SKU: 27895327824
vibrato pedal

vibrato pedal JHS Pedals Emperor V2

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Description

vibrato pedal JHS Pedals Emperor V2Maker: JHS Pedals Model: Emperor V2 Condition: New Description: In 2014 we brought you the Emperor V1, a 100% Analog Chorus Vibrato with Tap Tempo. Now in 2018 we are proud to bring you the Emperor V2! There are two ways to look at the JHS Pedals Emperor Analog Chorus Vibrato pedal. The Emperor V2 is a vintage correct effect that absolutely nails the hard to find sound of the Arion SCH 1 but its also a do it all modulation solution whether you want a

Maker: JHS Pedals

Model: Emperor V2

Condition: New

 

Description:

In 2014 we brought you the Emperor V1, a 100% Analog Chorus/Vibrato with Tap Tempo. Now in 2018 we are proud to bring you the Emperor V2!

There are two ways to look at the JHS Pedals Emperor Analog Chorus/Vibrato pedal. The Emperor V2 is a vintage-correct effect that absolutely nails the hard-to-find sound of the Arion SCH-1...but its also a do-it-all modulation solution whether you want a subtle sheen, convincing rotary simulation, or seasick vibrato. It utilizes a Bucket Brigade 3207 chipset to deliver the warmth of ‘80s-style analog chorus pedals and we've added enough onboard and outboard control to put you in command of every aspect of the Emperor’s voice. Whether spreading its warm tones or icy sheen to a pristine-sounding stereo rig... or utilizing an external expression pedal to cop realistic rotary speaker sounds on a blues gig, the Emperor can do it all.

So much modulation

We call the Emperor an analog chorus/vibrato. That’s just because calling it a true analog chorus/pitch vibrato with tap tempo/waveform selection/rotary speaker simulation/true stereo output/and more pedal would have been a pain to write every time.

- The glorious tones of a real-deal, 3207 chipset bucket brigade topology
- Mini toggle switch selects between chorus and vibrato effects
- Three available waveform types (sine, square, and triangle)
- Intuitive controls for Volume, EQ, Speed, and Depth

Updated Tone Shaping Capabilities 

Making a good thing better? Yes please. The Emperor V1 had a Tone control which has now become the EQ control in the Emperor V2. We've replaced the passive tone control in the V1 with an active EQ control that functions as a Tilt. You'll now have much more tone shaping capability in the V2. The overall EQ control is much improved in the Emperor V2 and you will not lose any low-end sometimes commonly found with chorus/vibrato pedals.

In-depth tempo control

To control the rate of the Emperor’s effect, simply turn the Speed knob up and down or tap the tap tempo footswitch. Or for the more adventurous among us...plug in an expression pedal for variable control in real time. And you can even slave the pedal to other tap-controlled pedals for rhythmic consistency across all your effects.

- Control the rate with the onboard Speed knob or tap tempo footswitch
- Internal Tap/Exp mini switch controls the side-mounted Tap/Exp TRS jack
- Use an external source to control the rate in the Tap Out setting (works great with our Panther Cub Delay and Unicorn pedals).
- LED indicator notifies you of the effect’s tempo, even when disengaged

A few extras

You knew we would throw in a few extra features for you, didn’t you? Of course we did. When designing the Emperor, we conveniently and inconspicuously tucked in stereo output and the ability to select between buffered or true-bypass operation.

- Plugging TRS splitter cable into the output jack is all it takes to wring true stereo output from the Emperor
- Internal switch changes from buffered to true-bypass operation**
- High-quality buffer helps drive long cable runs and in-depth pedal setups while restoring high-end loss

This pedal requires standard 9V DC Negative power, consumes less than 100mA.

There are a lot of penguins out there but only one Emperor.

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SKU: 27895327824

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Hashi Hanta
Los Angeles, US
★★★★★ 5
Excelllent book
Format: Hardcover
As one of the group of Native Americans who landed on Alcatraz with Richard Oakes, I enjoyed this book. Richard was a fantastic man. A good man.
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Reviewed in the United States on February 14, 2019
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Carol
Chelsea, US
★★★★★ 5
Need to read book
Format: Hardcover
The truth about the Native people. THANK YOU Kent for writing this book. We purchased about 12 total.
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Reviewed in the United States on November 24, 2019
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Walter Echo-Hawk, author of THE SEA OF GRASS.
Chelsea, US
★★★★★ 5
Native American history at its best!
Format: Hardcover
Kent Blansett's engrossing story about the life & times of the famed Mohawk activist Richard Oakes is Native American history at its best. I appreciated the well-written context provided about the birth, growth and impact of the Red Power Movement and the pivotal role that social justice activism played in the rise of modern Indian nations in the United States today. This scholarly work helps us understand modern Native America and is a "must-read" for every Native American Studies student and scholar, as well as readers interested in important American social justice movements.
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Reviewed in the United States on April 1, 2019
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Par
Birmingham, US
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents. The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!! The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!! I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge. Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents. Personally I was disappointed by lack of any example on time series. Must have for ML practitioner as a reference and guide.
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Reviewed in the United States on December 20, 2024
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Richard Hackathorn
Los Angeles, US
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book. For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch. For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min). From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures. I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications. Several textbook editions later, what is different about this new edition? First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills. Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like. Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
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Reviewed in the United States on February 26, 2022

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