Mel Frequency Cepstrum Research Articles

Recognition of behavior state of Penaeus vannamei based on passive acoustic technology

Underwater signal processing based on passive acoustic technology has carried out a lot of research on the behavioral sound of underwater creatures and the protection of marine resources, which proves the feasibility of passive acoustic technology for long-term and non-destructive monitoring of underwater biological sound production. However, at present, most relevant research focuses on fish but little research on shrimp. At the same time, as the main economic breeding industry, Penaeus vannamei has a backward industrial structure, in which the level of intelligence needs to be improved. In this paper, the acoustic signals generated by different physiological behaviors of P. vannamei are collected based on passive acoustic technology. Their different behaviors are finally classified and identified through feature extraction and analysis. Meanwhile, the characteristic non-parametric ANOVA is carried out to explore the relationship between the acoustic signals and the behavior state of P. vannamei to achieve the purpose of real-time monitoring of the behavior state of P. vannamei. The experimental results show that linear prediction cepstrum coefficient (LPCC) and Mel-frequency cepstrum coefficient (MFCC) characteristic coefficients are effective in the classification and recognition of different behavioral acoustic signals with interspecific acoustic signals of P. vannamei. Meanwhile, the SVM classifier based on OvR classification strategy can model the acoustic signal characteristics of different underwater biological behaviors more efficiently and has classification accuracy as high as 93%.

Phoneme analysis for multiple languages with fuzzy‐based speaker identification

Most voice biometric systems are dependent on the language of the user. However, if the idea is to create an all-inclusive and reliable system that uses speech as its input, then they should be able to recognise people regardless of language or accent. Thus, this paper investigates the effects of languages on speaker identification systems and the phonetic impact on their performance. The experiments are performed using three widely spoken languages which are Portuguese, English, and Chinese. The Mel-Frequency Cepstrum Coefficients and its Deltas are extracted from those languages. Also, this paper expands the research study of fuzzy models in the speaker recognition field, using a Fuzzy C-Means and Fuzzy k-Nearest Neighbours and comparing them with k-Nearest Neighbours and Support Vector Machines. Results with more languages decreases the accuracy from 92% to 85.59%, but further investigation suggests it is caused by the number of classes. A phonetic investigation finds no relation between the phonemes and the results. Finally, fuzzy methods offer more flexibility and in some cases, even better results compared to their crisp version. Therefore, the biometric system presented here is not affected by multilingual environments.

Recognition of Carbon Content of Pr-Nd Alloys Based on Mel-Frequency Cepstrum Coefficient of Force Signals

Recognition of Carbon Content of Pr-Nd Alloys Based on Mel-Frequency Cepstrum Coefficient of Force Signals

Application of a Deep Learning Neural Network for Voiding Dysfunction Diagnosis Using a Vibration Sensor

In a clinical context, there are increasing numbers of people with voiding dysfunction. To date, the methods of monitoring the voiding status of patients have included voiding diary records at home or urodynamic examinations at hospitals. The former is less objective and often contains missing data, while the latter lacks frequent measurements and is an invasive procedure. In light of these shortcomings, this study developed an innovative and contact-free technique that assists in clinical voiding dysfunction monitoring and diagnosis. Vibration signals during urination were first detected using an accelerometer and then converted into the mel-frequency cepstrum coefficient (MFCC). Lastly, an artificial intelligence model combined with uniform manifold approximation and projection (UMAP) dimensionality reduction was used to analyze and predict six common patterns of uroflowmetry to assist in diagnosing voiding dysfunction. The model was applied to the voiding database, which included data from 76 males aged 30 to 80 who required uroflowmetry for voiding symptoms. The resulting system accuracy (precision, recall, and f1-score) was around 98% for both the weighted average and macro average. This low-cost system is suitable for at-home urinary monitoring and facilitates the long-term uroflow monitoring of patients outside hospital checkups. From a disease treatment and monitoring perspective, this article also reviews other studies and applications of artificial intelligence-based methods for voiding dysfunction monitoring, thus providing helpful diagnostic information for physicians.

Metaverse Space Ecological Scene Design Based on Multimedia Digital Technology

Yuan cosmos is a virtual world linked and created by scientific and technological means, which is mapped and interacted with the real world, and a digital living space with a new social system. With the increasing popularity of data acquisition and production equipment, people are increasingly convenient to produce multimedia data such as images, graphics, audio, video, animation, and three-dimensional models. In addition to the rapid development of digital technology itself, the biological information technology related to digital technology also greatly promotes the emergence of the metauniverse. This paper aims to study the application of multimedia digital technology to the ecological scene design of metauniverse space, introduces the related concepts of metauniverse and multimedia digital technology, expounds the related methods of multimedia digital technology and neural network related algorithms, and then takes the three-dimensional simulation of the auditory system in the interactive multisensory simulation system of the constituent elements of metauniverse as an example. The mel-frequency cepstrum coefficient (MFCC) is used to simulate the auditory characteristics of the auditory periphery (cochlea) as the perceptual end of the model. A variety of bionic mechanisms are used in the model, such as designing the connection mode of neurons, learning state and release effect, and the regeneration mechanism of neurons. For the verification of the performance of the model, the speech sample database, including English words and phrases, is recorded and the speech content information recognized by the model by means of speech recognition is experimented. The experimental results show that, in terms of phrase accuracy, the DN-1 model improves 2.59% and the DN-2 model improves 2.77% compared with MFCC feature on the basis of mixed features. When only DBN features are used, the performance improvement rate of the developmental network model is small.

An event recognition method based on MFCC, superposition algorithm and deep learning for buried distributed optical fiber sensors

An event recognition method based on Mel-frequency cepstrum coefficient (MFCC), superposition algorithm and deep learning is proposed for Φ-OTDR event classification with buried fiber. The MFCC data matrix and temporal–spatial data matrix of intrusion events are superimposed and input into the convolutional neural network (CNN) to determine the event types. Six presentation formats of data samples are selected for comparison. Experimental results based on 8185 samples of 8 event classification show that the proposed method can illustrate the event feature better and achieves the highest classification accuracy of 99.55% and 97.95%, respectively, on two networks with different depths. The accuracies are 0.69% and 1.30% higher than those of the experiment using MFCC as event feature alone.

A novel optical sensor system for the automatic classification of mosquitoes by genus and sex with high levels of accuracy

BackgroundEvery year, more than 700,000 people die from vector-borne diseases, mainly transmitted by mosquitoes. Vector surveillance plays a major role in the control of these diseases and requires accurate and rapid taxonomical identification. New approaches to mosquito surveillance include the use of acoustic and optical sensors in combination with machine learning techniques to provide an automatic classification of mosquitoes based on their flight characteristics, including wingbeat frequency. The development and application of these methods could enable the remote monitoring of mosquito populations in the field, which could lead to significant improvements in vector surveillance.MethodsA novel optical sensor prototype coupled to a commercial mosquito trap was tested in laboratory conditions for the automatic classification of mosquitoes by genus and sex. Recordings of > 4300 laboratory-reared mosquitoes of Aedes and Culex genera were made using the sensor. The chosen genera include mosquito species that have a major impact on public health in many parts of the world. Five features were extracted from each recording to form balanced datasets and used for the training and evaluation of five different machine learning algorithms to achieve the best model for mosquito classification.ResultsThe best accuracy results achieved using machine learning were: 94.2% for genus classification, 99.4% for sex classification of Aedes, and 100% for sex classification of Culex. The best algorithms and features were deep neural network with spectrogram for genus classification and gradient boosting with Mel Frequency Cepstrum Coefficients among others for sex classification of either genus.ConclusionsTo our knowledge, this is the first time that a sensor coupled to a standard mosquito suction trap has provided automatic classification of mosquito genus and sex with high accuracy using a large number of unique samples with class balance. This system represents an improvement of the state of the art in mosquito surveillance and encourages future use of the sensor for remote, real-time characterization of mosquito populations.Graphical abstract

Interference Quality Assessment of Speech Communication Based on Deep Learning

In this article, interferencequality assessment is of great significance to reflect the communication environment and improve speech communication performance. However, most traditional assessment approaches aimed at the degraded speech produced in the communication telephone network, but lacking of methods in extreme communication environment with ultralow SNR. Therefore, in this article, we proposed a convolutional neural network (CNN) model evaluation method based on Log-Mel spectrogram to evaluate interfered speech quality. In this method, the Mel frequency cepstrum coefficients of interference speech are converted into images, which are used as input of CNN. In order to verify the performance of this method, we collected a speech dataset in real interfered communication scenarios and finished manual annotation. Experiments are carried out on this dataset to evaluate the interference speech, and the performance of this method is compared with that of machine learning evaluation method under different features. Experimental results show that the proposed method gives the better evaluation accuracy. Compared with the previous machine learning methods, the accuracy is improved by 12.5% from 75% to 87.5%.

Protocol for Rhapsody: a longitudinal observational study examining the feasibility of speech phenotyping for remote assessment of neurodegenerative and psychiatric disorders

IntroductionNeurodegenerative and psychiatric disorders (NPDs) confer a huge health burden, which is set to increase as populations age. New, remotely delivered diagnostic assessments that can detect early stage NPDs by...

Research on Audio Recognition Based on the Deep Neural Network in Music Teaching.

Solfeggio is an important basic course for music majors, and audio recognition training is one of the important links. With the improvement of computer performance, audio recognition has been widely used in smart wearable devices. In recent years, the development of deep learning has accelerated the research process of audio recognition. However, there is a lot of sound interference in music teaching environment, which leads to the performance of the audio classifier that cannot meet the actual demand. In order to solve this problem, an improved audio recognition system based on YOLO-v4 is proposed, which mainly improves the network structure. First, Mel frequency cepstrum number is used to process the original audio and extract the corresponding features. Then, try to apply the YOLO-v4 model in the field of deep learning to the field of audio recognition and improve it by combining with the spatial pyramid pool module to strengthen the generalization ability of data in different audio formats. Second, the stacking method in ensemble learning is used to fuse the independent submodels of two different channels. Experimental results show that compared with other deep learning technologies, the improved YOLO-v4 model can improve the performance of audio recognition, and it has better performance in processing data of different audio formats, which shows better generalization ability.

Audio Signal Acquisition and Processing System Based on Model DSP Rapid Design

When using DSP technology, technicians can easily and conveniently replace DSP audio processors, make second-party equipment, improve processor performance, reduce application costs, and receive and make music to meet the needs of different signals. Introduces knowledge of speech comprehension, which includes theoretical and cognitive processes, such as pre-speech characterization, final discovery, behavior, structure, and knowledge. By analyzing and comparing various common features, Mel frequency cepstrum coefficient is used to determine the physical features, and the results of traditional algorithms are studied, compared, and evaluated. Research, compare, and evaluate numbers in traditional algorithms. The simulation is based on the algorithms of all the connections used in the system and checks that the algorithm is correct. Design an experimental model, compare the advantages and disadvantages of algorithms and algorithms, consider the process requirements and changes of the whole algorithm, and select the algorithm accordingly algorithm. The experimental results show that the speed recognition of the TW algorithm is not much different from that of the HMM algorithm, and the efficiency of the DTW algorithm in the training models is lower than that of the HMM algorithm. Given the limited resources of the DSP platform in this system, we have chosen the DTW algorithm as the self-reporting system for this system. Finally, the performance of the system is checked and the test results are released. Observations of the experimental instrument showed that the system was effective in being able to recognize small words and discrepancies in speech.

Intelligent Aging Home Control Method and System for Internet of Things Emotion Recognition.

To solve a series of pension problems caused by aging, based on the emotional recognition of the Internet of Things, the control method and system research of smart homes are proposed. This article makes a detailed analysis and research on the necessity, feasibility, and how to realize speech emotion recognition technology in smart families, introduces the definition and classification of emotion, and puts forward five main emotions to be recognized in speech emotion recognition based on smart family environment. Then, based on this, it analyses the acquisition methods of emotional speech data. On this premise, this article discusses and analyses the related problems of voice data acquisition in smart homes, such as the voice characteristics and acquisition methods, puts forward three rules for voice text design, and determines the relatively suitable hybrid recording acquisition method applied in a smart home environment. At the same time, the design and establishment process of intelligent family emotional speech database is described in detail. The related problems of feature extraction in speech emotion recognition are studied. Starting from the definition of feature extraction, this article expounds on the necessity of feature extraction in the process of recognition and analyses the characteristics of the speech signals. For the specific environment of the smart family, the speech signal required to be processed needs to be close to the auditory characteristics of the human ears, and the speech signal contains enough information. Finally, the Mel frequency cepstrum coefficient (MFCC) is selected as the feature parameter applied in this article, and the extraction process of MFCC is introduced in detail.

An Urdu speech corpus for emotion recognition.

Emotion recognition from acoustic signals plays a vital role in the field of audio and speech processing. Speech interfaces offer humans an informal and comfortable means to communicate with machines. Emotion recognition from speech signals has a variety of applications in the area of human computer interaction (HCI) and human behavior analysis. In this work, we develop the first emotional speech database of the Urdu language. We also develop the system to classify five different emotions: sadness, happiness, neutral, disgust, and anger using different machine learning algorithms. The Mel Frequency Cepstrum Coefficient (MFCC), Linear Prediction Coefficient (LPC), energy, spectral flux, spectral centroid, spectral roll-off, and zero-crossing were used as speech descriptors. The classification tests were performed on the emotional speech corpus collected from 20 different subjects. To evaluate the quality of speech emotions, subjective listing tests were conducted. The recognition of correctly classified emotions in the complete Urdu emotional speech corpus was 66.5% with K-nearest neighbors. It was found that the disgust emotion has a lower recognition rate as compared to the other emotions. Removing the disgust emotion significantly improves the performance of the classifier to 76.5%.

Speaker Identification Based on Physical Variation of Speech Signal

Speaker identification for the speech signal processing request, determining the speaker is a challenge due to physical variation. This paper emphasizes a new algorithm based on acoustic feature analysis of text-dependent speech. In this proposed method text-dependent speech changed by ten physical variation methods. Acoustic feature of all types of voice is calculated by its arithmetical correlation coefficients and mean value. The audio characteristic is calculated with Mel-frequency cepstrum coefficient (MFCC), its derivatives and double derivatives. An acoustic characteristic is analysed by using normal voice and changed voice by different speakers, the mixed data used for test and training purpose. Passing all the training and test data through the various classifiers based on identification system. Speaker identification efficiency results are calculated from the different classifier.

English Speech Recognition System Model Based on Computer-Aided Function and Neural Network Algorithm.

With the economic globalization continuous growth of China's socioeconomic level tends to be internationalized, China's attention to English has been significantly improved. However, the domestic English teaching level is limited, so it is impossible to correct students' English pronunciation and make a reasonable evaluation at all times so that oral training has certain disadvantages. However, the computer-aided language learning system at home and abroad focuses on the practice of words and grammar, and the evaluation indicators are less and not comprehensive. In view of the complexity of English pronunciation changes, traditional speech recognition is difficult to recognize speech speed and improve its accuracy. Furthermore, to strengthen the English pronunciation of domestic students, a nonlinear network structure is studied in depth to simulate the human brain to analyze a model of speech recognition is established Mel frequency cepstrum characteristic parameters of human ear model and deep belief network. In this paper, the traditional computer pronunciation evaluation method is improved in an all-round way, and a set of high-quality speech recognition system of speech recognition method is constructed. Aiming at the above problems, it takes the students as the research, which proves that the method adopted in this paper can give the learners accurate pronunciation quality analysis report and guidance and correct their intonation and improve the learning effect, and the experimental data verify that the improved speech recognition system model recognition ability is higher than the traditional model.

Music Feature Extraction Method Based on Internet of Things Technology and Its Application.

Due to the influence of factors such as strong music specialization, complex music theory knowledge, and various variations, it is difficult to identify music features. We have developed a music characteristic identification system using the Internet-based method. The physical sensing layer of our designed system deploys audio sensors on various coordinates to capture the raw audio signal and performs audio signal processing and analysis using the TMS320VC5402 digital signal processor; the Internet transport layer places audio sensors at various locations to capture the raw audio signal. The TMS320VC5402 digital signal processor is used for audio signal diagnosis and treatment. The network transport layer transmits the finished audio signal to the data base of song signal in the application layer of the system; the song characteristic analysis block in the application layer adopts dynamics. The music characteristic analysis block in the applied layer adopts dynamic time warping algorithm to acquire the maximal resemblance between the test template and the reference template to achieve music signal characteristic identification and identify music tunes and music modes based on the identification results. The application layer music feature analysis block adopts dynamic time regularization algorithm and mel-frequency cepstrum coefficient to achieve music signal feature recognition and identify music tunes and music patterns based on the recognition results. We have verified through experiments, and the results show that the system operates consistently, can obtain high-quality music samples, and can extract good music characteristics.

MFCC-based descriptor for bee queen presence detection

Monitoring and detecting hive health are essential for the development of ecosystem conservation strategies and sustainable beekeeping. The absence of the queen bee is a warning signal and indicates an anomalous situation. The literature contains promising machine learning and deep learning techniques for detecting the presence of the queen bee through the spectral temporal analysis of hive sounds. Despite works that consider handcrafted and deep features to describe the problem, there is a lack of feature selection to evaluate which descriptors could better discriminate the presence/absence of queen bee. Additionally, deep learning techniques lead to high-dimension descriptors and a high computational cost. In this context, we explore feature extraction and selection techniques to obtain efficient and compact descriptors that can perform classification in a real-time monitoring scenario. The results reveal that combining cepstral, time, and frequency features achieve 0.99 for accuracy, kappa, area under curve, specificity, and sensibility metrics, outperforming most state-of-the-art models for queen bee presence classification, including convolution neural network models. The feature selection step notably reduces the descriptor size and maintains classification performance. The 15 and 31 mel-frequency cepstrum coefficients descriptors have a low dimension and are based on only one feature, which reduces the number of calculation and library dependencies. Given this, we believe that our findings can support deployment in low-computational devices for non-intrusive and real-time hive health diagnoses.

Extraction of low-dimensional features for single-channel common lung sound classification.

In this study, feature extraction methods used in the classification of single-channel lung sounds obtained by automatic identification of respiratory cycles were examined in detail in order to extract distinctive features at the lowest size. In this way, it will be possible to design a system for the detection of lung diseases, completely autonomously. In the study, automatic separation and classification of 400 respiratory cycles were performed from the single-channel common lung sounds obtained from 94 people. Leave one out cross validation (LOOCV) was used for the calibration and validation of the classification model. The Mel frequency cepstrum coefficients (MFCC), time domain features, frequency domain features, and linear predictive coding (LPC) were used for classification. The performance of the features was tested using linear discriminant analysis (LDA), k-nearest neighbors (k-NN), support vector machines (SVM), and naive Bayes (NB) classification algorithms. The success of combinations of features was explored and enhanced using the sequential forward selection (SFS). As a result, the best accuracy (90.14% in the training set and 90.63% in the test set) was acquired using the k-NN for the triple combination, which included the standard deviation of LPC and the standard deviation and the mean of MFCC.

The Effect of Signal Duration on the Classification of Heart Sounds: A Deep Learning Approach

Deep learning techniques are the future trend for designing heart sound classification methods, making conventional heart sound segmentation dispensable. However, despite using fixed signal duration for training, no study has assessed its effect on the final performance in detail. Therefore, this study aims at analysing the duration effect on the commonly used deep learning methods to provide insight for future studies in data processing, classifier, and feature selection. The results of this study revealed that (1) very short heart sound signal duration (1 s) weakens the performance of Recurrent Neural Networks (RNNs), whereas no apparent decrease in the tested Convolutional Neural Network (CNN) model was found. (2) RNN outperformed CNN using Mel-frequency cepstrum coefficients (MFCCs) as features. There was no difference between RNN models (LSTM, BiLSTM, GRU, or BiGRU). (3) Adding dynamic information (∆ and ∆²MFCCs) of the heart sound as a feature did not improve the RNNs’ performance, and the improvement on CNN was also minimal (≤2.5% in MAcc). The findings provided a theoretical basis for further heart sound classification using deep learning techniques when selecting the input length.

The Sustainable Development of Intangible Cultural Heritage with AI: Cantonese Opera Singing Genre Classification Based on CoGCNet Model in China

Chinese Cantonese opera, a UNESCO Intangible Cultural Heritage (ICH) of Humanity, has faced a series of development problems due to diversified entertainment and emerging cultures. While, the management on Cantonese opera data in a scientific manner is conducive to the sustainable development of ICH. Therefore, in this study, a scientific and standardized audio database dedicated to Cantonese opera is established, and a classification method for Cantonese opera singing genres based on the Cantonese opera Genre Classification Networks (CoGCNet) model is proposed given the similarity of the rhythm characteristics of different Cantonese opera singing genres. The original signal of Cantonese opera singing is pre-processed to obtain the Mel-Frequency Cepstrum as the input of the model. The cascade fusion CNN combines each segment’s shallow and deep features; the double-layer LSTM and CNN hybrid network enhance the contextual relevance between signals. This achieves intelligent classification management of Cantonese opera data, meanwhile effectively solving the problem that existing methods are difficult to classify accurately. Experimental results on the customized Cantonese opera dataset show that the method has high classification accuracy with 95.69% Precision, 95.58% Recall and 95.60% F1 value, and the overall performance is better than that of the commonly used neural network models. In addition, this method also provides a new feasible idea for the sustainable development of the study on the singing characteristics of the Cantonese opera genres.