Zero-crossing Rate Research Articles

Emergency Vehicle Classification Using Combined Temporal and Spectral Audio Features with Machine Learning Algorithms

This study presents a novel approach to emergency vehicle classification that leverages a comprehensive set of informative audio features to distinguish between ambulance sirens, fire truck sirens, and traffic noise. A unique contribution lies in combining time domain features, including root mean square (RMS) and zero-crossing rate, to capture the temporal characteristics, like signal energy changes, with frequency domain features derived from short-time Fourier transform (STFT). These include spectral centroid, spectral bandwidth, and spectral roll-off, providing insights into the sound’s frequency content for differentiating siren patterns from traffic noise. Additionally, Mel-frequency cepstral coefficients (MFCCs) are incorporated to capture the human-like auditory perception of the spectral information. This combination captures both temporal and spectral characteristics of the audio signals, enhancing the model’s ability to discriminate between emergency vehicles and traffic noise compared to using features from a single domain. A significant contribution of this study is the integration of data augmentation techniques that replicate real-world conditions, including the Doppler effect and noise environment considerations. This study further investigates the effectiveness of different machine learning algorithms applied to the extracted features, performing a comparative analysis to determine the most effective classifier for this task. This analysis reveals that the support vector machine (SVM) achieves the highest accuracy of 99.5%, followed by random forest (RF) and k-nearest neighbors (KNNs) at 98.5%, while AdaBoost lags at 96.0% and long short-term memory (LSTM) has an accuracy of 93%. We also demonstrate the effectiveness of a stacked ensemble classifier, and utilizing these base learners achieves an accuracy of 99.5%. Furthermore, this study conducted leave-one-out cross-validation (LOOCV) to validate the results, with SVM and RF achieving accuracies of 98.5%, followed by KNN and AdaBoost, which are 97.0% and 90.5%. These findings indicate the superior performance of advanced ML techniques in emergency vehicle classification.

Toward Wearables for Bruxism Detection: Voluntary Oral Behaviors Sound Recorded Across the Head Depend on Transducer Placement.

Bruxism is a parafunctional orofacial behavior. For diagnosis, wearable devices that use sounds as biomarkers can be applied to provide the necessary information. Human beings emit various verbal and nonverbal sounds, making it challenging to identify bruxism-induced sounds. We wanted to investigate whether the acoustic emissions of different oral behaviors have distinctive characteristics and if the placement of the transducer has an impact on recording the sound signals. Sounds from five oral behaviors were investigated: jaw clenching, teeth grinding, reading, eating, and drinking. Eight transducers were used; six were attached to the temporal, frontal, and zygomatic bones with the aid of medical tape, and two were integrated into two commercial earphones. The data from 15 participants were analyzed using time-domain energy, spectral flux, and zero crossing rate (ZCR). In summary, all oral behaviors showed distinct characteristic features except jaw clenching, though there was a peak in the recording, possibly due to tooth tapping, before its expected onset. For teeth grinding, the transducer placement did not have a significant impact (p > 0.05) based on energy, spectral flux, and ZCR. For jaw clenching, the transducer placement had an impact with regard to spectral flux (p < 0.01). For reading and eating, the transducer placement had a significant impact with regard to energy (p < 0.05 for reading, p < 0.01 for eating), spectral flux (p < 0.001 for reading, p < 0.01 for eating), and ZCR (p < 0.001 for both reading and eating). For drinking, the transducer placement only had a significant impact with regard to ZCR (p < 0.01). We were able to record the sounds of various oral behaviors from different locations on the head. However, the ears were an advantageous location to place the transducer, since they could compensate for various head movements and ear devices are socially tolerable.

A Novel Short-Term PM2.5 Forecasting Approach Using Secondary Decomposition and a Hybrid Deep Learning Model

PM2.5 pollution poses an important threat to the atmospheric environment and human health. To precisely forecast PM2.5 concentration, this study presents an innovative combined model: EMD-SE-GWO-VMD-ZCR-CNN-LSTM. First, empirical mode decomposition (EMD) is used to decompose PM2.5, and sample entropy (SE) is used to assess the subsequence complexity. Secondly, the hyperparameters of variational mode decomposition (VMD) are optimized by Gray Wolf Optimization (GWO) algorithm, and the complex subsequences are decomposed twice. Next, the sequences are divided into high-frequency and low-frequency parts by using the zero crossing rate (ZCR); the high-frequency sequences are predicted by a convolutional neural network (CNN), and the low-frequency sequences are predicted by a long short-term memory network (LSTM). Finally, the predicted values of the high-frequency and low-frequency sequences are reconstructed to obtain the final results. The experiment was conducted based on the data of 1009A, 1010A, and 1011A from three air quality monitoring stations in the Beijing area. The results indicate that the R2 value of the designed model increased by 2.63%, 0.59%, and 1.88% on average in the three air quality monitoring stations, respectively, compared with the other single model and the mixed model, which verified the significant advantages of the proposed model.

Multifeature Fusion Method with Metaheuristic Optimization for Automated Voice Pathology Detection

Voice pathologies occur due to various factors, such as malfunction of the vocal cords. Computerized acoustic examination-based vocal pathology detection is crucial for early diagnosis, efficient follow-up, and improving problematic speech. Different acoustic measurements provide it. Executing this process requires expert monitoring and is not preferred by patients because it is time-consuming and costly. This paper is aimed at detecting metaheuristic-based automatic voice pathology. First, feature maps of ten common diseases, including cordectomy, dysphonia, front lateral partial resection, contact pachyderma, laryngitis, lukoplakia, pure breath, recurrent laryngeal paralysis, vocal fold polyp, and vox senilis, were obtained from the Zero-Crossing Rate (ZCR), Root-Mean-Square Energy (RMSE), and Mel-frequency Cepstral Coefficients (MFCCs) using a thousand voice signals from the Saarbruecken Voice Database (SVD) dataset. Hybridizations of different features obtained from the voices of the same diseases using these three methods were used to increase the model's performance. The Grey Wolf Optimizer (MELGWO) algorithm based on local search, evolutionary operator, and concatenated feature maps derived from various approaches was employed to minimize the number of features, implement the models faster, and produce the best result. The fitness values of the metaheuristic algorithms were then determined using supervised machine learning techniques such as Support Vector Machine (SVM) and K-nearest neighbors (KNN). The F1 score, sensitivity, specificity, accuracy, and other assessment criteria were compared with the experimental data. The best accuracy result was achieved with 99.50% from the SVM classifier using the feature maps optimized by the improved MELGWO algorithms.

Novel combustion instability diagnosis method in a hydrogen/natural gas co-firing gas turbine combustor using a combination of four criteria: Temporal kurtosis, permutation entropy, energy of entropy, and zero-crossing rate

In this study, we developed a diagnosis method for combustion instability and validated its performance using experimental data obtained from a hydrogen/natural gas co-firing gas turbine combustor. Four criteria, namely temporal kurtosis, permutation entropy, energy entropy, and zero-crossing rate, were considered. First, the performance of each criterion was compared with that of the conventional method for determining combustion instability using the root mean square (RMS) of dynamic pressure. Temporal kurtosis the best performance in terms of detection speed (instability onset detected 0.71 s earlier than the RMS method). Permutation entropy yielded the best performance in terms of accuracy and precision with accuracy, recall, precision, and F1-score values of 96.6%, 100%, 92.6%, and 96.2%, respectively. The performances of combinations of these criteria were investigated using AND and OR gates. Twenty-two cases were considered and the combination of permutation entropy and zero-crossing rate (best case) achieved detection time, accuracy, precision, recall, and F1-score values of 8.49 s, 97.2%, 93.9%, 100%, and 96.8%, respectively. Other combinations exhibited competitive performance; however, the method combining two criteria was selected to simplify the computing process. The proposed method is expected to contribute to the safe and stable operation of hydrogen/natural gas co-firing gas turbines.

CNC Mechanical Machine and Musical Sound Analysis of Zero Crossing Rates (ZCR) by Artificial Intelligence Based Tools.

CNC Mechanical Machine and Musical Sound Analysis of Zero Crossing Rates (ZCR) by Artificial Intelligence Based Tools.

Speech emotion recognition based on multi-feature speed rate and LSTM

Correctly recognizing speech emotions is of significant importance in various fields, such as healthcare and human–computer interaction (HCI). However, the complexity of speech signal features poses challenges for speech emotion recognition. This study introduces a novel multi-feature method for speech emotion recognition that combines short-and rhythmic features. Utilizing short-time energy, zero-crossing rate, and average amplitude difference, the proposed approach effectively addressed overfitting concerns by reducing feature dimensionality. Employing an (LSTM) network, the experiment achieved notable accuracy across diverse datasets. Specifically, the proposed method achieved an impressive accuracy of up to 98.47% on the CASIA dataset, 100% on the Emo-DB dataset, and 98.87% on the EMOVO dataset, demonstrating its capability to accurately discern speaker emotions across different languages and emotion classes. These findings underscore the significance of incorporating speech rate for emotional content recognition, which holds promise for application in HCI and auxiliary medical diagnostics.

Failure analysis in predictive maintenance: Belt drive diagnostics with expert systems and Taguchi method for unconventional vibration features

Predictive maintenance to avoid fatigue and failure enhances the reliability of mechanics, herewith, this paper explores vibrational time-domain data in advancing fault diagnosis of predictive maintenance. This study leveraged a belt-drive system with the properties: operating rotational speeds of 500–2000 RPM, belt pretensions at 70 and 150 N, and three operational cases of healthy, faulty and unbalanced, which leads to 12 studied cases. In this analysis, two one-axis piezoelectric accelerometers were utilized to capture vibration signals near the driver and pulley. Five advanced statistics were calculated during signal processing, namely Variance, Mean Absolute Deviation (MAD), Zero Crossing Rate (ZCR), Autocorrelation Coefficient, and the signal's Energy. The Taguchi method was used to test the five selected features on the basis of Signal-to-Noise (S/N) ratio. For classifications, an expert system was used based on artificial intelligence where a Random Forest (RF) model was trained on untraditional parameters for optimizing the accuracy. The resulted 0.990 and 0.999, accuracy and AUC, demonstrate the RF model's high dependability. Evidently, the methodology highlights the features potential when progressed into expert systems, which advances predictive maintenance strategies for belt-drive systems.

The Impact of Contact Force on Signal Quality Indices in Photoplethysmography Measurements

Photoplethysmography (PPG) is widely used to assess cardiovascular health. Yet, its effectiveness is often hindered by external factors like contact force (CF), which significantly affects the accuracy and reliability of measurements. This study investigates how variations in the CF at the index fingertips influence six signal quality indices (SQIs)—including the perfusion index, skewness, kurtosis, entropy, zero-crossing rate, and relative power—using data from 11 healthy participants. Our analysis of normalized CF values reveals that lower CF ranges (0.2 to 0.4) may be optimal for extracting information about perfusion and blood flow. However, they may not be the best range to capture all the physiological details within the PPG pulse. In contrast, higher CF ranges (0.4 to 0.6) enable capturing more complex signals that could be physiologically representative. The findings underscore the necessity of considering viscoelastic tissue properties and individual biomechanical differences, advocating for both the normalization of CF for improved cross-subject comparison and personalized CF calibration to adapt PPG devices to diverse populations. These strategies ensure measurement reliability and consistency, thereby advancing the accuracy of cardiac and vascular assessments. Our study offers guidelines for adjusting the CF levels to balance signal detail and perfusion quality, customized to meet specific analytical requirements, with direct implications for both clinical and research environments.

Assessment of Pepper Robot's Speech Recognition System through the Lens of Machine Learning.

Speech comprehension can be challenging due to multiple factors, causing inconvenience for both the speaker and the listener. In such situations, using a humanoid robot, Pepper, can be beneficial as it can display the corresponding text on its screen. However, prior to that, it is essential to carefully assess the accuracy of the audio recordings captured by Pepper. Therefore, in this study, an experiment is conducted with eight participants with the primary objective of examining Pepper's speech recognition system with the help of audio features such as Mel-Frequency Cepstral Coefficients, spectral centroid, spectral flatness, the Zero-Crossing Rate, pitch, and energy. Furthermore, the K-means algorithm was employed to create clusters based on these features with the aim of selecting the most suitable cluster with the help of the speech-to-text conversion tool Whisper. The selection of the best cluster is accomplished by finding the maximum accuracy data points lying in a cluster. A criterion of discarding data points with values of WER above 0.3 is imposed to achieve this. The findings of this study suggest that a distance of up to one meter from the humanoid robot Pepper is suitable for capturing the best speech recordings. In contrast, age and gender do not influence the accuracy of recorded speech. The proposed system will provide a significant strength in settings where subtitles are required to improve the comprehension of spoken statements.

Combining Transformer, Convolutional Neural Network, and Long Short-Term Memory Architectures: A Novel Ensemble Learning Technique That Leverages Multi-Acoustic Features for Speech Emotion Recognition in Distance Education Classrooms

Speech emotion recognition (SER) is a technology that can be applied to distance education to analyze speech patterns and evaluate speakers’ emotional states in real time. It provides valuable insights and can be used to enhance students’ learning experiences by enabling the assessment of their instructors’ emotional stability, a factor that significantly impacts the effectiveness of information delivery. Students demonstrate different engagement levels during learning activities, and assessing this engagement is important for controlling the learning process and improving e-learning systems. An important aspect that may influence student engagement is their instructors’ emotional state. Accordingly, this study used deep learning techniques to create an automated system for recognizing instructors’ emotions in their speech when delivering distance learning. This methodology entailed integrating transformer, convolutional neural network, and long short-term memory architectures into an ensemble to enhance the SER. Feature extraction from audio data used Mel-frequency cepstral coefficients; chroma; a Mel spectrogram; the zero-crossing rate; spectral contrast, centroid, bandwidth, and roll-off; and the root-mean square, with subsequent optimization processes such as adding noise, conducting time stretching, and shifting the audio data. Several transformer blocks were incorporated, and a multi-head self-attention mechanism was employed to identify the relationships between the input sequence segments. The preprocessing and data augmentation methodologies significantly enhanced the precision of the results, with accuracy rates of 96.3%, 99.86%, 96.5%, and 85.3% for the Ryerson Audio–Visual Database of Emotional Speech and Song, Berlin Database of Emotional Speech, Surrey Audio–Visual Expressed Emotion, and Interactive Emotional Dyadic Motion Capture datasets, respectively. Furthermore, it achieved 83% accuracy on another dataset created for this study, the Saudi Higher-Education Instructor Emotions dataset. The results demonstrate the considerable accuracy of this model in detecting emotions in speech data across different languages and datasets.

Feature fusion method for pulmonary tuberculosis patient detection based on cough sound.

Since the COVID-19, cough sounds have been widely used for screening purposes. Intelligent analysis techniques have proven to be effective in detecting respiratory diseases. In 2021, there were up to 10 million TB-infected patients worldwide, with an annual growth rate of 4.5%. Most of the patients were from economically underdeveloped regions and countries. The PPD test, a common screening method in the community, has a sensitivity of as low as 77%. Although IGRA and Xpert MTB/RIF offer high specificity and sensitivity, their cost makes them less accessible. In this study, we proposed a feature fusion model-based cough sound classification method for primary TB screening in communities. Data were collected from hospitals using smart phones, including 230 cough sounds from 70 patients with TB and 226 cough sounds from 74 healthy subjects. We employed Bi-LSTM and Bi-GRU recurrent neural networks to analyze five traditional feature sets including the Mel frequency cepstrum coefficient (MFCC), zero-crossing rate (ZCR), short-time energy, root mean square, and chroma_cens. The incorporation of features extracted from the speech spectrogram by 2D convolution training into the Bi-LSTM model enhanced the classification results. With traditional futures, the best TB patient detection result was achieved with the Bi-LSTM model, with 93.99% accuracy, 93.93% specificity, and 92.39% sensitivity. When combined with a speech spectrogram, the classification results showed 96.33% accuracy, 94.99% specificity, and 98.13% sensitivity. Our findings underscore that traditional features and deep features have good complementarity when fused using Bi LSTM modelling, which outperforms existing PPD detection methods in terms of both efficiency and accuracy.

Marathi Speech Emotion recognition using Deep Learning techniques.

Marathi Speech Emotion recognition using Deep Learning techniques.

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.

Sex identification of ducklings based on acoustic signals

Sex identification of ducklings is a critical step in the poultry farming industry, and accurate sex identification is beneficial for precise breeding and cost savings. In this study, a method for identifying the sex of ducklings based on acoustic signals was proposed. In the first step, duckling vocalizations were collected and an improved spectral subtraction method and high-pass filtering were applied to reduce the influence of noise. Then, duckling vocalizations were automatically detected by using a double-threshold endpoint detection method with three parameters: short-time energy (STE), short-time zero-crossing rate (ZCR), and duration (D). Following the extraction of Mel-Spectrogram features from duckling vocalizations, an improved Res2Net deep learning algorithm was used for sex classification. This algorithm was introduced with the Squeeze-and-Excitation (SE) attention mechanism and Ghost module to improve the bottleneck of Res2Net, thereby improving the model accuracy and reducing the number of parameters. The ablative experimental results showed that the introduction of the SE attention mechanism improved the model accuracy by 2.01%, while the Ghost module reduced the number of model parameters by 7.26M and the FLOPs by 0.85G. Moreover, this algorithm was compared with five state-of-the-art (SOTA) algorithms, and the results showed that the proposed algorithm has the best cost-effectiveness, with accuracy, recall, specificity, number of parameters, and FLOPs of 94.80%, 94.92%, 94.69%, 18.91M, and 3.46G, respectively. After that, the vocalization detection score and the average confidence strategy were used to predict the sex of individual ducklings, and the accuracy of the proposed model reached 96.67%. In conclusion, the method proposed in this study can effectively detect the sex of ducklings and serve as a reference for automated sex identification of ducklings.

Detecting wire breaks in prestressed concrete pipes: an easy-to-install distributed fibre acoustic sensing approach

The escalating water stress resulting from drought conditions in certain global regions underscores the imperative to minimize water losses, particularly within drinking water supply networks. One way to achieve this is by improving pipe monitoring systems to allow the early detection of possible structural collapse of the pipes. One type of pipe widely used in water mains is the prestressed concrete pipe, whose main cause of structural failure is the breakage of prestressing wires. This research paper analyses the ability of an easy-to-install distributed acoustic sensing (DAS) monitoring system using fibre optics to identify and locate the acoustic signal produced by the wire breaks in prestressed concrete pipes to make early detection of possible structural failures. For this purpose, a large experimental pipeline stretch was built (approximately 1 m in diameter and 40 m long) where wire breaks were simulated. Several variables were studied: the origin of the signal (to distinguish wire breaks from events of a similar nature), the location of the event in the pipe, the presence of background noise, the internal water pressure, the length of the prestressed wire not subject to bonding with the concrete and the presence of water in the pipe. The results showed that the DAS system could detect almost all events. In addition, two of the multiple parameters measured in the signals, the zero-crossing rate and the short-time energy, made it possible to precisely determine the signal’s origin and the event’s location. Another parameter measured, the duration of the signal in this case, made it possible to differentiate whether the events had occurred when the pipe was empty or full of water. These and other results in this paper present a highly promising perspective on using this DAS system in water main monitoring.

Internet of Bio-NanoThings privacy: securing a multi compartmental targeted cancer drug delivery scheme

The Internet of Bio-Nano Things concept (IoBNT) emerged from the need to establish connections between biological nanomachines, the intra-body nanonetwork, and the cyber internet to facilitate information exchange. While extensive research has concentrated on optimizing communication efficiency among nanodevices within networks, challenges such as IoBNT security and the interface linking nanonetwork to the internet have remained unaddressed. Consequently, this study introduces a privacy scheme designed to operate atop the Physical Cyber Interface (pHCI) within the IoBNT framework. Our proposed chaotic system derives its foundation from the command signals issued by medical personnel to pHCI devices implanted within the human body. It employs a concealed version of features generated through a Modified Quadratic Map (MQM) to enhance the privacy of patient information and to ensure a precise dosage release. Additionally, our scheme incorporates Binary Phase Shifting Key (BPSK) modulation through the incorporation of a carrier wave, along with feature extraction with zero-crossing rates. This privacy scheme significantly amplifies the key space, thereby guaranteeing an accurate right dose release with the protection of patient privacy. To assess the performance of our proposed scheme, we evaluate its operation on top of the pHCI device using various performance metrics. Subsequently, we study its performance by employing multi-compartmental models in both the forward and reverse pHCI directions of the IoBNT paradigm. The results from our simulation model clearly illustrate that the IoBNT-based privacy scheme has potential to enhance the delivery of therapeutic drugs to target cells while effectively addressing privacy concerns. An evaluation of performance metrics for two binary codes (thermal and light) reveals sensitivity and specificity rates of 95.333% and 95%, 100%, and 100%, respectively. Furthermore, the performance of our proposed privacy scheme, as measured by EER, accuracy, NPV, and PPV, has proven to be highly satisfactory. Hence, our proposed scheme makes significant role in enhancing the security of the physical cyber interface device while remaining cost-effective, and ensuring the safety of patients' life and confidentiality.

Effective modelling of human expressive states from voice by adaptively tuning the neuro-fuzzy inference system

&lt;span lang="EN-US"&gt;This paper aims to develop efficient speech-expressive models using the adaptively tuning neuro-fuzzy inference system (ANFIS). The developed models differentiate a high-arousal happiness state from a low-arousal sadness state from the benchmark Berlin (EMODB) database. The proposed low-cost flexible developed algorithms are self-tunable and can address several vivid real-world issues such as home tutoring, banking, and finance sectors, criminal investigations, psychological studies, call centers, cognitive and biomedical sciences. The work develops the proposed structures by formulating several novel feature vectors comprising both time and frequency information. The features considered are pitch (F0), the standard deviation of pitch (SDF0), autocorrelation coefficient (AC), log-energy (E), jitter, shimmer, harmonic to noise ratio (HNR), spectral centroid (SC), spectral roll-off (SR), spectral flux (SF), and zero-crossing rate (ZCR). to alleviate the issues of the curse of dimensionality associated with the frame-level extraction, the features are extracted at the utterance level. Several performance parameters have been computed to validate the individual time and frequency models. Further, the ANFIS models are tested for their efficacy in a combinational platform. The chosen features are complementary and the augmented vectors have indeed shown improved performance with more available information as revealed by our results.&lt;/span&gt;

Machine learning-based infant crying interpretation.

Crying is an inevitable character trait that occurs throughout the growth of infants, under conditions where the caregiver may have difficulty interpreting the underlying cause of the cry. Crying can be treated as an audio signal that carries a message about the infant's state, such as discomfort, hunger, and sickness. The primary infant caregiver requires traditional ways of understanding these feelings. Failing to understand them correctly can cause severe problems. Several methods attempt to solve this problem; however, proper audio feature representation and classifiers are necessary for better results. This study uses time-, frequency-, and time-frequency-domain feature representations to gain in-depth information from the data. The time-domain features include zero-crossing rate (ZCR) and root mean square (RMS), the frequency-domain feature includes the Mel-spectrogram, and the time-frequency-domain feature includes Mel-frequency cepstral coefficients (MFCCs). Moreover, time-series imaging algorithms are applied to transform 20 MFCC features into images using different algorithms: Gramian angular difference fields, Gramian angular summation fields, Markov transition fields, recurrence plots, and RGB GAF. Then, these features are provided to different machine learning classifiers, such as decision tree, random forest, K nearest neighbors, and bagging. The use of MFCCs, ZCR, and RMS as features achieved high performance, outperforming state of the art (SOTA). Optimal parameters are found via the grid search method using 10-fold cross-validation. Our MFCC-based random forest (RF) classifier approach achieved an accuracy of 96.39%, outperforming SOTA, the scalogram-based shuffleNet classifier, which had an accuracy of 95.17%.

Speech Emotion Recognition Model Using Deep Learning

Speech has long been recognized as the main form of communication between people and computers. Technology made it possible for humans and computers to interact through the development of human-computer interfaces. Although speech emotion recognition systems have advanced quickly in recent years, many difficulties have also arisen during this development, such as the inability to recognize emotions that lead to depression and mood swings, which can be used by therapists to track their patients' moods. It is necessary to create a model that detects the many emotions that contribute to depression to improve doctor-patient relationships and increase the effectiveness of spoken emotion recognition models. In this paper, over 2000 audio files were compiled. We curated a local dataset that accounts for 60% of the total dataset acquired, 40% of the dataset used was obtained from RAVDESS. To extract the proper vocal features, we employed the Mel-Frequency Cepstral Coefficients (MFCC), Zero Crossing Rate (ZCR), and Root Mean Square (RMS). The Tensorflow CONV1D with Relu activation function and several sequential layers was used to build the model. The batch size was 64 and the epoch size was 50. Seven emotional states, including anger, disgust, sadness, happiness, surprise, fear, and neutrality were extracted. The accuracy of the confusion matrix, which served as the performance metrics, is 96%.