Mel Frequency Cepstral Coefficients Features Research Articles

A telemedicine software application for asthma severity levels identification using wheeze sounds classification

Early and precise knowledge of asthma severity levels may help in effective precautions, proper medication, and follow-up planning for the patients. Keeping this in view, we propose a telemedicine application that is capable of automatically identifying the severity level of asthma patients by using machine learning techniques. Respiratory sounds of 111 asthmatic patients were collected. The 111-patient dataset consisted of 34 mild, 36 moderate, and 41 severe levels. Data was collected from two auscultation locations, i.e., from the trachea and lower lung base. The first dataset was used for the testing and training (cross-validation) of classifiers while a second database was used for the validation of the system. Mel-frequency cepstral coefficient (MFCC) features were extracted to discriminate the severity levels. Then, ensemble and k-nearest neighbor (KNN) classifiers were used for classification. This was performed on both auscultation locations jointly and individually. The developed telemedicine application, based on MFCC features and classifiers, automatically detects wheeze and classifies it into a severity level. The extracted features showed significant differences (p < 0.05) for all severity levels. Based on the testing, training, and validation results, the performance of the ensemble and KNN classifiers were comparable. MFCC-based features classification provides maximum accuracy of 99%, 90%, and 89% for mild, moderate, and severe samples, respectively. The average rate of wheeze detection was observed to be 93%. The maximum accuracy of validation of the telemedicine application was found to be 57%, 72%, and 76% for mild, moderate, and severe levels, respectively.

Operation monitoring method of pipeline equipment in natural gas compressor station based on spiral microphone array

The abnormal operating conditions of pipeline equipment in natural gas compressor station, such as gas pipeline leakage or equipment failure, lead to negative social impacts. To solve the current problem of untimely and inaccurate manual maintenance, this study proposes a 24/7 real-time method for monitoring the operation of pipeline equipment in natural gas compressor station based on a spiral microphone array. First, the Linearly Constrained Minimum Variance (LCMV) beamformer is used to enhance the signal in the target angle direction and suppress the interference in other directions. Then we train the support vector machine (SVM) model based on the station’s Mel-Frequency Cepstral Coefficients (MFCC) feature dataset for diagnosis. The results show that the method is of great feasibility and reliability.

A deep learning-based approach to diagnose mild traumatic brain injury using audio classification.

Mild traumatic brain injury (mTBI or concussion) is receiving increased attention due to the incidence in contact sports and limitations with subjective (pen and paper) diagnostic approaches. If an mTBI is undiagnosed and the athlete prematurely returns to play, it can result in serious short-term and/or long-term health complications. This demonstrates the importance of providing more reliable mTBI diagnostic tools to mitigate misdiagnosis. Accordingly, there is a need to develop reliable and efficient objective approaches with computationally robust diagnostic methods. Here in this pilot study, we propose the extraction of Mel Frequency Cepstral Coefficient (MFCC) features from audio recordings of speech that were collected from athletes engaging in rugby union who were diagnosed with an mTBI or not. These features were trained on our novel particle swarm optimised (PSO) bidirectional long short-term memory attention (Bi-LSTM-A) deep learning model. Little-to-no overfitting occurred during the training process, indicating strong reliability of the approach regarding the current test dataset classification results and future test data. Sensitivity and specificity to distinguish those with an mTBI were 94.7% and 86.2%, respectively, with an AUROC score of 0.904. This indicates a strong potential for the deep learning approach, with future improvements in classification results relying on more participant data and further innovations to the Bi-LSTM-A model to fully establish this approach as a pragmatic mTBI diagnostic tool.

Multi-Class Cardiac Diagnostic Decision Support System Based On Phonocardiogram Signal

A Phonocardiogram (PCG) signal is the representation of signal like murmurs and sounds which made by the vibrations caused for the period of cardiac cycle . Where the heart beat is recorded using a lowcost small handled digital device called as stethoscope . By using this device it provide information the heart rate , intensity , tone , frequency , quality and the location of the various components of the cardiac sound . Due to these characters , phonocardiogram signals ca detect the heart status at the early state and go for the treatment . Diagnoses at the early stage is the only way to decrease the death rate due to cardiac vascular diseases (CVD) . There are many invasive and non invasive method to diagnoses the cardiac vascular diseases . The facilities are no available in the low and middle income areas where the lack of availability of facilities or lack of money the facilities are no easy available and it case death . In previous studies , it uses convolutional neural network (convnet) , which is trained by hybrid constant – Q transform (HCQT) for heart beat sound classification and most studied architecture . Constant Q – transform (CQT) , variable – Q transform (VQT) and hybrid constant Q-transform which is extracted from phonocardiogram signals as the acoustic features , which includes the domains of Mel Frequency Cepstral Coefficients ( MFCCs ) where audio or speech signal processing . In the proposed system convolutional neural network &amp; CQT, Variable-Q Transform (VQT), and HCQT are extracted from each phonocardiogram signal as the acoustic features, including the dominant MFCC features, feed into five-layer regularized convnet like convolution layer , pooling layer and dense layer. After analyzing the literature in the same domain, it can be stated that this is the first time HCQT is being utilized for PCG signals. HCQT is more effective than standard CQT and other variants. Also, the accuracies of the system proposed in this work on the validation datasets are 94% in multi-class classification, which outperforms the proposed work relative to other models significantly

Chest X ray and cough sample based deep learning framework for accurate diagnosis of COVID-19

All witnessed the terrible effects of the COVID-19 pandemic on the health and work lives of the population across the world. It is hard to diagnose all infected people in real time since the conventional medical diagnosis of COVID-19 patients takes a couple of days for accurate diagnosis results. In this paper, a novel learning framework is proposed for the early diagnosis of COVID-19 patients using hybrid deep fusion learning models. The proposed framework performs early classification of patients based on collected samples of chest X-ray images and Coswara cough (sound) samples of possibly infected people. The captured cough samples are pre-processed using speech signal processing techniques and Mel frequency cepstral coefficient features are extracted using deep convolutional neural networks. Finally, the proposed system fuses extracted features to provide 98.70% and 82.7% based on Chest-X ray images and cough (audio) samples for early diagnosis using the weighted sum-rule fusion method.

Spoken Language Identification System Using Convolutional Recurrent Neural Network

Following recent advancements in deep learning and artificial intelligence, spoken language identification applications are playing an increasingly significant role in our day-to-day lives, especially in the domain of multi-lingual speech recognition. In this article, we propose a spoken language identification system that depends on the sequence of feature vectors. The proposed system uses a hybrid Convolutional Recurrent Neural Network (CRNN), which combines a Convolutional Neural Network (CNN) with a Recurrent Neural Network (RNN) network, for spoken language identification on seven languages, including Arabic, chosen from subsets of the Mozilla Common Voice (MCV) corpus. The proposed system exploits the advantages of both CNN and RNN architectures to construct the CRNN architecture. At the feature extraction stage, it compares the Gammatone Cepstral Coefficient (GTCC) feature and Mel Frequency Cepstral Coefficient (MFCC) feature, as well as a combination of both. Finally, the speech signals were represented as frames and used as the input for the CRNN architecture. After conducting experiments, the results of the proposed system indicate higher performance with combined GTCC and MFCC features compared to GTCC or MFCC features used individually. The average accuracy of the proposed system was 92.81% in the best experiment for spoken language identification. Furthermore, the system can learn language-specific patterns in various filter size representations of speech files.

ERIL: An Algorithm for Emotion Recognition from Indian Languages Using Machine Learning

It is critical for a computer to understand the speaker’s mood during a human–machine conversation. Until now, we’ve only used neutral phrases or utterances to train robots. A person’s mood affects their performance. Machines have a hard time deciphering human mood from voice because humans can make fourteen distinct sounds in a second. For a machine to comprehend human behavior, it must first comprehend the human ear’s acoustic skills. Linear Prediction Coefficients (LPC) and Mel Frequency Cepstral Coefficients (MFCC) can simulate the human auditory system. Emotion Recognition from Indian Languages (ERIL) extracts emotions like fear, anger, surprise, sadness, happiness, and neutral. ERIL first pre-processes the voice signal, extracts selective MFCC, LPC, pitch, and voice quality features, then classifies the speech using Catboost. We tested ERIL on different benchmark classifiers to choose Catboost. ERIL is a multilingual emotion classifier, it is independent of any language. We checked it on Hindi, Gujarati, Marathi, Punjabi, Bangla, Tamil, Oriya, Kannada, Assamese, and Telugu. We recorded a speech dataset of various emotions in these languages. The accuracy of distinct emotions is 95.05 percent on average. The languages have a combined average of 95.05082 percent.

Under-resourced dialect identification in Ao using source information.

This paper reports the findings of an automatic dialect identification (DID) task conducted on Ao speech data using source features. Considering that Ao is a tone language, in this study for DID, the gammatonegram of the linear prediction residual is proposed as a feature. As Ao is an under-resourced language, data augmentation was carried out to increase the size of the speech corpus. The results showed that data augmentation improved DID by 14%. A perception test conducted on Ao speakers showed better DID by the subjects when utterance duration was 3 s. Accordingly, automatic DID was conducted on utterances of various duration. A baseline DID system with the Slms feature attained an average F1-score of 53.84% in a 3 s long utterance. Inclusion of source features, Silpr and S, improved the F1-score to 60.69%. In a final system, with a combination of Silpr, S, Slms, and Mel frequency cepstral coefficient features, the F1-score increased to 61.46%.

Automatic Classification of Bird Sounds: Using MFCC and Mel Spectrogram Features with Deep Learning

Bird species identification is a relevant and time-consuming task for ornithologists and ecologists. With growing amounts of audio-annotated data, automatic bird classification using machine learning techniques is an important trend in the scientific community. Analyzing bird behavior and population trends helps detect other organisms in the environment and is an important problem in ecology. Bird populations react quickly to environmental changes, which make their real-time counting and tracking challenging and very useful. A reliable methodology that automatically identifies bird species from audio would therefore be a valuable tool for the experts in different scientific and applicational domains. The goal of this work is to propose a methodology to identify bird sounds. In this paper, we explore deep learning techniques that are being used in this domain, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to classify the data. In deep learning, audio problems are commonly approached by converting them into images using audio feature extraction techniques such as Mel Spectrograms and Mel Frequency Cepstral Coefficients (MFCCs). We propose and test multiple deep learning and feature extraction combinations in order to find the most suitable approach to this problem.

Automatic Parkinson’s disease detection based on the combination of long-term acoustic features and Mel frequency cepstral coefficients (MFCC)

In recent years, acoustic signals have found increasing popularity among the scientific community as biomarkers for detecting Parkinson’s disease. Literature highlights that 90% of people with Parkinson’s disease have vocal impairment symptoms at earlier stages of the disease. Standard Parkinson’s disease diagnosis depends on medical imaging or observation of motor symptoms such as gait disturbance and muscle rigidity which may appear at later stages. Several acoustic feature extraction algorithms, such as long-term and short-term features, were developed to train machine learning models to detect Parkinson’s disease using a single algorithm. Although the literature demonstrates promising results, there has not been any exploitation of the potential of combining solely long-term and short-term algorithms to the best of the authors’ knowledge. This paper presents a novel method to investigate the effect of a dataset incorporating long-term and short-term features known as Mel frequency cepstral coefficients (MFCC) on the performance of random forest model for Parkinson’s disease detection. The performance of random forest has been compared between three feature sets: MFCC features, long-term features, and a combination of MFCC with long-term features. The combined features improved the detection accuracy to 88.84%, while independent sets of MFCC and long-term features, were 84.12% and 84.00%, respectively. The findings of the proposed method indicates the effectiveness of the long-term features and MFCC combination in improving the overall performance of Parkinson’s disease detection model.

Intelligent estimation of blood glucose level using wristband PPG signal and physiological parameters

The inconvenience and risk associated with the regular use of invasive blood glucose measurements has led to tremendous research in this area. This paper proposes the design of a non-invasive blood glucose estimation system using novel Mel frequency cepstral coefficients features of wristband photoplethysmogram signal and physiological parameters. A dataset from 217 participants of a hospital in Cuenca Ecuador is used to validate the proposed model. The support vector regression (SVR) and extreme gradient boost regression (XGBR) techniques are used to estimate blood glucose levels (BGL). The XGBR technique achieves the least value for the standard error of prediction (SEP), 9.78 mg/dL. Further, 5 features are selected from the feature set based on the feature importance in XGBR. The XGBR model with the reduced feature set results in further reduction of SEP value (5.53 mg/dL) with a correlation coefficient of 0.99. Standard Clarke error grid analysis and Bland-Altman analysis shows that the predicted glucose values are in the clinically acceptable region. The results of the proposed model demonstrate the potential of wearable BGL monitoring technology.

Research on Acoustic Events Recognition Method With Dimensionality Reduction Combining Attention and Mutual Information

The environment sound classification(ESC) is of great significance to the monitoring and control of urban noise. Aiming at the curse of dimensionality phenomenon in ESC, a feature dimensionality reduction architecture combining attention and mutual information is proposed. In order to match the two-dimensional MFCC (Mel Frequency Cepstral Coefficients) feature matrix, the proposed method separates and reconstructs the feature frames of different samples, and achieves the effect of dimensionality reduction by making decisions on the information entropy between the feature frames and labels. In addition, the method combines LSTM (Long Short-Term Memory) model with attention mechanism to ensure the recognition accuracy of the model after dimensionality reduction. Ten urban acoustic events from UrbanSound8k (US8K) dataset are selected to verify the performances of the proposed method by simulation experiments, which are also compared with the existing classification methods. The simulation results show that by combining the attention mechanism and mutual information, the recognition accuracy of the proposed method on the UrbanSound8k dataset is 95.16&#x0025;, and the parameter scale is the smallest, only 0.92M. Moreover, the model parameter scale is adjustable by dynamic frame retention mechanism to balance the recognition accuracy and speed. This method not only ensures a high classification accuracy, but also can reduce computing power consumption and storage space of monitoring equipment, which shows a better practical performance for urban acoustic events recognition.

Klasifikasi Suara Paru-Paru Berdasarkan Ciri MFCC

The lungs are an important organ in the human respiratory system, which functions to exchange carbon dioxide from the blood with oxygen in the air. Detection of respiratory disorders and lung disorders can be done in various ways; view medical records, physical examination, detection by x-ray and also auscultation of breathing. Digital signal processing can be used as a method to detect lung disorders based on the sound produced. In this study, lung sounds were classified into normal, crackle, wheeze, and crackle-wheeze classes using the Mel Frequency Cepstral Coefficient (MFCC) and Convolutional Neural Network (CNN) methods.Observations were made by varying the MFCC feature extraction using MFCC 8 and 13 coefficients, the number of frames are 50 and 60, and the width of the frames used was 0,1, 0,15 and 0,2 seconds. The result of feature extraction is then applied to the CNN classification system, and the confusion matrix is used to get the accuracy and precision values. The highest accuracy and precision values were obtained at 71,85% and 65,70% on the MFCC 13 coefficient with an average of 71,18%. Based on these results, the system that has been created can classify normal lung sounds, crackle, wheeze and crackle-wheeze quite well.

KLASIFIKASI MOOD MUSIK BERDASARKAN MEL FREQUENCY CEPSTRAL COEFFICIENTS DENGAN BACKPROPAGATION NEURAL NETWORK

In music industry, each music is grouped by type, including music genre, artist identification, instrument introduction, and mood. Then came a field of research called Music Information Retrieval (MIR) which is a field of science that retrieves and processes the metadata of music files to perform the grouping. This research is based on the uniqueness of music that has its own mood implied in it. By creating a Machine Learning model using Backpropagation Neural Network (BPNN) based on the Mel Frequency Cepstral Coefficients (MFCC) input feature, it will be able to classify types of music based on mood. Grouping is carried out on four mood classes based on Thayer's model. Based on several previous studies, the use of MFCC in voice processing produces very good accuracy as well as the use of BPNN for classification, which is expected to result in better machine learning model performance. The data used in this study were obtained from the Internet with a total dataset of 200. The results obtained from this study are the classification of music mood using BPNN based on the MFCC feature capable of producing 87.67%. accuracy.

Whispered Speech Detection Using Glottal Flow-Based Features

Recent studies have reported that the performance of Automatic Speech Recognition (ASR) technologies designed for normal speech notably deteriorates when it is evaluated by whispered speech. Therefore, the detection of whispered speech is useful in order to attenuate the mismatch between training and testing situations. This paper proposes two new Glottal Flow (GF)-based features, namely, GF-based Mel-Frequency Cepstral Coefficient (GF-MFCC) as a magnitude-based feature and GF-based relative phase (GF-RP) as a phase-based feature for whispered speech detection. The main contribution of the proposed features is to extract magnitude and phase information obtained by the GF signal. In the GF-MFCC, Mel-frequency cepstral coefficient (MFCC) feature extraction is modified using the estimated GF signal derived from the iterative adaptive inverse filtering as the input to replace the raw speech signal. In a similar way, the GF-RP feature is the modification of the relative phase (RP) feature extraction by using the GF signal instead of the raw speech signal. The whispered speech production provides lower amplitude from the glottal source than normal speech production, thus, the whispered speech via Discrete Fourier Transformation (DFT) provides the lower magnitude and phase information, which make it different from a normal speech. Therefore, it is hypothesized that two types of our proposed features are useful for whispered speech detection. In addition, using the individual GF-MFCC/GF-RP feature, the feature-level and score-level combination are also proposed to further improve the detection performance. The performance of the proposed features and combinations in this study is investigated using the CHAIN corpus. The proposed GF-MFCC outperforms MFCC, while GF-RP has a higher performance than the RP. Further improved results are obtained via the feature-level combination of MFCC and GF-MFCC (MFCC&amp;GF-MFCC)/RP and GF-RP(RP&amp;GF-RP) compared with using either one alone. In addition, the combined score of MFCC&amp;GF-MFCC and RP&amp;GF-RP gives the best frame-level accuracy of 95.01% and the utterance-level accuracy of 100%.

Robust cepstral feature for bird sound classification

Birds are excellent environmental indicators and may indicate sustainability of the ecosystem; birds may be used to provide provisioning, regulating, and supporting services. Therefore, birdlife conservation-related researches always receive centre stage. Due to the airborne nature of birds and the dense nature of the tropical forest, bird identifications through audio may be a better solution than visual identification. The goal of this study is to find the most appropriate cepstral features that can be used to classify bird sounds more accurately. Fifteen (15) endemic Bornean bird sounds have been selected and segmented using an automated energy-based algorithm. Three (3) types of cepstral features are extracted; linear prediction cepstrum coefficients (LPCC), mel frequency cepstral coefficients (MFCC), gammatone frequency cepstral coefficients (GTCC), and used separately for classification purposes using support vector machine (SVM). Through comparison between their prediction results, it has been demonstrated that model utilising GTCC features, with 93.3% accuracy, outperforms models utilising MFCC and LPCC features. This demonstrates the robustness of GTCC for bird sounds classification. The result is significant for the advancement of bird sound classification research, which has been shown to have many applications such as in eco-tourism and wildlife management.

Encrypted Speech Retrieval Scheme Based on Multiuser Searchable Encryption in Cloud Storage

In order to solve the problem of multiuser security sharing and privacy protection of the speech data in cloud storage and realize efficient encrypted speech retrieval, an encrypted speech retrieval scheme based on multiuser searchable encryption was proposed. Firstly, the ciphertext-policy attribute-based encryption (CP-ABE) and searchable encryption (SE) are combined to support a multiuser searchable speech encryption scheme, which achieves the encryption and fine-grained access control of the speech data. Secondly, the Mel frequency cepstral coefficient (MFCC) feature of the original speech that is used as the input of the long- and short-term memory network (LSTM) is extracted to perform the deep semantic feature extraction as the speech keywords. Finally, the speech keywords are encrypted to generate a secure index, bound together with the encrypted speech, and then stored in the cloud. During the user retrieving, the keywords of query speech are extracted by utilizing the trained LSTM to generate the search trapdoor of the user and then uploaded to the cloud server, and the Euclidean distance is used for matching the security index with the search trapdoor. In addition, a proxy server is introduced to execute the partial ciphertext decryption operations to reduce computation overhead and storage space. The theoretical analysis and experimental results show that the proposed scheme has higher security and retrieval accuracy and can realize the secure storage of the massive speech data and multiuser data sharing.

Selection of Audio Learning Resources Based on Big Data

Currently, audio learning resources account for a large proportion of the total online learning resources. Designing and implementing a method for optimizing and selecting audio learning resources based on big data of education will be of great significance to the recommendation of learning resources. Therefore, this paper studies a method for selecting audio learning resources based on the big data of education, with music learning as an example. First, the audio signals were converted into mel spectrograms, and accordingly, the mel-frequency cepstral coefficient features of audio learning resources were obtained. Then, on the basis of the conventional content-based audio recommendation algorithm, the established interest degree vector of target students with respect to music learning was expanded, and a collaborative filtering hybrid algorithm for audio learning resources that incorporates the interest degrees of neighbouring students was proposed, which effectively improved the accuracy and stability in the prediction of students’ interest in music learning. Finally, the experimental results verified the feasibility and prediction accuracy of the proposed algorithm.

Signal recognition method based on Mel frequency cepstral coefficients and fast dynamic time warping for optical fiber perimeter defense systems.

To improve the accuracy of signal recognition in optical fiber perimeter defense systems, a method based on Mel frequency cepstral coefficients (MFCCs) and a fast dynamic time warping (FastDTW) algorithm is proposed. Four kinds of sensing signals are acquired by employing an in-line Sagnac interferometer system. MFCC features of the signals are extracted. The FastDTW algorithm is utilized to calculate distances of the optimal warping paths between a test sample and all reference templates. According to the nearest neighbor criterion, signal recognition results are obtained. The average accuracy is over 96%. This proposed identification method is highly efficient and easy to implement.

Voice Analysis and Classification System Based on Perturbation Parameters and Cepstral Presentation in Psychoacoustic Scales

The paper describes an approach to design a system for analyzing and classification of a voice signal based on perturbation parameters and cepstral representation. Two variants of the cepstral representation of the voice signal are considered: based on mel-frequency cepstral coefficients (MFCC) and based on bark-frequency cepstral coefficients (BFCC). The work used a generally accepted approach to calculating the MFCC based on the time-frequency analysis by the method of discrete Fourier transform (DFT) with summation of energy in subbands. This method approximates the frequency resolution of human hearing, but has a fixed temporal resolution. As an alternative, a variant of the cepstral representation based on the BFCC has been proposed. When calculating the BFCC, a warped DFT-modulated filter bank was used, which approximates the frequency and temporal resolution of hearing. The aim of the work was to compare the effectiveness of the use of features based on the MFCC and BFCC for the designing systems for the analysis and classification of the voice signal. The results of the experiment showed that in the case when using acoustic features based on the MFCC, it is possible to obtain a voice classification system with an average recall of 80.6 %, and in the case when using features based on the BFCC, this metric is 83.7 %. With the addition of the set of MFCC features with perturbation parameters of the voice, the average recall of the classification increased to 94.1 %, with a similar addition to the set of BFCC features, the average recall of the classification increased up to 96.7 %.