death by audio vocal pedal ECHO MASTER
SKU: 55766345228
death by audio vocal pedal

death by audio vocal pedal ECHO MASTER

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Description

death by audio vocal pedal ECHO MASTERPSYCHEDELIC VOCAL TRIP Designed for vocals, the Echo Master is a lo fi, analog tape style delay unit that can transform your voice into a chaotic realm of noise and feedback. The Echo Master is not just a delay unit, however. It utilizes matched Bourns transformers on the input to create a vibrant sounding and feedback minimizing preamp to bring you phenomenal vocal quality before you even hit the PA. Your sound engineer will hate you because you'll

PSYCHEDELIC VOCAL TRIP

Designed for vocals, the Echo Master is a lo-fi, analog tape-style delay unit that can transform your voice into a chaotic realm of noise and feedback. The Echo Master is not just a delay unit, however. It utilizes matched Bourns transformers on the input to create a vibrant-sounding and feedback-minimizing preamp to bring you phenomenal vocal quality before you even hit the PA. Your sound engineer will hate you because you'll sound better than ever without their help.

This multitasking analog tape-style echo rules––onstage, it delivers lush vocals shrouded in dusky analog repeats, and it’s invaluable in the studio while recording, reamping, mixing, and more. It’s carefully voiced to avoid shrieking monitor feedback, includes a robust transformer-balanced mic preamp, sturdy Neutrik XLR jacks, and is equipped with an impedance-matching effects loop so you can patch other pedals in your vocal chain. An (optional) mic clip backplate puts the controls within reach during performances so you can tweak the delay effect as you sing, effectively remixing yourself (or your bandmates) live. Best of all, the Echo Master puts total control over your vocal sound at your fingertips, so you get the sound you want, when you want it, all of the time.

Since changeovers at shows can happen quickly and there isn’t always time for soundcheck, the Echo Master gets you amplified and rocking with consistent high-quality echo effects in no time at all. Simply plug your microphone into the mic input, connect the sound engineer’s vocal line to the mic output, plug your pedals into the effects loop, and there’s still time to order another drink before you play. When you’re ready to rock, the F-Back control adjusts the number of repeats from one single echo to infinite raging feedback and the Time knob sets the delay time between 30ms – 620ms. The Mix knob is calibrated to preserve intelligibility (and keep the sound engineer happy), allowing you to sweep from bone-dry to a 50/50 wet/dry blend.

In the studio, the Echo Master is a whole other animal. Use it with acoustic instrument and percussion mics to commit wild new sounds to tape (or hard drive).

9 VDC power supply (US or EU) included.

Time

Controls the delay time, with numbers labeled in milliseconds. In the Red Zone, the delay chip is pushed to the extreme. The signal decreases in fidelity as the time increases.

F-Back

Controls the number of repeats in the delay circuit, anywhere from one to an infinite to a wall of oscillation.

Mix

Controls the level of the delayed channel in comparison to the dry signal, from fully dry to an equal mix.

FBACK (Internal)

Sets the maximum level of the external feedback. Turn all the way clockwise for maximum feedback (factory setting) or turn to the left to limit the range for more precise control.

Want to use your Echo Master with the rest of your favorite guitar pedals? The Send and Receive makes it as easy as your guitar rig!

Plug the Send jack into any additional effects, then back into the Return jack to introduce new sounds into your vocals. This loop is before the delay line, allowing you to affect your dry vocals before they enter the Echo Master.

Tired of wrangling all your vocal pedals on the floor or lugging a table to your shows? We have the solution!

The Echo Master can also be purchased with a backplate that mounts to your mic stand. Puts the pedal within hands reach to maximize live performance tweak-ability.

Note: Cords, microphone, and stand are not included. 9 VDC power supply included.

Power Details

Power Supply: Standard 9 VDC power supply with a 2.1mm negative center barrel.
Note: We strongly recommend using the included power supply or a similar low-noise power supply.

Current Draw: 25 mA

Physical Details

Dimensions: 6.09" x 3.45" x 2.75" (including hardware)

Weight (Standard): 16 oz.

Weight (Alternate Back Plate): 30 oz.

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SKU: 55766345228

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William P Ross
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★★★★★ 5
Comprehensive Look At An Incredibly Complex Topic
Format: Hardcover
Deep Learning is an advanced book with great explanations and details. There is a heavy math focus with the book's beginning chapters detailing the necessary linear algebra and probability that one will need to understand deep learning. I liked that the author's chose to cover only the parts of these subjects which are relevant to deep learning. There are many interesting philosophical sections in the book as well. Just about when I was feeling overwhelmed with the complexity of the mathematics the authors take a step back and cover the foundations of deep learning such as borrowing concepts from human learning. There was an interesting dicussion about the early studies done on the vision of cat's and monkey's in the 1970s. The text covers the entire history of deep learning and the bibliography is hundreds of sources. It is clear this is the most comprehensive text available about deep learning. For anybody interested in this topic this book is a mandatory read. There are sections about machine learning as well, which makes sense because deep learning is a subset of machine learning. These sections focused on the machine learning concepts which are most relevant to deep learning. The book was well organized and divided into three parts which cover mathematics related to deep learning, typical deep learning techniques, and then more experiment learning techniques. Often the author's state when a technique works well or when it does not, and which types of data works best for the technique. Just a warning, the math in this book is highly complex. It requires a lot of work to go through this book, but the effort will be well rewarded.
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Reviewed in the United States on March 15, 2017
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Adam
Port Orchard, US
★★★★★ 4
Too Dry.
Format: Hardcover
This was a required textbook for my class in college. I think it was too dry. The book titled Deep Learning: From Curiosity To Mastery is much more approachable.
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Reviewed in the United States on May 22, 2026
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Amazon Customer
Lake Worth, US
★★★★★ 5
Comprehensive! The Bible of Deep Learning!
This book has by far surpassed my expectations! I have purchased many machine learning and deep neural network books in the past, but nothing has ever come close to this book! First of all, it is written by the fathers of Deep Learning, and is therefore an authority. Secondly, the book is broken into three parts: 1. A math overview and refresher. 2. Deep Learning applications and 3. Research in Deep Learning. I can't help but go through this book from front to back. It is a smooth read, and every sentence written is meaningful. These guys know their stuff! And after you read this book, YOU WILL ALSO know your stuff! If you feel daunted by the price, just remember, you get what you pay for! I'd say they could easily charge about $300+ for this book, but they are doing everyone a very kind favor by ONLY charging this reasonable amount. You get A LOT of bang for your buck with this purchase. I hesitated at first about buying this book because of the price, but I am soooooo happy that I did! Worth every penny! Look no further, get this book and start your Deep Learning journey!!
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Reviewed in the United States on July 14, 2017
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mackster
Louisville, US
★★★★★ 1
A rushed, poorly written guide of how the "experts" can't really explain what Deep Learning is
Format: Hardcover
This book, in every sense of the word, is rushed. I think the authors wanted to establish themselves as leaders of this young-ish field, but does so by sacrificing quality. It also shows that Deep Learning theory has been there for a long time, known by another name called Neural Networks. The interesting algorithms are of MLP, Back Propagation and the classical neural networks. The optimization methods such as Adam are the ones that are new and interesting, and the only ones worthy of in this book. So, essentially, what you get from this book is use A for X, B for Y and C for Z type of dry, un-intuitive, badly written waste of paper. As for the structure of the book, it's like an example of how not to structure a book. It has some linear algebra, probability at the start (not good enough, and confuses more people and wastes paper). Goes on to prove other algorithms such as PCA (yeah, ok!). Then, talks about how this architecture works for this and that architecture. So, yeah, if you really want to try out deep learning, don't buy this book. Set up Tensorflow/pytorch/ other library, run the tutorials, find an architecture for the problem you are interested in and start tweaking that. You will have far more fun and would have saved your money. The praise that this book gets is beyond me. Did Musk even read this book? I doubt it.
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Reviewed in the United States on May 15, 2018
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Stergios Papadimitriou
San Leandro, US
★★★★★ 5
The classic textbook on Deep Learning
Format: Hardcover
Deep Learning is the promising direction towards general purpose effective artificial intelligence. There is an explosion of fruitful research in recent years and a lot of applications pursued mainly from technology giants as Google, Amazon, etc. and outstanding research institutions. The book "Deep Learning " by Ian Goodfellow, Yoshua Bengio, Aaron Gourville, is an excellent piece of work. They manage to present rather difficult things in an understandable manner. The theoretical presentation is outstanding typical of "classic" books. Also, the book stays close to the practical applicability of all the methods and discusses applications extensively. There are a lot of other useful books on deep learning that follow a more practical approach by focusing on a particular deep learning software package, but this one book is certainly much more essential since it provides the required theoretical background in order to be able to do serious work on deep learning. I consider the book as "must have" for anyone that works on deep learning either in an academic or in an industrial environment.
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Reviewed in the United States on August 25, 2018

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