Auto-Tune (or autotune) is an audio processor introduced in 1996 by American company Antares Audio Technologies.[4] Auto-Tune uses a proprietary device to measure and alter pitch in vocal and instrumental music recording and performances.[5]
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Auto-Tune was originally intended to disguise or correct off-key inaccuracies, allowing vocal tracks to be perfectly tuned despite originally being slightly off-pitch. The 1998 Cher song "Believe" popularized the technique of using Auto-Tune to distort vocals. In 2018, the music critic Simon Reynolds observed that Auto-Tune had "revolutionized popular music", calling its use for effects "the fad that just wouldn't fade. Its use is now more entrenched than ever."[6]
According to the Auto-Tune patent, the referred implementation detail simply consists, when processing new samples, of reusing the former autocorrelation bin, and adding the product of the new sample with the older sample corresponding to a lag value, while subtracting the autocorrelation product of the sample that correspondingly got out of window.[5]
Hildebrand had come up with the idea for a vocal pitch correction technology on the suggestion of a colleague's wife, who had joked that she could benefit from a device to help her sing in tune.[14][15] Originally, Auto-Tune was designed to discreetly correct imprecise intonations, in order to make music more expressive, with the original patent asserting that "When voices or instruments are out of tune, the emotional qualities of the performance are lost."[6]
Used by stars from Snoop Dogg and Lil Wayne to Britney Spears and Cher, Auto-Tune has been widely criticized as indicative of an inability to sing on key.[20][42][43][44][45] Trey Parker used Auto-Tune on the South Park song "Gay Fish", and found that he had to sing off-key in order to sound distorted; he claimed, "You had to be a bad singer in order for that thing to actually sound the way it does. If you use it and you sing into it correctly, it doesn't do anything to your voice."[46] Electropop recording artist Kesha has been widely recognized as using excessive Auto-Tune in her songs, putting her vocal talent under scrutiny.[42][47][48][49][50] Music producer Rick Rubin wrote that "Right now, if you listen to pop, everything is in perfect pitch, perfect time and perfect tune. That's how ubiquitous Auto-Tune is."[51] Time journalist Josh Tyrangiel called Auto-Tune "Photoshop for the human voice".[51]
YouTuber Conor Maynard, who has received criticism for his use of Auto-Tune, defended the audio processor in an interview on the Zach Sang Show in 2019, stating: "It doesn't mean you can't sing ... auto-tune can't make anyone who can't sing sound like they can sing ... it just tightens it up ever so slightly because we're human and we are not perfect, whereas [Auto-Tune] is literally digitally perfect".[61][62]
Similar to Graillon 2, it is compatible with the majority of VST as well as AU plugin hosts for PC as well as Mac. It has a resizable user interface (you can adjust the size to any of 75 percent and 200 percent). You can listen to autotuned voice recordings in any kind of music, from top chart-topping singles of the moment to demo tracks from independent artists. This license is usually used in video games and allows players to access and use the games free. In essence, the game is provided for free to play and the player can choose whether or not to spend for additional functions, products, or physical products that increase the capabilities of the game. Alongside keys and scales, Auto-Key also tells you the frequency that you are using as a reference for your music. The majority of modern music is tuned to ensure that A is equal to 440Hz, however, this is not always the case. Get More Softwares From Getintopc
No matter what your requirements for pitch correction The free autotune VSTs that are listed below could replace with the initial Auto-Tune effect created by Antares. The primary purpose of pitch correction is to correct pitch imperfections in vocal recordings.
The feature of pitch correction which was previously only available to Antares Auto-Tune Pro ($399) software is now accessible for everyone due to the free autotune options. The trial software permits users to try out the program for a short duration of time. After that time, the user can decide whether to purchase the software or not. While most trial software programs are limited in time, some have additional limitations on features.
In recent years, volume scanning electron microscopy (SEM) modalities have been used for CLEM on cultured cells. Besides offering access to large volumes, both serial block-face SEM (SBF-SEM; Titze and Genoud, 2016) and array tomography (Hayworth et al., 2015; Kislinger et al., 2020) also require block trimming before imaging and therefore suffer from the same limitations as TEM when utilized for CLEM. Focused ion beam SEM (FIB-SEM; Russell et al., 2017) however can accommodate the imaging of large specimens without the need for trimming. In particular, multiple cultured cells grown on a Petri dish or coverslip can be imaged in a CLEM workflow, even when scattered across the full surface of the substrate (Cosenza et al., 2017). Despite this capability, CLEM has been performed one cell at a time and for a limited number of cells (Narayan and Subramaniam, 2015; Cosenza et al., 2017; Fermie et al., 2018; Luckner and Wanner, 2018b), because up to now, FIB-SEM microscopes lack automation procedures to acquire multiple sites without interruption.
All images are then loaded to CLEMSite-LM. The first step of the software is to automatically extract landmarks that will be used as references to register the stage coordinates coming from LM and EM images. The grid pattern imprinted on the bottom of the culture dish is a convenient coordinate system for registration. As the screened cells are typically distributed across the whole surface of the CLEM dish, a map of local landmarks is built from multiple sparse images of the grid.
When enough landmarks are collected, an affine 2D transformation is computed to register the landmarks from LM and EM. The transformation is applied to all LM stage coordinates of target cells to predict their position in SEM stage coordinates at the surface of the resin block (Fig. 2 d). When all four experiments are taken into consideration, this global transformation reduces the error in target accuracy down to 13 6 µm. If the grid pattern is sharp and the block surface does not present any defects such as cracks, scratches, or dust, grid edges are detected perfectly, and the center point of the landmark can be calculated with higher accuracy (Fig. S2). In our case, we had two such experiments, reaching a global targeting accuracy (RMSE) of 8 5 µm.
Examples of landmark detection on SEM images (SE detector) from surfaces of different samples. Cracks, scratches, and dirt on the surface make landmark detection difficult and more error-prone. For each square, the left image shows the final detection, with the yellow dot representing the detected center position of the crossing and the red points the corners of the crossing. The right image is the same with inverted brightness and contrast, with red pixels representing the probability of being a grid edge as detected by the neural network. The probability map from the neural network is the result of the network inference, with the set of images used during training different from the images used as input during the experiment, which is shown here. We observe that the neural network can generalize very well the detection of the grid patterns in the resin surface. Here we exemplify the common cases that can lead to an error in the detection of a landmark. (a) The sample is in a perfect state. (b) A crack present in the upper part might affect the predicted accuracy of the overall map, even if the detection is identified as good (or close to it). (c) Scratches can be the cause of false positives for the grid detection, in this case, scratches parallel to the grid bar. Even if this specific error was later corrected by taking also into account the length of the line stroke, we presume that longer scratches than the ones shown in the exemplary image could cause the same problem again. (d) In other cases, dirt and other material residues, e.g., from silver painting (used around the sample border to derive charges), might mislead the detection algorithm and increase the final error. The detection problems might change on a sample basis. A detailed analysis of the error detection is shown in the supplementary material in notebook 2 ( _notebooks). Scale bars: all 100 µm.
Schematics of some of the implemented components to achieve FIB-SEM automation and its results. (a) Automated Coincidence Point routine is illustrated schematically. When not tuned, the two beams are usually pointing at different positions of the sample surface (green plane, blue point for FIB center, red point for SEM center). The orange plane below shows the case where the ideal position (yellow point) is achieved for both FIB and SEM beams. In the software routine, a square is sputtered with the ion beam on the sample surface. The offset between the two beams is calculated based on the difference between the center of the sputtered mark in the SEM and FIB images (dy, distance between red and blue positions in the green plane). The z height (dz) of the stage is then corrected, and a further refinement using the SEM beam shift is performed by calculating the translation of the square mark between FIB (50 pA image) and SEM images. (b) Milling & Trench Detection: (1) After finding the coincidence point, a trench is milled to expose a cross-section at the region of interest. (2) The trench is detected to accurately position the field of view. First, three-level thresholding is applied to the image, followed by the detection of the biggest connected component that fits a trapezoid shape. From the final binary shape, boundaries of the trapezoid are found (3): the top corners (red circles), the trapezoid top center (blue circle), and the trapezoid center (light blue circle). (c) Image features detection: The image of the cross-section surface is analyzed and scored for the best focus positions to perform autofocus and autostigmatism. Features inside the image are found by using Harris corner detection and the variance of a small region surrounding each detected corner position. The initial features (red points) highlight the high contrast and complex areas of the imaging surface which usually cluster on cellular structures. Features are clustered and their centroids (green dots) are then filtered and prioritized to detect the first 6 ones suitable for AFAS (blue points). Due to the brightness/contrast settings to make the cell visible well inside the cross-section, the top surface of the sample above the cellular edge, which is covered with a gold coat, is only faintly visible. This region is excluded from the analysis of the cross-section to prevent autofocus outside the proper field of view. (d) Acquired data: Images are acquired at 200 nm intervals (in z) throughout the Golgi apparatus region. The resulting stack is used for 3D render and quantifications. (e) Multi-site images: Result of an experiment, where multiple targets had been acquired automatically across the full surface of the sample. Scale bars: (a) all 50 µm; (b) all 25 µm; (c) 5 µm; (d) slices all 2 µm, model 5 µm; (e) 500, 50 µm. 2ff7e9595c
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