Clone Ensemble is the plug-in for making solo voices and instruments sound like an ensemble. Whatever line you play or sing, Clone Ensemble will generate a room full of up to 32 of you in unison or octaves. For vocals, you can change the sex of some or all of the voices. You can dial up a classical choir, a tight jazz chorus or an incredibly realistic ADT effect. The Clones dial controls how many clones are generated in the ensemble. The range is 1 to 32, and the more you have, the more CPU is used. On a 2GHz P4, 32 clones with a 3-way Sex Machine split (Bass:Natural:Alto) uses a little over 30% of the CPU. For a natural choir sound, the more clones you can afford to run, the better the results. For ADT (automatic double-tracking) a single clone (along with some of the dry signal using the Mix control, below) may suffice. The Timing dial scales the small delays between each of the clones. The ratios are fixed, this dial merely stretches them all. Tight values are best for thickening synth sounds, middle values are usually appropriate for realistic vocal ensembles and acoustic instruments, and loose is a kind of chaotic delay effect. If you are creating a multi-part harmony, the Section control provides four variations in the way the clones are organised - each clone has a different vibrato, timing delay, comb filter and position in the stereo image. The A, B, C and D sections shuffle these attributes, so that if you process each harmony part separately, they won't all end up sounding the same. The next two controls determine how the clones are positioned in the stereo image. Focus controls the spread - they can fill the stereo image evenly from left to right, or you can group them closer together. Balance is similar to a pan control, except that it is controlling the panning of each individual clone rather than the entire mix. Together with the Section control, these two are very useful when processing harmony parts separately. The Mix control lets you balance the dry unprocessed sound with the wet ensemble sound. For a realistic choir, this should be set close to the maximum. For ADT (automatic double-tracking) try somewhere in the middle. The Dry Delay control let you appy a delay to the unprocessed signal, to help it sit in the middle better with all the clones (which of course have varying delays). Finally, the Gain control adjusts the final output volume. Some synth sounds get very loud when lots of clones are active - the peaks add up fast. Vocal however tend to "pack together", and the volume does not build as clones are added - so you might need to boost the levels.
Abstract:Automated pavement crack detection and measurement are important road issues. Agencies have to guarantee the improvement of road safety. Conventional crack detection and measurement algorithms can be extremely time-consuming and low efficiency. Therefore, recently, innovative algorithms have received increased attention from researchers. In this paper, we propose an ensemble of convolutional neural networks (without a pooling layer) based on probability fusion for automated pavement crack detection and measurement. Specifically, an ensemble of convolutional neural networks was employed to identify the structure of small cracks with raw images. Secondly, outputs of the individual convolutional neural network model for the ensemble were averaged to produce the final crack probability value of each pixel, which can obtain a predicted probability map. Finally, the predicted morphological features of the cracks were measured by using the skeleton extraction algorithm. To validate the proposed method, some experiments were performed on two public crack databases (CFD and AigleRN) and the results of the different state-of-the-art methods were compared. To evaluate the efficiency of crack detection methods, three parameters were considered: precision (Pr), recall (Re) and F1 score (F1). For the two public databases of pavement images, the proposed method obtained the highest values of the three evaluation parameters: for the CFD database, Pr = 0.9552, Re = 0.9521 and F1 = 0.9533 (which reach values up to 0.5175 higher than the values obtained on the same database with the other methods), for the AigleRN database, Pr = 0.9302, Re = 0.9166 and F1 = 0.9238 (which reach values up to 0.7313 higher than the values obtained on the same database with the other methods). The experimental results show that the proposed method outperforms the other methods. For crack measurement, the crack length and width can be measure based on different crack types (complex, common, thin, and intersecting cracks.). The results show that the proposed algorithm can be effectively applied for crack measurement.Keywords: automated pavement crack detection and measurement; deep learning; ensemble network; convolutional neural network; segmentation; morphological
Clone Ensemble V4 2 Crackedl
NEO STRINGS is a modern solo string ensemble comprising of violin, viola, cello and double bass. Recorded in the same beautiful space through 4 mic signals, and a huge 24 articulations captured for each instrument in great detail.
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Chorus-Ensemble offers a classic two-delay line chorus with an optional third delay line mode. With a wide variety of tools for thickening sounds, creating flanging and vibrato effects, this device also allows you to easily recreate string ensemble chorus sounds.
What drum VSTs you go for relies heavily on the genre or style you are composing for. You may look into electronic drums for EDM or video game scores, or softer, mellow kits for jazz ensembles.
The virtual space perfectly reproduces what a real recording of an ensemble would be using convolution. When you record a big band, you usually place a pair of microphones in front of the musicians and they naturally take position in the stereo field depending on where they are in the room.
An ensemble is a group of models that are used together for prediction both in classification and regression classes. Ensemble learning helps improve ML results because it combines several models. By doing so, it allows for a better predictive performance compared to a single model. They are superior to individual models as they reduce variance, average out biases, and have lesser chances of overfitting.
Boosting focuses on errors found in previous iterations until they become obsolete. Whereas in bagging there is no corrective loop. This is why boosting is a more stable algorithm compared to other ensemble algorithms. 2ff7e9595c
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