Linear Prediction Cepstral Coefficients Research Articles

MFCC-CNN: A patient-independent seizure prediction model.

Automatic prediction of seizures is a major goal in the field of epilepsy. However, the high variability of Electroencephalogram (EEG) signals in different patients limits the use of prediction models in clinical applications. This paper proposes a patient-independent seizure prediction model, named MFCC-CNN, to improve the generalization ability. MFCC-CNN model introduces Mel-Frequency Cepstrum Coefficients (MFCC) features and Linear Predictive Cepstral Coefficients (LPCC) features concentrated in the low frequency region, which contains more detailed information. Convolutional neural network (CNN) is used to construct a seizure prediction model. Experimental results showed that the proposed model obtained accuracy of 96 , sensitivity of 92 , specificity of 84 and F1-score of 85 for 24 cases in CNHB-MIT dataset. The overall performance of MFCC-CNN model is better than the other models. MFCC-CNN model does not need to be specifically customized for different patients. As a patient-independent seizure prediction model, it has good generalization ability.

Automatic detection of obstructive sleep apnea based on speech or snoring sounds: a narrative review.

Obstructive sleep apnea (OSA) is a common chronic disorder characterized by repeated breathing pauses during sleep caused by upper airway narrowing or collapse. The gold standard for OSA diagnosis is the polysomnography test, which is time consuming, expensive, and invasive. In recent years, more cost-effective approaches for OSA detection based in predictive value of speech and snoring has emerged. In this paper, we offer a comprehensive summary of current research progress on the applications of speech or snoring sounds for the automatic detection of OSA and discuss the key challenges that need to be overcome for future research into this novel approach. PubMed, IEEE Xplore, and Web of Science databases were searched with related keywords. Literature published between 1989 and 2022 examining the potential of using speech or snoring sounds for automated OSA detection was reviewed. Speech and snoring sounds contain a large amount of information about OSA, and they have been extensively studied in the automatic screening of OSA. By importing features extracted from speech and snoring sounds into artificial intelligence models, clinicians can automatically screen for OSA. Features such as formant, linear prediction cepstral coefficients, mel-frequency cepstral coefficients, and artificial intelligence algorithms including support vector machines, Gaussian mixture model, and hidden Markov models have been extensively studied for the detection of OSA. Due to the significant advantages of noninvasive, low-cost, and contactless data collection, an automatic approach based on speech or snoring sounds seems to be a promising tool for the detection of OSA.

Improving piano music signal recognition through enhanced frequency domain analysis

Feature extraction is a crucial component in the analysis of piano music signals. This article introduced three methods for feature extraction based on frequency domain analysis, namely short-time Fourier transform (STFT), linear predictive cepstral coefficient (LPCC), and Mel-frequency cepstral coefficient (MFCC). An improvement was then made to the MFCC. The inverse MFCC (IMFCC) was combined with mid-frequency MFCC (MidMFCC). The Fisher criterion was used to select the 12-order parameters with the maximum Fisher ratio, which were combined into the F-MFCC feature for recognizing 88 single piano notes through a support vector machine. The results indicated that when compared with the STFT and LPCC, the MFCC exhibited superior performance in recognizing piano music signals, with an accuracy rate of 78.03 % and an F1 value of 85.92 %. Nevertheless, the proposed F-MFCC achieved a remarkable accuracy rate of 90.91 %, representing a substantial improvement by 12.88 % over the MFCC alone. These findings provide evidence for the effectiveness of the designed F-MFCC feature for piano music signal recognition as well as its potential application in practical music signal analysis.

Likelihood Ratio Based Voice Comparison Using Cepstral Coefficients and GAN

In order to identify suspects in digital forensics, Forensic Voice Comparison (FVC) entails comparing trace voices with known suspect voice samples. The FVC uses semi-automatic approaches to identify speakers using acoustic features. Two Australian datasets such as forensic evaluation dataset 1 & English dataset 2 were used. The current research on FVC samples is noise-reduced using a stationary noise reduction algorithm within a likelihood ratio-based framework. To demonstrate the power of knowledge representation, acoustic features like Mel Frequency Cepstral Coefficients(MFCC) and Linear Predictive Cepstral Coefficients (LPCC) were extracted. The suspect is then identified by classifying the model using the Generative Adversarial Network(GAN) and Artificial Neural Network (ANN). The accuracy is 80% & 90% for MFCC for dataset 1 & 2. Similarly, accuracy is 20% & 40% for LPCC for dataset 1 & 2. The overall comparison of acoustic features with two different datasets the MFCC performs better accurate results for Australian English dataset 2 with varying sample sizes.

Evaluation of Machine Learning Algorithms using Combined Feature Extraction Techniques for Speaker Identification

The aim of this study is to evaluate and compare machine learning algorithms when various feature extraction techniques are employed together and determine the optimal feature combinations for the models studied. The TIMIT online database was used where 5 male and 5 female non-native English speakers from five American locations were selected. Each speaker had ten 3-second utterances, totaling 500. Mel frequency cepstral coefficients (MFCC), linear predictive cepstral coefficients (LPCC), gammatone frequency cepstral coefficients (GFCC), discrete wavelet transforms (DWT) and pitch features were extracted using MATLAB and concatenated. The concatenated features were used to train and evaluate three classifier models—Random Forest (RF), Linear Discriminant Analysis (LDA), and Logistic Regression (LR)—using Python software. The results obtained showed that as the number of features combinations increased, the models’ performances improved as well. This improved performance was observed when all the cepstral features were part of the combinations. This implies that cepstral features are more robust and improve speaker identification systems. The best average score of accurate predictions of ≈ 76% for the LR model was obtained for the MGL (39 features) features combination and dropped to 70% for the highest number of feature combinations MGDLP (53 features). This indicates that more training data improves system performance, however, too much data does not translate to even better performance because the system will eventually achieve its peak performance. This information is useful for applications where limited data can present a problem.

Advancements in Speaker Recognition: Exploring Mel Frequency Cepstral Coefficients (MFCC) for Enhanced Performance in Speaker Recognition

Abstract: Speaker recognition, a fundamental capability of software or hardware systems, involves receiving speech signals, identifying the speaker present in the speech signal, and subsequently recognizing the speaker for future interactions. This process emulates the cognitive task performed by the human brain. At its core, speaker recognition begins with speech as the input to the system. Various techniques have been developed for speech recognition, including Mel frequency cepstral coefficients (MFCC), Linear Prediction Coefficients (LPC), Linear Prediction Cepstral coefficients (LPCC), Line Spectral Frequencies (LSF), Discrete Wavelet Transform (DWT), and Perceptual Linear Prediction (PLP). Although LPC and several other techniques have been explored, they are often deemed impractical for real-time applications. In contrast, MFCC stands out as one of the most prominent and widely used techniques for speaker recognition. The utilization of cepstrum allows for the computation of resemblance between two cepstral feature vectors, making it an effective tool in this domain. In comparison to LPC-derived cepstrum features, the use of MFCC features has demonstrated superior performance in metrics such as False Acceptance Rate (FAR) and False Rejection Rate (FRR) for speaker recognition systems. MFCCs leverage the human ear's critical bandwidth fluctuations with respect to frequency. To capture phonetically important characteristics of speech signals, filters are linearly separated at low frequencies and logarithmically separated at high frequencies. This design choice is central to the effectiveness of the MFCC technique. The primary objective of the proposed work is to devise efficient techniques that extract pertinent information related to the speaker, thereby enhancing the overall performance of the speaker recognition system. By optimizing feature extraction methods, this research aims to contribute to the advancement of speaker recognition technology.

Performance Evaluation of Environmental Sound Classification: A Machine Learning Stacking and Multi-Criteria Metrics Based Approach

This study proposes an Environment Sound Classification Task (ESC) model that includes numerous element channels given as a contribution to Machine learning with an Attention instrument. ESC is a significant testing issue. The interest in the paper lies in utilizing different part channels involving the MFCCs-Mel Frequency Cepstral Coefficients a mutual module in speaker detection and artificial speech systems. LPCs-Linear Prediction Coefficients and Linear Prediction Cepstral Coefficients were the most commonly used types in ASR- Automated speech recognition. The paper also discusses some basic features of MFCCs and how to put them into practice. The techniques used for this project are Background Gaussian Noise and Time Shifting, by observing that every technique is implemented with a provided probability, however, when at the time of generation of a new sample the same spectrogram input may have several combinations. We go through the blend information expansion method to additional lift execution. Our model can accomplish cutting-edge execution on three benchmark climate sound characterization datasets, for example, the UrbanSound8K.

Comparative analysis of various feature extraction techniques for classification of speech disfluencies

Speech plays a vital role in communication, from expressing oneself, to utilizing speech-based platforms, speech is a necessity. Any disruption in speech is referred to as disfluency, and can impact one's quality of life. This paper presents an experimental study on various techniques for the detection and classification of speech disfluencies. Six different types of disfluencies are examined in this paper, namely Interjection, Sound Repetition, Word Repetition, Phrase Repetition, Revision and Prolongation (6 classes). However, this paper also goes a step further by including the clean speech signals as an added class alongside the six disfluencies, thereby making this work more robust with 7 classes. Various machine learning approaches have been investigated on the University College London Archive of Stuttered Speech (UCLASS) dataset; a standard disfluency dataset generated by University College London (UCL). Five different feature extraction techniques viz. Mel Frequency Cepstral Coefficients (MFCC), Linear Predictive Cepstral Coefficients (LPCC), Gammatone Frequency Cepstral Coefficients (GFCC), Mel-filterbank energy features, and Spectrograms have been used. Comparative analysis of various classifiers shows that MFCC, GFCC, and Spectrograms achieved greater than 90% accuracy on both 6 and 7 classes with the kNN classifier. As a future scope to this study, the authors aim to focus on tackling the challenges of detecting multiple disfluencies present simultaneously in a speech sample.

A Narrative Review of Speech and EEG Features for Schizophrenia Detection: Progress and Challenges.

Schizophrenia is a mental illness that affects an estimated 21 million people worldwide. The literature establishes that electroencephalography (EEG) is a well-implemented means of studying and diagnosing mental disorders. However, it is known that speech and language provide unique and essential information about human thought. Semantic and emotional content, semantic coherence, syntactic structure, and complexity can thus be combined in a machine learning process to detect schizophrenia. Several studies show that early identification is crucial to prevent the onset of illness or mitigate possible complications. Therefore, it is necessary to identify disease-specific biomarkers for an early diagnosis support system. This work contributes to improving our knowledge about schizophrenia and the features that can identify this mental illness via speech and EEG. The emotional state is a specific characteristic of schizophrenia that can be identified with speech emotion analysis. The most used features of speech found in the literature review are fundamental frequency (F0), intensity/loudness (I), frequency formants (F1, F2, and F3), Mel-frequency cepstral coefficients (MFCC's), the duration of pauses and sentences (SD), and the duration of silence between words. Combining at least two feature categories achieved high accuracy in the schizophrenia classification. Prosodic and spectral or temporal features achieved the highest accuracy. The work with higher accuracy used the prosodic and spectral features QEVA, SDVV, and SSDL, which were derived from the F0 and spectrogram. The emotional state can be identified with most of the features previously mentioned (F0, I, F1, F2, F3, MFCCs, and SD), linear prediction cepstral coefficients (LPCC), linear spectral features (LSF), and the pause rate. Using the event-related potentials (ERP), the most promissory features found in the literature are mismatch negativity (MMN), P2, P3, P50, N1, and N2. The EEG features with higher accuracy in schizophrenia classification subjects are the nonlinear features, such as Cx, HFD, and Lya.

An efficient and robust Phonocardiography (PCG)-based Valvular Heart Diseases (VHD) detection framework using Vision Transformer (ViT)

Background and objectives:Valvular heart diseases (VHDs) are one of the dominant causes of cardiovascular abnormalities that have been associated with high mortality rates globally. Rapid and accurate diagnosis of the early stage of VHD based on cardiac phonocardiogram (PCG) signal is critical that allows for optimum medication and reduction of mortality rate. Methods:To this end, the current study proposes novel deep learning (DL)-based high-performance VHD detection frameworks that are relatively simpler in terms of network structures, yet effective for accurately detecting multiple VHDs. We present three different frameworks considering both 1D and 2D PCG raw signals. For 1D PCG, Mel frequency cepstral coefficients (MFCC) and linear prediction cepstral coefficients (LPCC) features, whereas, for 2D PCG, various deep convolutional neural networks (D-CNNs) features are extracted. Additionally, nature/bio-inspired algorithms (NIA/BIA) including particle swarm optimization (PSO) and genetic algorithm (GA) have been utilized for automatic and efficient feature selection directly from the raw PCG signal. To further improve the performance of the classifier, vision transformer (ViT) has been implemented levering the self-attention mechanism on the time frequency representation (TFR) of 2D PCG signal. Our extensive study presents a comparative performance analysis and the scope of enhancement for the combination of different descriptors, classifiers, and feature selection algorithms. Main Results:Among all classifiers, ViT provides the best performance by achieving mean average accuracy Acc of 99.90 % and F1-score of 99.95 % outperforming current state-of-the-art VHD classification models. Conclusions:The present research provides a robust and efficient DL-based end-to-end PCG signal classification framework for designing a automated high-performance VHD diagnosis system.

Deep Learning for the Recognition of Human Speech

Abstract—Humans primarily communicate through speech, and language is their main means of doing so. Emotion is crucial to social connection. Knowing how to recognise the emotions in a speech is important and challenging given that we are dealing with human-machine interaction. Depending on the mood a person is going through, they will express themselves differently. When someone expresses their emotions, each one has a distinctive energy, pitch, and tone fluctuation that is categorised according to the situation. The identification of spoken emotions is thus a future goal for computer vision. The objective of our project is to develop intelligent speechthat utilises a convolutional neural network to recognise emotions. which uses a variety of modules for emotion recognition and classifier. Keywords— Human speech, Emotion, recognition of speech, GMM, Mel-Frequency-Cepstrum Coefficient (MFCC), linear prediction Cepstral coefficients (LPCC) , (LSTM)

Speech Emotion Recognition Based on Multiple Acoustic Features and Deep Convolutional Neural Network

Speech emotion recognition (SER) plays a vital role in human–machine interaction. A large number of SER schemes have been anticipated over the last decade. However, the performance of the SER systems is challenging due to the high complexity of the systems, poor feature distinctiveness, and noise. This paper presents the acoustic feature set based on Mel frequency cepstral coefficients (MFCC), linear prediction cepstral coefficients (LPCC), wavelet packet transform (WPT), zero crossing rate (ZCR), spectrum centroid, spectral roll-off, spectral kurtosis, root mean square (RMS), pitch, jitter, and shimmer to improve the feature distinctiveness. Further, a lightweight compact one-dimensional deep convolutional neural network (1-D DCNN) is used to minimize the computational complexity and to represent the long-term dependencies of the speech emotion signal. The overall effectiveness of the proposed SER systems’ performance is evaluated on the Berlin Database of Emotional Speech (EMODB) and the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) datasets. The proposed system gives an overall accuracy of 93.31% and 94.18% for the EMODB and RAVDESS datasets, respectively. The proposed MFCC and 1-D DCNN provide greater accuracy and outpace the traditional SER techniques.

Stressed Speech Emotion Recognition Using Teager Energy and Spectral Feature Fusion with Feature Optimization.

The objective of speech emotion recognition (SER) is to enhance man-machine interface. It can also be used to cover the physiological state of a person in critical situations. In recent time, speech emotion recognition also finds its operations in medicine and forensics. A new feature extraction technique using Teager energy operator (TEO) is proposed for the detection of stressed emotions as Teager energy-autocorrelation envelope (TEO-Auto-Env). TEO is basically designed for increasing the energies of the stressed speech signals whose energies are reduced during the speech production process and hence used in this analysis. A stressed speech emotion recognition (SSER) system is developed using TEO-Auto-Env and spectral feature combination for detecting the emotions. The spectral features considered are Mel-frequency cepstral coefficients (MFCC), linear prediction cepstral coefficients (LPCC), and relative spectra-perceptual linear prediction (RASTA-PLP). EMO-DB (German), EMOVO (Italian), IITKGP (Telugu), and EMA (English) databases are used in this analysis. The classification of the emotions is carried out using the k-nearest neighborhood (k-NN) classifier for gender-dependent (GD) and speaker-independent (SI) cases. The proposed SSER system provides improved accuracy compared to the existing ones. Average recall is used for performance evaluation. The highest classification accuracy is achieved using the feature combination of TEO-Auto-Env, MFCC, and LPCC features with 91.4% (SI), 91.4% (GD-male), and 93.1%(GD-female) for EMO-DB; 68.5% (SI), 68.5% (GD-male), and 74.6% (GD-female) for EMOVO; 90.6%(SI), 91% (GD-male), and 92.3% (GD-female) for EMA; and 95.1% (GD-female) for IITKGP female database.

Music Note Feature Recognition Method based on Hilbert Space Method Fused with Partial Differential Equations

Hilbert space method is an old mathematical theoretical model developed based on linear algebra and has a high theoretical value and practical application. The basic idea of the Hilbert space method is to use the existence of some stable relationship between variables and to use the dynamic dependence between variables to construct the solution of differential equations, thus transforming mathematical problems into algebraic problems. This paper firstly studies the denoising model in the process of music note feature recognition based on partial differential equations, then analyzes the denoising method based on partial differential equations and gives an algorithm for fused music note feature recognition in Hilbert space; secondly, this paper studies the commonly used music note feature recognition methods, including linear predictive cepstral coefficients, Mel frequency cepstral coefficients, wavelet transform-based feature extraction methods and Hilbert space-based feature extraction methods. Their corresponding feature extraction processes are given.

EEG-based mental fatigue detection using linear prediction cepstral coefficients and Riemann spatial covariance matrix

Objective. Establishing a mental fatigue monitoring system is of great importance as for severe fatigue may cause unimaginable consequences. Electroencephalogram (EEG) is often utilized for mental fatigue detection because of its high temporal resolution and ease of use. However, many EEG-based approaches for detecting mental fatigue only take into account the feature extraction of a single domain and do not fully exploit the information that EEG may offer. Approach. In our work, we propose a new algorithm for mental fatigue detection based on multi-domain feature extraction and fusion. EEG components representing fatigue are closely related in the past and present because fatigue is a dynamic and gradual process. Accordingly, the idea of linear prediction is used to fit the current value with a set of sample values in the past to calculate the linear prediction cepstral coefficients (LPCCs) as the time domain feature. Moreover, in order to better capture fatigue-related spatial domain information, the spatial covariance matrix of the original EEG signal is projected into the Riemannian tangent space using the Riemannian geometric method. Then multi-domain features are fused to obtain comprehensive spatio-temporal information. Main results. Experimental results prove the suggested algorithm outperforms existing state-of-the-art methods, achieving an average accuracy of 87.10% classification on the public dataset SEED-VIG (three categories) and 97.40% classification accuracy (two categories) on the dataset made by self-designed experiments. Significance. These findings show that our proposed strategy perform more effectively for mental fatigue detection based on EEG.

Detection of Neurogenic Voice Disorders Using the Fisher Vector Representation of Cepstral Features

Neurogenic voice disorders (NVDs) are caused by damage or malfunction of the central or peripheral nervous system that controls vocal fold movement. In this paper, we investigate the potential of the Fisher vector (FV) encoding in automatic detection of people with NVDs. FVs are used to convert features from frame level (local descriptors) to utterance level (global descriptors). At the frame level, we extract two popular cepstral representations, namely, Mel-frequency cepstral coefficients (MFCCs) and perceptual linear prediction cepstral coefficients (PLPCCs), from acoustic voice signals. In addition, the MFCC features are also extracted from every frame of the glottal source signal computed using a glottal inverse filtering (GIF) technique. The global descriptors derived from the local descriptors are used to train a support vector machine (SVM) classifier. Experiments are conducted using voice signals from 80 healthy speakers and 80 patients with NVDs (40 with spasmodic dysphonia (SD) and 40 with recurrent laryngeal nerve palsy (RLNP)) taken from the Saarbruecken voice disorder (SVD) database. The overall results indicate that the use of the FV encoding leads to better identification of people with NVDs, compared to the defacto temporal encoding. Furthermore, the SVM trained using the combination of FVs derived from the cepstral and glottal features provides the overall best detection performance.

Portable and Non-Intrusive Fill-State Detection for Liquid-Freight Containers Based on Vibration Signals.

Remote, automated querying of fill-states of liquid-freight containers can significantly boost the operational efficiency of rail- and storage-yards. Most existing methods for fill-state detection are intrusive, or require sophisticated instrumentation and specific testing conditions, making them unsuitable here, due to the noisy and changeable surroundings and restricted access to the interior. We present a non-intrusive system that exploits the influence of the fill-state on the container’s response to an external excitation. Using a solenoid and accelerometer mounted on the exterior wall of the container, to generate pulsed excitation and to measure the container response, the fill-state can be detected. The decision can be either a binary (empty/non-empty) label or a (quantised) prediction of the liquid level. We also investigate the choice of the signal features for the detection/classification, and the placement of the sensor and actuator. Experiments conducted in real settings validate the algorithms and the prototypes. Results show that the placement of the sensor and actuator along the base of the container is the best in terms of detection accuracy. In terms of signal features, linear predictive cepstral coefficients possess sufficient discriminative information. The prediction accuracy is 100% for binary classification and exceeds 80% for quantised level prediction.

Acoustic and Linguistic Features of Impromptu Speech and Their Association With Anxiety: Validation Study.

BackgroundThe measurement and monitoring of generalized anxiety disorder requires frequent interaction with psychiatrists or psychologists. Access to mental health professionals is often difficult because of high costs or insufficient availability. The ability to assess generalized anxiety disorder passively and at frequent intervals could be a useful complement to conventional treatment and help with relapse monitoring. Prior work suggests that higher anxiety levels are associated with features of human speech. As such, monitoring speech using personal smartphones or other wearable devices may be a means to achieve passive anxiety monitoring.ObjectiveThis study aims to validate the association of previously suggested acoustic and linguistic features of speech with anxiety severity.MethodsA large number of participants (n=2000) were recruited and participated in a single web-based study session. Participants completed the Generalized Anxiety Disorder 7-item scale assessment and provided an impromptu speech sample in response to a modified version of the Trier Social Stress Test. Acoustic and linguistic speech features were a priori selected based on the existing speech and anxiety literature, along with related features. Associations between speech features and anxiety levels were assessed using age and personal income as covariates.ResultsWord count and speaking duration were negatively correlated with anxiety scores (r=–0.12; P<.001), indicating that participants with higher anxiety scores spoke less. Several acoustic features were also significantly (P<.05) associated with anxiety, including the mel-frequency cepstral coefficients, linear prediction cepstral coefficients, shimmer, fundamental frequency, and first formant. In contrast to previous literature, second and third formant, jitter, and zero crossing rate for the z score of the power spectral density acoustic features were not significantly associated with anxiety. Linguistic features, including negative-emotion words, were also associated with anxiety (r=0.10; P<.001). In addition, some linguistic relationships were sex dependent. For example, the count of words related to power was positively associated with anxiety in women (r=0.07; P=.03), whereas it was negatively associated with anxiety in men (r=–0.09; P=.01).ConclusionsBoth acoustic and linguistic speech measures are associated with anxiety scores. The amount of speech, acoustic quality of speech, and gender-specific linguistic characteristics of speech may be useful as part of a system to screen for anxiety, detect relapse, or monitor treatment.

Interdisciplinary Collaboration Between Engineering and Nursing On Baby Crying Analyzing and Classification

This study aims to reveal a multidisciplinary study on analysis and signal processing on infant crying in the field of engineering and nursing. It is a known fact that babies report all their needs with crying behavior. It is often very difficult for those responsible for the baby to determine the needs of the baby with this crying behavior. It is of great importance for the comfort of the baby that the parents can accurately predict the crying behavior and needs of the babies. For this reason, the analysis of the sound signals produced by babies during crying behavior is an interesting subject in the field of engineering. In the literature, proposed approaches capture the baby's cry signal and extract a unique set of features from this signal using Mel Frequency Cepstral Coefficients, Linear Predictive Cepstral Coefficients, and pitch. This feature set is used to distinguish between partner signals to recognize the causes of crying. Furthermore, this classification is used to represent different classes of causes of crying, such as hunger, pain, sleep, and discomfort. As a result, in this study, the clinical analysis of infant crying behaviors was examined and optimum solutions were evaluated in terms of engineering. Thus, new approaches have been tried to be brought by analyzing artificial intelligence-based sound analysis systematics.

Heart Valve Diseases Detection Based on Feature-Fusion and Hierarchical LSTM Network

Early detection and diagnosis of heart valve diseases (HVDs) can prevent cardiac arrest. This work proposes a novel feature fusion method for detecting HVDs using a Phonocardiogram (PCG) signal. In the proposed method, first, the raw PCG signal is pre-processed. Then, two feature fusion models are proposed based on mel-frequency cepstral coefficients (MFCC) and linear prediction cepstral coefficients (LPCC). One model is the series feature fusion (SFF) model. Another is the parallel feature fusion (PFF) model. We propose a hierarchical long-short term memory (HLSTM) network with a self-attention mechanism for both models. HLSTM encodes the sequential information of the fused features in different abstractions. Then, the self-attention module aggregates these encoded vectors based on their clinical relevance to detect HVDs. The efficacy of the proposed method is evaluated using two publicly available databases offered by Physionet challenge (CinC) 2016 and the GitHub repository. The CinC 2016 database is used for binary classification. The results show an overall accuracy (OA) of 98.76%and 98.29%for binary classification using SFF-HLSTM and PFF-HLSTM models. The heart sound murmur (HSM) database in the GitHub repository is used for multi-class classification. The results show an OA of 99.10 % and 98.71 % for multi-class classification using the SFF-HLSTM and PFF-HLSTM models, respectively. The promising results show that the proposed method is compelling enough for preliminary automated diagnosis of HVDs in cardiac care units.