Heartbeat Audio Download

For example, player needs to reduce insanity so he needs to do the heartbeat game to reduce it. When event started, player need to press the correct input of button which have 3 stages. Visual and sound will be blurry and heartbeat sound is the fastest. As player click the correct button the visual and sound which were loud earlier became more clearer and no sound of heartbeat played anymore.

Heartbeat sound classification usually comprises three steps. The first step is a pre-processing that cleans the heartbeat signal by passing a band-pass filter to eliminate the noise. The second step is a feature extraction technique that transforms each heartbeat sound signal into a fixed-sized heartbeat sound signal. Then, the Down-sampling [5,6] technique is applied that reduces the heartbeat sound signal frames. However, it helps us to decrease the computation time of our system without affecting the performance of our algorithm performance. The last step is to choose a suitable classifier that extracted features and complete classification tasks. In machine learning, classification algorithms used to classify new data by learns from previous data. Classification algorithms used in this study are Decision Tree (DT), Random Forest (RF) and Linear Support Vector Classifier (LSVC). Deep learning is a subset of Machine Learning. The state-of-the-art methods used in Deep learning algorithms are Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN).

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Elsa Ferreira Gomes et al. [4] describe a methodology for classifying heart sound PASCAL Challenge. They used an algorithm that identified the S1 and S2 heart sound where S1 is lub and S2 is dub. First, they applied the decimate function of MATLAB on the original sound signal and applied a band-pass filter to remove the noise in these signals. After that, they applied the average Shannon energy that is useful to identify the peaks of the heart sound signal easily. They used an algorithm in which they find the maxima and minima points of the sound signal to accomplished the segmentation of heartbeat sound. They used the J48 and MLP algorithm to train the model that predicts the sound signal. These signals are Normal, Murmur, Extra-sound and Artifact.

Wenjie Zhang et al. [10] purpose a method to extract the discriminative feature of heartbeat sound classification by using scaled spectrogram and tensor decomposition. The spectrograms detected the heart cycles of fixed size to extract the discriminative feature. Tensor decomposition is used to reduce the dimension to get a more discriminative feature. After that, they applied the Support Vector Machine (SVM) to these features. The SVM model that gets the normalized precision is 0.74.

Yaseen et al. [11] used multiple feature extraction techniques for heartbeat sound classification. They used an electronics stethoscopes device to get the digital recording of the heart sound called a phonocardiogram (PCG). Mel Frequency Cepstral Coefficient (MFCCs) and Discrete Wavelets Transform (DWT) are the features extraction techniques used in this study. Furthermore, they combine the MFCCs and DWT feature extraction to enhance the results. They used various classification algorithms in this study, such as Support Vector Machine (SVM), Deep Neural Network and K-Nearest Neighbor (KNN).

Many researchers usually involve three steps of heartbeat sound classification. The first step is heart sound segmentation that detects by amplitude threshold-based method [12,13,14] and probabilistic- based method [15,16]. The second step is feature extraction based on time [17], frequency [18] and time-frequency [10,17,19] and the last step is the classification model. The commonly classification model used is SVM [8,9,10] and MLP [7] as shown in Table 1.

Deep Neural Network is the most emerging field in data mining. Deep learning is the subset of machine learning that used different types of layers to perform different tasks on data. These tasks are object detection and voice recognition. Many researchers have worked on deep neural networks to perform classification on the audio dataset.

Shawn et al. [20] have worked on a large-scale audio dataset and proved that its results are good by using Convolutional Neural Network. Yong Xu et al. [21] presented a gated Convolutional Neural Network that won the 1st place in the large-scale weakly supervised sound event detection and Classification of Acoustic Scenes and Events (DCASE) 2017 challenge. Yan Xiong Li et al. [22] used the BLSTM Network on Acoustic Scenes to get a better result. Kele Xu et al. [23] purpose a novel ensemble-learning system consists of CNN that gets a superior classification performance.

The main framework of heartbeat sound classification has divided into two sections. Sections (a) and (b) are the training and test phases shown in Figure 2. In the training phase, the data insert as a signal and label then applied the pre-processing technique on these training data and applied feature extraction technique based on data framing and down-sampling. In the last, we have trained the purposed model.

A normal heartbeat sounds like e.g., lub dub or dub lub, and a murmur heartbeat sound has a noise between dub to lub or lub to dub and extra-systole heart sound is out of beats e.g., lub-lub dub or lub dub-dub [4]. These heartbeats sound waves are shown in Figure 3.

In this study classification algorithm applies to the Dataset-B. Decision Tree (DT) and Random Forest (RF) is the Machine learning algorithm applied to heartbeat sound dataset to classify the heartbeat diseases. Decision Tree is the earlier classification algorithm used in a diverse area of classification. Random forest is an ensemble learning method used for text classification and generating random decision tree [31]. These algorithms perform well on categorical and text datasets. Their performance on voice and image dataset is unsatisfied. As a result, DT and RF have gained less accuracy displayed in Table 4 as compare to the neural network.

Deep learning is an artificial neural network that acts like a human brain to learn and make a decision on its own. For classifying audio and image datasets, authors [20,21,32,33] prefers the deep learning algorithm and their performance very well. Deep learning is the subset of machine learning, as well as machine learning, which is a subset of artificial intelligence. In deep learning, data passes through each layer. That is the output of the previous layer and input to its next layer. The first and last layer is called the input and output layer. Another layer between the input and output layer is considered to be a hidden layer in which each layer performs an activation function. The deep learning algorithm extracts the discriminative feature itself but extracts the feature separately in the machine learning algorithm. The deep learning algorithm extracts the feature itself. However, machine learning needs to extracts the feature separately.

In healthy adults, there are two normal heart sounds, often described as a lub and a dub that occur in sequence with each heartbeat. These are the first heart sound (S1) and second heart sound (S2),produced by the closing of the atrioventricular valves and semilunar valves, respectively. In addition to these normal sounds, a variety of other sounds may be present including heart murmurs, adventitious sounds, and gallop rhythms S3 and S4.

Over the past couple days I've been getting this glitch where the heartbeat sound, when you are very low on health, doesn't stop even after I heal. The only thing that stops it is logging out (to switch characters) or restarting my xbox.

Instrumentation

The Audi Heartbeat is based on a real human heartbeat. The heartbeat is a long-standing acoustic trademark closely associated with Audi and conveys the emotional nature of the brand. It is accompanied by instruments from the Audi Sound Studio, thereby creating the unmistakable Audi Heartbeat.

The audio recordings on these CDs must only be used for public playback in association with the Audi brand. Music playback must be registered with the relevant collecting society beforehand and a fee is payable to this society. Please ensure that you contact the relevant institution in your country in good time.

The term "fetal heartbeat," as used in the anti-abortion law in Texas, is misleading and not based on science, say physicians who specialize in reproductive health. What the ultrasound machine detects in an embryo at six weeks of pregnancy is actually just electrical activity from cells that aren't yet a heart. And the sound that you "hear" is actually manufactured by the ultrasound machine. Scott Olson/Getty Images hide caption

The Texas abortion law that went into effect last fall reads: "A physician may not knowingly perform or induce an abortion on a pregnant woman if the physician detected a fetal heartbeat for the unborn child."

The law defines "fetal heartbeat" as "cardiac activity or the steady and repetitive rhythmic contraction of the fetal heart within the gestational sac" and claims that a pregnant woman could use that signal to determine "the likelihood of her unborn child surviving to full-term birth."

Later in a pregnancy is when a clinician might use the term "fetal heartbeat," after the sound of the heart valves can be heard, she says. That sound "usually can't be heard with our Doppler machines until about 10 weeks."

The term "fetal heartbeat" has been used in laws restricting access to abortion for years. According to the Guttmacher Institute, which tracks reproductive health policy, the first such law was passed in North Dakota in 2013, but it was struck down in the courts. Since then, over a dozen states have passed similar laws, but Texas' is the first to go into effect.

The text of the Texas law claims that "fetal heartbeat has become a key medical predictor that an unborn child will reach live birth" and continues, "the pregnant woman has a compelling interest in knowing the likelihood of her unborn child surviving to full-term birth based on the presence of cardiac activity." e24fc04721