Combination Of Mel Frequency Cepstral Coefficients Research Articles

Stridulatory Organs and Sound Recognition of Three Species of Longhorn Beetles (Coleoptera: Cerambycidae).

Sound is an important medium of communication among insects. Some longhorn beetles produce sounds during their daily activities, and these sounds play a role in courtship, predation, and defense. However, whether there are differences in the sounds emitted by longhorn beetles and how to distinguish and recognize these sounds have not been investigated in detail. Here, the sounds of Glenea cantor (Fabricius), Moechotypa diphysis (Pascoe), and Psacothea hilaris (Pascoe) were collected, and the differences in their stridulatory organs were observed and compared using scanning electron microscopy (SEM). The characteristics of their sounds were analyzed using MATLAB. Linear prediction cepstral coefficients (LPCC) and Mel frequency cepstral coefficients (MFCC) were used to extract the sound features, and the support vector machine (SVM) model was used to identify the sounds of three species. The results showed that the stridulatory organs of three species of longhorn beetles differed in morphology and time domain, and the combination of MFCC and SVM had a better recognition ability. The difference in the stridulatory organs of longhorn beetles may be an important reason for the differences in the sounds they produce, and we discussed the application of insect sounds in insect classification.

A convolutional neural network with image and numerical data to improve farming of edible crickets as a source of food-A decision support system.

Crickets (Gryllus bimaculatus) produce sounds as a natural means to communicate and convey various behaviors and activities, including mating, feeding, aggression, distress, and more. These vocalizations are intricately linked to prevailing environmental conditions such as temperature and humidity. By accurately monitoring, identifying, and appropriately addressing these behaviors and activities, the farming and production of crickets can be enhanced. This research implemented a decision support system that leverages machine learning (ML) algorithms to decode and classify cricket songs, along with their associated key weather variables (temperature and humidity). Videos capturing cricket behavior and weather variables were recorded. From these videos, sound signals were extracted and classified such as calling, aggression, and courtship. Numerical and image features were extracted from the sound signals and combined with the weather variables. The extracted numerical features, i.e., Mel-Frequency Cepstral Coefficients (MFCC), Linear Frequency Cepstral Coefficients, and chroma, were used to train shallow (support vector machine, k-nearest neighbors, and random forest (RF)) ML algorithms. While image features, i.e., spectrograms, were used to train different state-of-the-art deep ML models, i,e., convolutional neural network architectures (ResNet152V2, VGG16, and EfficientNetB4). In the deep ML category, ResNet152V2 had the best accuracy of 99.42%. The RF algorithm had the best accuracy of 95.63% in the shallow ML category when trained with a combination of MFCC+chroma and after feature selection. In descending order of importance, the top 6 ranked features in the RF algorithm were, namely humidity, temperature, C#, mfcc11, mfcc10, and D. From the selected features, it is notable that temperature and humidity are necessary for growth and metabolic activities in insects. Moreover, the songs produced by certain cricket species naturally align to musical tones such as C# and D as ranked by the algorithm. Using this knowledge, a decision support system was built to guide farmers about the optimal temperature and humidity ranges and interpret the songs (calling, aggression, and courtship) in relation to weather variables. With this information, farmers can put in place suitable measures such as temperature regulation, humidity control, addressing aggressors, and other relevant interventions to minimize or eliminate losses and enhance cricket production.

Prestressing wire breakage monitoring using sound event detection

AbstractDetecting prestressed wire breakage in concrete bridges is essential for ensuring safety and longevity and preventing catastrophic failures. This study proposes a novel approach for wire breakage detection using Mel‐frequency cepstral coefficients (MFCCs) and back‐propagation neural network (BPNN). Experimental data from two bridges in Italy were acquired to train and test the models. To overcome the limited availability of real‐world training data, data augmentation techniques were employed to increase the data set size, enhancing the capability of the models and preventing over‐fitting problems. The proposed method uses MFCCs to extract features from acoustic emission signals produced by wire breakage, which are then classified by the BPNN. The results show that the proposed method can detect and classify sound events effectively, demonstrating the promising potential of BPNN for real‐time monitoring and diagnosis of bridges. The significance of this work lies in its contribution to improving bridge safety and preventing catastrophic failures. The combination of MFCCs and BPNN offers a new approach to wire breakage detection, while the use of real‐world data and data augmentation techniques are significant contributions to overcoming the limited availability of training data. The proposed method has the potential to be a generalized and robust model for real‐time monitoring of bridges, ultimately leading to safer and longer‐lasting infrastructure.

Central Kurdish Automatic Speech Recognition using Deep Learning

Automatic Speech Recognition (ASR) as an interesting field of speech processing, is nowadays utilized in real applications which are implemented using various techniques. Amongst them, the artificial neural network is the most popular one. Increasing the performance and making these systems robust to noise are among the current challenges. This paper addresses the development of an ASR system for the Central Kurdish language (CKB) using a transfer learning of Deep Neural Networks (DNN). The combination of Mel-Frequency Cepstral Coefficients (MFCCs) for extracting features of speech signals, Long Short-Term Memory (LSTM) with Connectionist Temporal Classification (CTC) output layer is used to create an Acoustic Model (AM) on the AsoSoft CKB speech dataset. Also, we have used the N-gram language model on the collected large text dataset which includes about 300 million tokens. The text corpus is also used to extract a dynamic lexicon model that contains over 2.5 million CKB words. The obtained results show that the use of a DNN improves the results compared to classical statistics modules. The proposed method achieves a 0.22%-word error rate by combining transfer learning and language model adaptation. This result is superior to the best-reported result for the CKB.

IMPROVED NOISE-RESILIENT ISOLATED WORDS SPEECH RECOGNITION USING PIECEWISE DIFFERENTIATION

Speech is the primary method of communication among humans; it is the main form of communication to deliver emotions and thoughts. Intuitively, humans would find it convenient to communicate with machines through speech. The automatic speech recognition model’s primary goal is to transcribe or identify the word sequence represented by the acoustic signals or speech. In this advanced era of digital media and e-learning platforms, the effective use of voice recognition technology to deliver a more individualized and effective educational experience in e-learning initiatives. It helps the students to improve their oral pronunciation skills. Even though Arabic is one of the most spoken languages, the research works related to Arabic speech and text are lacking when compared to other languages. Here, we propose a hybrid model of [Formula: see text]-Nearest Neighbor (KNN) classifier and Dynamic Time Warping (DTW) for implementing a noise-resilient speech recognition system for isolated words in the Arabic language. The model is implemented with Mel-Frequency Cepstral Coefficients (MFCC) and its piecewise first and second derivatives as feature representation models. The proposed model is implemented in Python and simulated using Arabic Speech Corpus for Isolated Words [A. Alalshekmubarak and L. Smith, On improving the classification capability of reservoir computing for Arabic speech recognition, in International Conference on Artificial Neural Networks (Springer, Cham, 2014), pp. 225–232]. The proposed model is implemented with (1) MFCC alone for feature representation, (2) combination of MFCC with Delta coefficients, and (3) combination of MFCC with Delta and Delta–Delta coefficients. The implemented model is evaluated using different test sets of varying sizes of 100, 200, 500, 1000, 1500, and 2000. Evaluation is performed for both noised and noiseless speech using these three feature representation models and performed an evaluation of these three models. For evaluating this model’s performance in noised conditions, both white and babble noises of various signal-to-noise ratio values such as 10 dB, 20 dB, and 30 dB are added into the noiseless speech and estimate the classification accuracy in those situations. The proposed KNN–DTW model with MFCC outperformed all the other models in the literature. MFCC with Delta and Delta–Delta coefficients is effective than other two models using MFCC and MFCC with Delta coefficients. The proposed model could be used to recognize the isolated word recognition of grade 1 textbook vocabulary in classroom noisy environment for providing a better interactive classroom environment to Kuwaiti elementary students to enhance their math learning level. In an interactive computer-based education environment for Mathematics learning, the proposed model could be recognized as the spoken utterances of students.

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.

Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal.

The aim of the study was to inspect the acoustic properties and sleep characteristics of a preapneic snoring sound. The feasibility of forecasting upcoming respiratory events by snoring sound was also investigated. Participants with habitual snoring or a heavy breathing sound during sleep were recruited consecutively. Polysomnography was conducted, and snoring-related breathing sound was recorded simultaneously. Acoustic features and sleep features were extracted from 30-second samples, and a machine learning algorithm was used to establish 2 prediction models. A total of 74 eligible participants were included. Model 1, tested by 5-fold cross-validation, achieved an accuracy of 0.92 and an area under the curve of 0.94 for respiratory event prediction. Model 2, with acoustic features and sleep information tested by Leave-One-Out cross-validation, had an accuracy of 0.78 and an area under the curve of 0.80. Sleep position was found to be the most important among all sleep features contributing to the performance of the 2 models. Preapneic sound presented unique acoustic characteristics, and snoring-related breathing sound could be deployed as a real-time apneic event predictor. The models, combined with sleep information, serve as a promising tool for an early warning system to forecast apneic events. Wang B, Yi X, Gao J, etal. Real-time prediction of upcoming respiratory events via machine learning using snoring sound signal. J Clin Sleep Med. 2021;17(9):1777-1784.

Indonesian Speech Emotion Recognition using Cross-Corpus Method with the Combination of MFCC and Teager Energy Features

Emotion recognition is one of the widely studied topics in speech technology. Emotions that come from speech can contain useful information for many purposes. The main aspects in speech emotion recognition are speech features, speech corpus, and machine learning algorithms as the classifier method. In this paper, cross-corpus method is used to conduct Indonesian Speech Emotion Recognition (SER) along with the combination of Mel Frequency Cepstral Coefficients (MFCC) and Teager Energy features. Using Support Vector Machine (SVM) as classifier, the experiment result shows that applying cross-corpus method by adding corpora from other languages to the training dataset improves the emotion classification accuracy by 4.16% on MFCC Statistics feature and 2.09% on Teager-MFCC Statistics feature.

Speech Recognition System for Isolated Tamil Words using Random Forest Algorithm

ASR is the use of system software and hardware based techniques to identify and process human voice. In this research, Tamil words are analyzed, segmented as syllables, followed by feature extraction and recognition. Syllables are segmented using short term energy and segmentation is done in order to minimize the corpus size. The algorithm for syllable segmentation works by performing the STE function of the continuous speech signal. The proposed approach for speech recognition uses the combination of Mel-Frequency Cepstral Coefficients (MFCC) and Linear Predictive Coding (LPC). MFCC features are used to extract a feature vector containing all information about the linguistic message. The LPC affords a robust, dependable and correct technique for estimating the parameters that signify the vocal tract system.LPC features can reduce the bit rate of speech (i.e reducing the measurement of transmitting signal).The combined feature extraction technique will minimize the size of transmitting signal. Then the proposed FE algorithm is evaluated on the speech corpus using the Random forest approach. Random forest is an effective algorithm which can build a reliable training model as its training time is less because the classifier works on the subset of features alone.

On the use of long-term features in a newborn cry diagnostic system

This study proposes using a novel combination of short-term and long-term features from different timescales to develop an automatic newborn cry diagnostic system to differentiate the cry audio signals (CASs) of healthy infants from those with respiratory distress syndrome (RDS). Mel-frequency cepstral coefficients (MFCCs) were used as the short-term features, while the melody and rhythm features obtained from longer timescales were used as the long-term features. We hypothesized that the differences between these groups may occur on several timescales. Finally, a support vector machine model was used to generate the final classification. Among other findings, the best results were obtained from the combination of all three feature sets (the MFCCs and the rhythm and melody features) in the expiration episode; the combination of MFCCs and tilt features improved the classifier performance in the inspiration episode. In terms of F-score measure, in the inspiration experiment, the tilt features alone were the strongest classification features for differentiating infants with RDS from healthy infants. The results indicate that the combination of short-term and long-term features provide a better classification method for differentiating the CASs of healthy infants versus RDS infants. Moreover, the results confirmed the importance of long-term features in expiration and inspiration episodes as diagnostic markers between groups of healthy infants and RDS infants.

A Robust Feature Extraction Method for Real-Time Speech Recognition System on a Raspberry Pi 3 Board

The development of a real-time automatic speech recognition system (ASR) better adapted to environmental variabilities, such as noisy surroundings, speaker variations and accents has become a high priority. Robustness is required, and it can be performed at the feature extraction stage which avoids the need for other pre-processing steps. In this paper, a new robust feature extraction method for real-time ASR system is presented. A combination of Mel-frequency cepstral coefficients (MFCC) and discrete wavelet transform (DWT) is proposed. This hybrid system can conserve more extracted speech features which tend to be invariant to noise. The main idea is to extract MFCC features by denoising the obtained coefficients in the wavelet domain by using a median filter (MF). The proposed system has been implemented on Raspberry Pi 3 which is a suitable platform for real-time requirements. The experiments showed a high recognition rate (100%) in clean environment and satisfying results (ranging from 80% to 100%) in noisy environments at different signal to noise ratios (SNRs).

Efficient FPGA-based architecture of an automatic wheeze detector using a combination of MFCC and SVM algorithms

This paper proposes a new hardware implementation of an automatic wheeze detector in pulmonary sounds. The proposed system is based on the combination of Mel-frequency cepstral coefficients (MFCC) feature extraction method and a support vector machine (SVM) classifier. It has been implemented on a field programmable gate array (FPGA) chip using the Xilinx System Generator (XSG) programming tool and the Nexys-4 development board, which is built around the low-cost Artix-7 FPGA device. The LIBSVM library has been used to extract the SVM parameters during the training phase in the Matlab environment, then the MFCC feature extraction and the SVM testing phase are performed on the FPGA chip. Two hardware architectures (default and optimized) of the SVM classifier are implemented and compared in terms of resource utilization, maximum operating frequency, and power consumption for the Artix-7 XC7A100T FPGA Chip. The detection rates obtained by the fixed-point XSG implementation are presented and compared to those obtained by the floating-point Matlab simulation. It has been also compared to existing methods in terms of the detection rates and the characteristics of its implementation.

Effect Of Combination Of Different Features On Speech Recognition For Abnormal Speech

Speech is very important for communication. With the help of speech one can express their thoughts, interact with people easily. Feature Extraction is the very first step in speech recognition. This paper presents the effect of combination of different features on speech recognition. Different features of speech such as spectral, temporal, perceptual and energy. Also accuracy for each feature is calculated and it seems that Mel Frequency Cepstral Coefficients (MFCC) gives better accuracy among all the features. And the accuracy of MFCC is further increased by combination of MFCC with bark.

Non‐intrusive speech quality assessment using multi‐resolution auditory model features for degraded narrowband speech

A multi-resolution framework using auditory perception-based wavelet packet transform is invoked in multi-resolution auditory model (MRAM) and used for non-intrusive objective speech quality estimation. The MRAM provides a detailed time-frequency modelling of the human auditory system compared to earlier models that have been used for non-intrusive speech quality estimation. The objective Mean Opinion Score (MOS) of a degraded narrowband speech utterance has been estimated by Gaussian Mixture Model (GMM) probabilistic approach using MRAM-based feature vector. Additionally, a recent auditory model (Lyons’ auditory model) based features, mel-frequency cepstral coefficients (MFCC), and line spectral frequencies (LSF) features have also been used independently for comparison of the performance of MRAM features. The combination of MFCC and LSF features with MRAM features for non-intrusive speech quality estimation using GMM probabilistic approach has been proposed and investigated. The performance of these feature vectors has been evaluated and compared with ITU-T Recommendation P.563 and a recent published work by computing correlation coefficient and root-mean-square error between the subjective MOS and the estimated objective MOS. It is found that the proposed method that uses a combination of MRAM features, MFCC, and LSF feature vectors for non-intrusive speech quality performs better than both the other algorithms.

On combining information from modulation spectra and mel-frequency cepstral coefficients for automatic detection of pathological voices

This work presents a novel approach for the automatic detection of pathological voices based on fusing the information extracted by means of mel-frequency cepstral coefficients (MFCC) and features derived from the modulation spectra (MS). The system proposed uses a two-stepped classification scheme. First, the MFCC and MS features were used to feed two different and independent classifiers; and then the outputs of each classifier were used in a second classification stage. In order to establish the best configuration which provides the highest accuracy in the detection, the fusion of information was carried out employing different classifier combination strategies. The experiments were carried out using two different databases: the one developed by The Massachusetts Eye and Ear Infirmary Voice Laboratory, and a database recorded by the Universidad Politécnica de Madrid. The results show that the combination of MFCC and MS features employing the proposed approach yields an improvement in the detection accuracy, demonstrating that both methods of parameterization are complementary.

Speaker recognition under stressed condition

This paper presents the feature analysis and design of compensators for speaker recognition under stressed speech conditions. Any condition that causes a speaker to vary his or her speech production from normal or neutral condition is called stressed speech condition. Stressed speech is induced by emotion, high workload, sleep deprivation, frustration and environmental noise. In stressed condition, the characteristics of speech signal are different from that of normal or neutral condition. Due to changes in speech signal characteristics, performance of the speaker recognition system may degrade under stressed speech conditions. Firstly, six speech features (mel-frequency cepstral coefficients (MFCC), linear prediction (LP) coefficients, linear prediction cepstral coefficients (LPCC), reflection coefficients (RC), arc-sin reflection coefficients (ARC) and log-area ratios (LAR)), which are widely used for speaker recognition, are analyzed for evaluation of their characteristics under stressed condition. Secondly, Vector Quantization (VQ) classifier and Gaussian Mixture Model (GMM) are used to evaluate speaker recognition results with different speech features. This analysis help select the best feature set for speaker recognition under stressed condition. Finally, four VQ based novel compensation techniques are proposed and evaluated for improvement of speaker recognition under stressed condition. The compensation techniques are speaker and stressed information based compensation (SSIC), compensation by removal of stressed vectors (CRSV), cepstral mean normalization (CMN) and combination of MFCC and sinusoidal amplitude (CMSA) features. Speech data from SUSAS database corresponding to four different stressed conditions, Angry, Lombard, Question and Neutral, are used for analysis of speaker recognition under stressed condition.