Selection of the appropriate features on the frame.

A Modified MFCC for Improved Wavelet-Based Denoising on Robust Speech Recognition

Research on the current speech recognition system leads to the creation of a noise-resistant system. The Mel Frequency Cepstral Coefficients (MFCC) extraction method becomes a popular method in the speech recognition system. In this paper, the MFCC's weakness of noise interference is the main reason underlies the accomplishment of a robust speech recognition system. Development was carried out by improving the denoising performance using a wavelet transform. Modifications were carried out by analyzing the weakness of the wavelet denoising process on the recognition system using the MFCC method. The analysis was conducted at one of the MFCC stages, the Fast Fourier Transform (FFT) stage. The proposed method was conducted by performing the denoising process using Wavelet only on the noise-related data based on the FFT process' analysis results. The study utilized speech data in the form of eleven isolated words in English added with noise with several different characteristics. Results showed that the proposed method was capable of generating a better accuracy than conventional wavelet denoising methods on the signal to noise ratio (SNR) of 10dB, 15dB, and 20dB using a Fejer Korovkin 6 wavelet type. The highest accuracy increase of the proposed method was in signal to noise ratio (SNR) of 15dB with a rise of 4.63%, followed by a 3.96% increase at 20dB intensity, and 2.3% at 10dB intensity. The performance of the proposed method is then compared with other methods. The results show that the proposed method has the best performance on clean speech and noisy speech at SNR intensities of 10dB, 15dB, and 20dB.

PERANCANGAN SISTEM PENCARIAN LAGU INDONESIA MENGGUNAKAN QUERY BY HUMMING BERBASIS LONG SHORT-TERM MEMORY

Song identification dan query by humming is an application that is developed using Mel-frequency cepstral coefficients (MFCC) and Long Short-Term Memory (LSTM) algorithm.The application purpose is to detect and recognize humming from the input data. In this application the humming input will be divided into two parts, namely the training audio and test audio. For the training audio, the training audio will be divided into two process stages, namely recognizing humming and searching for the unique features of a humming audio.To recognize the humming feature, the humming will be processed using the MFCC method. After obtaining a part of the MFCC Features, the MFCC features will be saved as a vector model. The feature that has been extracted will be learned by the LSTM method. For the test audio of the stages carried out as in the training audio, after the MFCC Feature is detected, an introduction will be made based on learning that has been done with the LSTM method to obtain output in the form of a song name that is successfully recognized and detected will be labeled by the application.

Інтелектуальний модуль розпізнавання емоцій за голосом

Speech is the main way of communication for people, and people can receive not only semantic but also emotional information from speech. Recognition of emotions by voice is relevant to areas such as psychological care, security systems development, lie detection, customer relationship analysis, video game development. Because the recognition of emotions by a person is subjective, and therefore inexact and time consuming, there is a need to create software that could solve this problem. The article considers the state of the problem of recognizing human emotions by voice. Modern publications, the approaches used in them, namely models of emotions, data sets, methods of extraction of signs, classifiers are analyzed. It is determined that existing developments have an average accuracy of about 0.75. The general structure of the system of recognition of human emotions by voice is analyzed, the corresponding intellectual module is designed and developed. A Unified Modeling Language (UML) is used to create a component diagram and a class diagram. RAVDESS and TESS datasets were selected as datasets to diversify the training sample. A discrete model of emotions (joy, sadness, anger, disgust, fear, surprise, calm, neutral emotion), MFCC (Mel Frequency Cepstral Coefficients) method for extracting signs, convolutional neural network for classification were used. . The neural network was developed using the TensorFlow and Keras machine learning libraries. The spectrogram and graphs of the audio signal, as well as graphs of accuracy and recognition errors are constructed. As a result of the software implementation of the intelligent module for recognizing emotions by voice, the accuracy of validation has been increased to 0.8.

Identifikasi Pola Suara Pada Bahasa Jawa Meggunakan Mel Frequency Cepstral Coefficients (MFCC)

Voice Recognition is a process of developing systems used between computer and human. The purpose of this study is to find out the sound pattern of a person based on the spoken Javanese language. This study used the Mel Frequency Cepstral Coefficients (MFCC) method to solve the problem of feature extraction from human voices. Tests were carried out on 4 users consisting of 2 women and 2 men, each saying 1 word "KUTHO", the word pronounced 5 times. The results of the testing are to get a sound pattern from the characteristics of 1 person with another person so that research using the MFCC method can produce different sound patterns

Application of Digital Sound Recognition on Piano Tones Using MFCC and LVQ

In playing musical instruments especially the piano, a musician needs a score as the guidance to play the song. A score is a music sheet used by an artist to store or deliver a song. Many pianists do not have the expertise to create a score, especially in the making of spontaneous music. Therefore, an app is required to assist the pianists in the creation process of scores for piano music sounds. Mel-Frequency Cepstral Coefficient (MFCC) extraction method and Learning Vector Quantization method were implemented to build the application. The MFCC method was used to extract the vectors that reside in a song, while LVQ method was applied to match the testing data with the trained data. The output of this system is a musical score of the inputted song.

Multitaper MFCC and normalized multitaper phase-based features for speaker verification

One trend of current research activity in speaker verification systems focuses on features that capture complementary information to the Mel-frequency cepstral coefficients (MFCCs). In conventional speaker verification methods, MFCCs are usually computed from a single-tapered DFT spectrum. These methods use the power of the Fourier transform of the time-domain speech frames and ignore the phase component due to its large amount of uncertainty. The multitaper phase information has been also ignored when the multitaper MFCC method applies. We propose a phase information extraction method that normalizes the change variation in the multitaper phase. The goal of this work is to incorporate phase information using low-variance multitaper spectrum estimation method instead of conventional single-taper window and normalizes the change variation in multitaper phase according to the frame position of the input speech to reduce the uncertainty of multitaper phase information in both the state-of-the-art Gaussian mixture model-universal background model (GMM-UBM) baseline and the i-vector speaker verification system. The experiments are conducted on the TIMIT database for both the clean and noisy conditions (SNR = 10 dB). The multitaper inverted-MFCC is also added to the overall system to produce diversity with respect to the fused subsystem. The results show relative improvement of 34% and 25% in terms of equal error rate (EER) over the MFCC baseline in clean and noisy conditions for GMM-UBM system, respectively. The relative improvement in terms of EER with i-vector back-end are about 36% and 16% in clean and noisy conditions as well.

An Acoustic-Based Feature Extraction Method for the Classification of Moving Vehicles in the Wild

The acoustic classification technology of moving vehicles is a significant application in wireless sensor networks (WSNs). However, the wild environment makes it more intractable for the acoustic target classification owing to the complicated interference noise. The widely used mel-frequency cepstral coefficients (MFCCs) are somewhile contaminated by the acoustic noise, resulting in degrading the classification performance in real environments. To increase the classification accuracy of the moving vehicles, this paper presents an acoustic feature extraction method, which integrates the nonlinear function with the MFCC method, termed as NMFCC. Through nonlinear transformation, the proposed method could amplify the acoustic features in a nonlinear way, and obtain more robust acoustic features, thereby achieving a superior classification performance. Comparing the NMFCC with the baseline feature extraction method, experimental results not only demonstrate the viability of the proposed algorithm but also show a satisfactory performance of the moving vehicle classification for diverse practical scenarios in the wild.