Mel Frequency Cepstral Coefficients Research Articles

Research on sound quality of roller chain transmission system based on multi-source transfer learning.

To establish the sound quality evaluation model of roller chain transmission system, we collect the running noise under different working conditions. After the noise samples are preprocessed, a group of experienced testers are organized to evaluate them subjectively. Mel frequency cepstral coefficient (MFCC) of each noise sample is calculated, and the MFCC feature map is used as an objective evaluation. Combining with the subjective and objective evaluation results of the roller chain system noise, we can get the original dataset of its sound quality research. However, the number of high-quality noise samples is relatively small. Based on the sound quality research of various chain transmission systems, a novel method called multi-source transfer learning convolutional neural network (MSTL-CNN) is proposed. By transferring knowledge from multiple source tasks to target task, the difficulty of small sample sound quality prediction is solved. Compared with the problem that single source task transfer learning has too much error on some samples, MSTL-CNN can give full play to the advantages of all transfer learning models. The results also show that the MSTL-CNN proposed in this paper is significantly better than the traditional sound quality evaluation methods.

Novel Approach Combining Shallow Learning and Ensemble Learning for the Automated Detection of Swallowing Sounds in a Clinical Database

Cervical auscultation is a simple, noninvasive method for diagnosing dysphagia, although the reliability of the method largely depends on the subjectivity and experience of the evaluator. Recently developed methods for the automatic detection of swallowing sounds facilitate a rough automatic diagnosis of dysphagia, although a reliable method of detection specialized in the peculiar feature patterns of swallowing sounds in actual clinical conditions has not been established. We investigated a novel approach for automatically detecting swallowing sounds by a method wherein basic statistics and dynamic features were extracted based on acoustic features: Mel Frequency Cepstral Coefficients and Mel Frequency Magnitude Coefficients, and an ensemble learning model combining Support Vector Machine and Multi-Layer Perceptron were applied. The evaluation of the effectiveness of the proposed method, based on a swallowing-sounds database synchronized to a video fluorographic swallowing study compiled from 74 advanced-age patients with dysphagia, demonstrated an outstanding performance. It achieved an F1-micro average of approximately 0.92 and an accuracy of 95.20%. The method, proven effective in the current clinical recording database, suggests a significant advancement in the objectivity of cervical auscultation. However, validating its efficacy in other databases is crucial for confirming its broad applicability and potential impact.

HBNET: A blended ensemble model for the detection of cardiovascular anomalies using phonocardiogram.

Cardiac diseases are highly detrimental illnesses, responsible for approximately 32% of global mortality [1]. Early diagnosis and prompt treatment can reduce deaths caused by cardiac diseases. In paediatric patients, it is challenging for paediatricians to identify functional murmurs and pathological murmurs from heart sounds. The study intends to develop a novel blended ensemble model using hybrid deep learning models and softmax regression to classify adult, and paediatric heart sounds into five distinct classes, distinguishing itself as a groundbreaking work in this domain. Furthermore, the research aims to create a comprehensive 5-class paediatric phonocardiogram (PCG) dataset. The dataset includes two critical pathological classes, namely atrial septal defects and ventricular septal defects, along with functional murmurs, pathological and normal heart sounds. The work proposes a blended ensemble model (HbNet-Heartbeat Network) comprising two hybrid models, CNN-BiLSTM and CNN-LSTM, as base models and Softmax regression as meta-learner. HbNet leverages the strengths of base models and improves the overall PCG classification accuracy. Mel Frequency Cepstral Coefficients (MFCC) capture the crucial audio signal characteristics relevant to the classification. The amalgamation of these two deep learning structures enhances the precision and reliability of PCG classification, leading to improved diagnostic results. The HbNet model exhibited excellent results with an average accuracy of 99.72% and sensitivity of 99.3% on an adult dataset, surpassing all the existing state-of-the-art works. The researchers have validated the reliability of the HbNet model by testing it on a real-time paediatric dataset. The paediatric model's accuracy is 86.5%. HbNet detected functional murmur with 100% precision. The results indicate that the HbNet model exhibits a high level of efficacy in the early detection of cardiac disorders. Results also imply that HbNet has the potential to serve as a valuable tool for the development of decision-support systems that aid medical practitioners in confirming their diagnoses. This method makes it easier for medical professionals to diagnose and initiate prompt treatment while performing preliminary auscultation and reduces unnecessary echocardiograms.

Automatically detecting OSAHS patients based on transfer learning and model fusion

Abstract Snoring is the most typical symptom of obstructive sleep apnea hypopnea syndrome (OSAHS) that can be used to develop a non-invasive approach for automatically detecting OSAHS patients. In this work, a model based on transfer learning and model fusion was applied to classify simple snorers and OSAHS patients. Three kinds of basic models were constructed based on pretrained Visual Geometry Group-16 (VGG16), Pretrained Audio Neural Networks (PANN), and Mel-frequency Cepstral Coefficient (MFCC). The XGBoost was used to select features based on feature importance, the max voting strategy was applied to fuse these basic models and leave-one-subject-out cross validation was used to evaluate the proposed model. The results show that the fused model embedded with top-5 VGG16 features and top-5 PANN features can correctly identify OSAHS patients (AHI>5) with 100% accuracy. The proposed fused model provides a good classification performance with lower computational cost and higher robustness that makes detecting OSAHS patients at home possible.

Flexible Self-Powered Low-Decibel Voice Recognition Mask

In environments where silent communication is essential, such as libraries and conference rooms, the need for a discreet means of interaction is paramount. Here, we present a single-electrode, contact-separated triboelectric nanogenerator (CS-TENG) characterized by robust high-frequency sensing capabilities and long-term stability. Integrating this TENG onto the inner surface of a mask allows for the capture of conversational speech signals through airflow vibrations, generating a comprehensive dataset. Employing advanced signal processing techniques, including short-time Fourier transform (STFT), Mel-frequency cepstral coefficients (MFCC), and deep learning neural networks, facilitates the accurate identification of speaker content and verification of their identity. The accuracy rates for each category of vocabulary and identity recognition exceed 92% and 90%, respectively. This system represents a pivotal advancement in facilitating secure and efficient unobtrusive communication in quiet settings, with promising implications for smart home applications, virtual assistant technology, and potential deployment in security and confidentiality-sensitive contexts.

Machine learning for parameters diagnosis of spark discharge by electro-acoustic signal

Abstract Discharge plasma parameter measurement is a key focus in low-temperature plasma research. Traditional diagnostics often require costly equipment, whereas electro-acoustic signals provide a rich, non-invasive, and less complex source of discharge information. This study harnesses machine learning to decode these signals. It establishes links between electro-acoustic signals and gas discharge parameters, such as power and distance, thus streamlining the prediction process. By building a spark discharge platform to collect electro-acoustic signals and implementing a series of acoustic signal processing techniques, the Mel-Frequency Cepstral Coefficients (MFCCs) of the acoustic signals are extracted to construct the predictors. Three machine learning models (Linear Regression, k-Nearest Neighbors, and Random Forest) are introduced and applied to the predictors to achieve real-time rapid diagnostic measurement of typical spark discharge power and discharge distance. All models display impressive performance in prediction precision and fitting abilities. Among them, the k-Nearest Neighbors model shows the best performance on discharge power prediction with the lowest mean square error (MSE=0.00571) and the highest R-squared value (R^2=0.93877). The experimental results show that the relationship between the electro-acoustic signal and the gas discharge power and distance can be effectively constructed based on the machine learning algorithm, which provides a new idea and basis for the online monitoring and real-time diagnosis of plasma parameters.

Parallel Dual-Branch Fusion Network for Epileptic Seizure Prediction

Epilepsy is a prevalent chronic disorder of the central nervous system. The timely and accurate seizure prediction using the scalp Electroencephalography (EEG) signal can make patients adopt reasonable preventive measures before seizures occur and thus reduce harm to patients. In recent years, deep learning-based methods have made significant progress in solving the problem of epileptic seizure prediction. However, most current methods mainly focus on modeling short- or long-term dependence in EEG, while neglecting to consider both. In this study, we propose a Parallel Dual-Branch Fusion Network (PDBFusNet) which aims to combine the complementary advantages of Convolutional Neural Network (CNN) and Transformer. Specifically, the features of the EEG signal are first extracted using Mel Frequency Cepstral Coefficients (MFCC). Then, the extracted features are delivered into the parallel dual-branches to simultaneously capture the short- and long-term dependencies of EEG signal. Further, regarding the Transformer branch, a novel feature fusion module is developed to enhance the ability of utilizing time, frequency, and channel information. To evaluate our proposal, we perform sufficient experiments on the public epileptic EEG dataset CHB-MIT, where the accuracy, sensitivity, specificity and precision are 95.76%, 95.81%, 95.71% and 95.71%, respectively. PDBFusNet shows superior performance compared to state-of-the-art competitors, which confirms the effectiveness of our proposal.

Amharic spoken digits recognition using convolutional neural network

Spoken digits recognition (SDR) is a type of supervised automatic speech recognition, which is required in various human–machine interaction applications. It is utilized in phone-based services like dialing systems, certain bank operations, airline reservation systems, and price extraction. However, the design of SDR is a challenging task that requires the development of labeled audio data, the proper choice of feature extraction method, and the development of the best performing model. Even if several works have been done for various languages, such as English, Arabic, Urdu, etc., there is no developed Amharic spoken digits dataset (AmSDD) to build Amharic spoken digits recognition (AmSDR) model for the Amharic language, which is the official working language of the government of Ethiopia. Therefore, in this study, we developed a new AmSDD that contains 12,000 utterances of 0 (Zaero) to 9 (zet’enyi) digits which were recorded from 120 volunteer speakers of different age groups, genders, and dialects who repeated each digit ten times. Mel frequency cepstral coefficients (MFCCs) and Mel-Spectrogram feature extraction methods were used to extract trainable features from the speech signal. We conducted different experiments on the development of the AmSDR model using the AmSDD and classical supervised learning algorithms such as Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF) as the baseline. To further improve the performance recognition of AmSDR, we propose a three layers Convolutional Neural Network (CNN) architecture with Batch normalization. The results of our experiments show that the proposed CNN model outperforms the baseline algorithms and scores an accuracy of 99% and 98% using MFCCs and Mel-Spectrogram features, respectively.

Dual-branch network with fused Mel features for logic-manipulated speech detection

In recent years, logic-manipulated speech detection has predominantly relied on single-feature and single-channel networks, which fail to consider a broader range of acoustic features. To address this limitation, we propose a novel approach based on a dual-branch network that incorporates fused Mel features for detecting logic-manipulated speech. The structure of our network consists of three main components. Firstly, we extract Mel spectrograms and Mel-frequency cepstral coefficients (MFCC) to address the deficiencies caused by the reduction in the information dimension. Then, to better utilize these two types of features, we construct a dual-branch network framework that enables the separate learning of temporal and frequency domain characteristics. Finally, at the backend of the network, we fuse the feature vectors for decision-making. Experimental evaluations conducted on the ASVspoof2019LA dataset demonstrate the effectiveness and superiority of our proposed method compared to state-of-the-art models and other acoustic features.

Sound classification with time-frequency features in forest environment

The study of forest sound classification has drawn more attention recently due to its potential for illegal activities and natural disaster monitoring. Based on the forest sound classification dataset (FSC22), a dataset specific to possible sound existing in the forest, five classification methods are utilized to investigate the relationship between recognition accuracy and the number of sound acoustic features, as well as the number of target classes. The results confirmed that extreme random forest is the best method for forest sound classification, with an accuracy of around 70% when the target class number is above 20. Further, Mel-frequency cepstral coefficients are the critical feature for sound classification, while fuzzy labels in the dataset may reduce the success rate of recognition.

Gammatone-Frequency Cepstral Coefficients Based Fear Emotion Level Recognition System

Emotions represent affective states that induce alterations in behavior and interactions within one's environment. An avenue for discerning human emotions lies in the realm of speech analysis. Empirical evidence indicates that 1.6 million Indonesian teenagers grapple with mental anxiety disorders, characterized by sensations of fear or ambiguous vigilance. This work endeavors to devise a tool for discerning an individual's emotional state through voice processing, focusing particularly on fear emotions stratified into three levels of intensity: low, medium, and high. The proposed system employs Gammatone-Frequency Cepstral Coefficients (GFCC) for feature extraction, leveraging the efficacy of its gamma filter in reducing noise. Furthermore, a Random Forest (RF) Classifier is integrated to facilitate the recognition of fear's emotional intensity in speech signals. The system is deployed on a Raspberry Pi 4B and establishes a Bluetooth connection using the RFCOMM communication protocol to an Android application, presenting the classification results. The outcomes reveal that the Signal-to-Noise Reduction achieved through GFCC extraction surpasses that of Mel-Frequency Cepstral Coefficients (MFCC). In terms of accuracy, the implemented recognition system for fear emotion levels, employing GFCC extraction and Random Forest Classifier, attains a commendable accuracy of 73.33 %.

Face and Speech Emotion Recognition System

Abstract: Emotions serve as the cornerstone of human communication, facilitating the expression of one's inner thoughts and feelings to others. Speech Emotion Recognition (SER) represents a pivotal endeavour aimed at deciphering the emotional nuances embedded within a speaker's voice signal. Universal emotions such as neutrality, anger, happiness, and sadness form the basis of this recognition process, allowing for the identification of fundamental emotional states. To achieve this, spectral and prosodic features are leveraged, each offering unique insights into the emotional content of speech. Spectral features, exemplified by the Mel Frequency Cepstral Coefficient (MFCC), provide a detailed analysis of the frequency distribution within speech signals, while prosodic features encompass elements like fundamental frequency, volume, pitch, speech intensity, and glottal parameters, capturing the rhythmic and tonal variations indicative of different emotional states. Through the integration of these features, SER systems can effectively simulate and classify a diverse range of emotional expressions, paving the way for enhanced human-computer interaction and communication technologies

Creation of a diverse mixed-lingual emotional speech corpus with a framework for enhanced emotion detection

Speech Emotion Recognition (SER) has advanced considerably during the past 20 years. Till date, various SER systems have been developed for monolingual, multilingual and cross corpus contexts. However, in a country like India where numerous languages are spoken and often humans converse in more than one language, a dedicated SER system for mixed-lingual scenario is more crucial to be established which is the focus of this work. A self-recorded database that includes speech emotion samples with 11 diverse Indian languages has been developed. In parallel, a mixed-lingual database is formed with three popular standard databases of Berlin, Baum and SAVEE to represent mixed-lingual environment for western background. A detailed investigation of GeMAPS (Geneva Minimalistic Acoustic Parameter Set) feature set for mixed-lingual SER is performed. A distinct set of MFCC (Mel Frequency Cepstral Coefficients) coefficients derived from sine and cosine-based filter banks enriches the GeMAPS feature set and are proven to be robust for mixed-lingual emotion recognition. Various Machine Learning (ML) and Deep Learning (DL) algorithms have been applied for emotion recognition. The experimental results demonstrate GeMAPS features classified from ML has been quite robust for recognizing all the emotions across the mixed-lingual database of the western languages. However, with diverse recording conditions and languages of the Indian self-recorded database the GeMAPS with enriched features and classified using DL are proven to be significant for mixed-lingual emotion recognition.

Marathi Speech Emotion recognition using Deep Learning techniques.

Marathi Speech Emotion recognition using Deep Learning techniques.

Advances in Speech Emotion Recognition and Analysis: A Review of Applied Machine Learning Methodologies

Abstract: The most important marketing tactics in the current environment is emotion detection based on individual’s interests, allowing extensive customization to cater to the varied needs of customers. For instance, contact centers may start playing music while a caller is on the line if they are agitated. A smart car that slows down when the driver is angry or scared is another illustration.The recognition of speech emotion has become a vital task in the field of computer science department . A very important aspect of human emotional state information is speech emotion that is the source of communication between human and computer interactions . The recognition of speech emotion is currently a very hot topic in the field of machine learning and deep learning and many systems have been build and are currently working on the recognition of these speech emotions using deep learning and neural networks algorithms. In this paper, various types of models and features like Mel-frequency Cepstral Coefficients (MFCC), Modulation spectral features (MSFs), Low-Level Descriptors(LLDs) for speech emotion recognition are categorized. Ultimately, the algorithms like recurrent neural network(RNN), support vector machine(SVM) used in different models are tabulated presenting their advantages and disadvantages along with their features and efficiency.

Prediction of Unilateral Vocal Cord Paralysis Patients Through Machine Learning Analysis of Acoustic Parameters: A Preliminary Study

Background and Objectives The purpose of this study is to evaluate value of diagnostic tool for vocal cord palsy utilizing artificial intelligence without laryngoscopeMaterials and Method A dataset consisting of recordings from patients with unilateral vocal cord paralysis (n=54) as well as normal individuals (n=163). The dataset included prolonged pronunciations of the vowels /ah/, /u/, /i/, and vocal cord data from paralyzed patients. Various acoustic parameters such as Mel-frequency cepstral coefficients, jitter, shimmer, harmonics-to-noise ratio, and fundamental frequency statistics were analyzed. The classification of vocal cord paralysis encompassed paralysis status, paralysis degree, and paralysis location. The deep learning model employed the leave-one-out method, and the feature set with the highest performance was selected using the following methods.Results Vocal Cord Paralysis Classifier: The classifier accurately distinguished normal voice from vocal cord paralysis, achieving an accuracy and F1 score of 1.0. Paralysis Location Classifier: The classifier accurately differentiated between median and paramedian vocal cord paralysis, achieving an accuracy and micro F1 score of 1.0. Breathiness Degree Classifier: The classifier achieved an accuracy of 0.795 and a mean absolute error of 0.2857 in distinguishing different degrees of breathiness.Conclusion Although the small sample size raises concerns of potential overfitting, this preliminary study highlights distinctive acoustic features in cases of unilateral vocal fold paralysis compared to those of normal individuals. These findings suggest the feasibility of determining the presence, degree, and location of paralysis through the utilization of acoustic parameters. Further research is warranted to validate and expand upon these results.

Convolutional Neural Networks to Facilitate the Continuous Recognition of Arabic Speech with Independent Speakers

Automatic speech recognition (ASR) is a field of research that focuses on the ability of computers to process and interpret speech feedback from humans and to provide the highest degree of accuracy in recognition. Speech is one of the simplest ways to convey a message in a basic context, and ASR refers to the ability of machines to process and accept speech data from humans with the greatest degree of accuracy. As the human-to-machine interface continues to evolve, speech recognition is expected to become increasingly important. However, the Arabic language has distinct features that set it apart from other languages, such as the dialect and the pronunciation of words. Until now, insufficient attention has been devoted to continuous Arabic speech recognition research for independent speakers with a limited database. This research proposed two techniques for the recognition of Arabic speech. The first uses a combination of convolutional neural network (CNN) and long short-term memory (LSTM) encoders, and an attention-based decoder, and the second is based on the Sphinx-4 recognizer, which includes pocket sphinx, base sphinx, and sphinx train, with various types and number of features to be extracted (filter bank and mel frequency cepstral coefficients (MFCC)) based on the CMU Sphinx tool, which generates a language model for different sentences spoken by different speakers. These approaches were tested on a dataset containing 7 hours of spoken Arabic from 11 Arab countries, covering the Levant, Gulf, and African regions, which make up the Arab world, and achieved promising results. CNN-LSTM achieved a word error rate (WER) of 3.63% using 120 features for filter bank and 4.04% WER using 39 features for MFCC, respectively, while the Sphinx-4 recognizer technique achieved 8.17% WER and an accuracy of 91.83% using 25 features for MFCC and 8 Gaussian mixtures, respectively, when tested on the same benchmark dataset.

A Comparative Study of Khasi Speech Recognition Systems with Recurrent Neural Network-Based Language Model

This paper offers a comparative analysis of Khasi speech recognition systems utilizing a recurrent neural network-based language model (RNN-LM). Develop different acoustic models (AMs) to evaluate the optimal performance. This paper observed that using RNN-LM performed best than traditional other models. The wave surfer performs data processing followed by collecting the recorder based continuous speech database. Moreover, a minimization of word error rate (WER) in 2.83.8% range for major speech data and 2.4-3.5% for minor speech data. Additionally, two acoustic features are used, and from the experimental results, the Mel frequency cepstral coefficient (MFCC) yielded improved performance than the perceptual linear prediction (PLP).

Deep Learning for Arabic Speech Recognition Using Convolutional Neural Networks

Extracting the speaker's emotional state from their speech has become an active research topic lately due to the demand for more human interactive applications. This field of research has noted significant advancement, especially in the English language, owing to the availability of massive speech-labeled corpora. However, the progress of analogous methodologies in the Arabic language is still in its infancy stages. In this paper, we present a Speech Recognition model for the Arabic language, proficient in discerning both the emotional state and gender of the speaker through voice analysis. Three primary emotion labels were selected: low, standard, and high levels of emotion. Various spectral features, such as the mel-frequency cepstral coefficient (MFCC), were extracted and tested to determine the optimal features. Furthermore, various Machine Learning models (SVM, KNN, and HMM) and Deep Learning models (LSTM and CNN) were evaluated for training. The results were compared between the five models using different extracted features, ultimately culminating in the selection of MFCC, root-mean-square (RMS), mel-scaled spectrogram, spectral, and zero-crossing rate as spectral features, and the CNN as a classification model. This selection yielded significant results, with an accuracy of 93% for emotion recognition and 99% for gender recognition.

Classification of laryngeal diseases including laryngeal cancer, benign mucosal disease, and vocal cord paralysis by artificial intelligence using voice analysis

Voice change is often the first sign of laryngeal cancer, leading to diagnosis through hospital laryngoscopy. Screening for laryngeal cancer solely based on voice could enhance early detection. However, identifying voice indicators specific to laryngeal cancer is challenging, especially when differentiating it from other laryngeal ailments. This study presents an artificial intelligence model designed to distinguish between healthy voices, laryngeal cancer voices, and those of the other laryngeal conditions. We gathered voice samples of individuals with laryngeal cancer, vocal cord paralysis, benign mucosal diseases, and healthy participants. Comprehensive testing was conducted to determine the best mel-frequency cepstral coefficient conversion and machine learning techniques, with results analyzed in-depth. In our tests, laryngeal diseases distinguishing from healthy voices achieved an accuracy of 0.85–0.97. However, when multiclass classification, accuracy ranged from 0.75 to 0.83. These findings highlight the challenges of artificial intelligence-driven voice-based diagnosis due to overlaps with benign conditions but also underscore its potential.