SKU: 79921768761
pedalboard volume pedal

pedalboard volume pedal Canvas Volume Pedal

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Description

pedalboard volume pedal Canvas Volume PedalThe Canvas Volume Pedal is designed to give players unprecedented control, reliability, and flexibility from a volume pedal. Built around a precision position sensor and high quality VCAs (Voltage Controlled Amplifiers), Canvas Volume delivers a premium, all analog signal path without the mechanical compromises of traditional designs. Shape the feel and response of Canvas Volume to fit your playing style with customizable features, including multiple

The Canvas Volume Pedal is designed to give players unprecedented control, reliability, and flexibility from a volume pedal. Built around a precision position sensor and high-quality VCAs (Voltage Controlled Amplifiers), Canvas Volume delivers a premium, all-analog signal path without the mechanical compromises of traditional designs. Shape the feel and response of Canvas Volume to fit your playing style with customizable features, including multiple tapers modeled from existing volume pedals, minimum-on volume, maximum gain, and lag.

No strings. No pots. No gears. Nothing to wear out, get scratchy, or fail mid-set. With contactless position sensing and a robust metal chassis, Canvas Volume is designed for players who demand consistency night after night. It’s a modern volume pedal built to solve old problems without sacrificing tone or feel.

Housed in a rugged steel and aluminum chassis that balances durability with pedalboard-friendly weight, Canvas Volume is engineered for years of consistent performance on stage, in the studio, and everywhere in between.


 

Fully Analog Signal Path, Digitally Controlled Precision

Canvas Volume uses a precision position sensor to track pedal movement and translate it into smooth, musical volume changes via high-quality analog VCAs. The result is the feel and tone players expect from a classic volume pedal, paired with modern consistency and control. 


 

Flexible Signal Routing

Designed to integrate seamlessly into virtually any rig, Canvas Volume supports multiple signal flows:

    • Mono In / Mono Out with Dedicated Tuner Out

    • Stereo In / Stereo Out

    • Mono In / Dual Mono Out

Whether you’re running a simple mono setup, a stereo board, or need dual outputs from a single input, Canvas Volume adapts easily to your workflow.


 

Deep Customization Under the Heel

The control interface is discreetly tucked under the heel of the pedal, keeping your settings safe while remaining easy to access. Two buttons and LED indicators give you control over four powerful parameters:

Taper

The taper defines how your volume changes across the pedal’s sweep - and it’s the key to dialing in the perfect feel. Canvas Volume includes our own custom-designed taper plus seven carefully modeled tapers from classic, real-world volume pedals. Taper 1 is custom-designed specifically for the Canvas Volume, aiming to provide an ultra-smooth response for guitar players. Tapers 2-6 provide more traditional audio tapers modeled after popular volume pedals. Tapers 7 and 8 feature models from pedals with a more linear response favored by pedal steel players: 

    1. Taper One (Canvas Taper)

    2. Taper Two (Lehle model)

    3. Taper Three (Boss model)

    4. Taper Four (Dunlop model)

    5. Taper Five (Ernie Ball VP Jr model)

    6. Taper Six (Hotone model)

    7. Taper Seven (Hilton model)

    8. Taper Eight (Goodrich model)

Minimum On

Sets how much signal passes through when the pedal is fully heel-down. At its lowest setting, Canvas Volume provides extreme attenuation (about -90dB). Raising this value allows some signal to remain at heel-down, perfect for volume swells that never fully disappear or for faster response at the start of the sweep.

Gain

Controls the maximum output level at toe-down. Unity gain (0dB) sits at the default setting, with additional headroom available in 3dB increments, up to +9dB, making Canvas Volume equally capable as a clean volume boost.

Lag

Lag introduces a delay between pedal movement and volume response, smoothing out transitions and enhancing swells. From immediate, fast response to a dramatic 2-second lag, this parameter lets you fine-tune the pedal’s feel to your playing style. 

Note: Lag works alongside whichever taper you’ve chosen and will respond to that taper’s curve. For example, a linear taper curve will have an equal amount of lag going from toe down to heel down positions. Lag following a non-linear curve will swell into a toe-down position quicker than going into a heel-down position.


 

Utility Menu & Advanced Functions

Canvas Volume includes a built-in Utility Menu for deeper system control:

    • Factory Reset – Restore all settings to factory defaults

    • Calibration – Easily recalibrate pedal travel for precise operation

    • Mono Mode Selection – Choose between tuner output or dual mono output when running mono

    • DFU Mode – For firmware updates (only when directed by Walrus Audio support)

These tools ensure your pedal stays dialed in, accurate, and ready for long-term use.

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SKU: 79921768761

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Shannon
Alexandria, US
★★★★★ 5
The best DL/ML book I have ever seen!!
Format: Hardcover
Fantastic deep-learning book! The logic is very easy to follow, but the content is very thorough when it comes to explaining the theories behind it, making it perfect for beginners as well as math and CS students. The best DL/ML book I have ever seen!!
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Reviewed in the United States on November 30, 2025
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William P Ross
New York, US
★★★★★ 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
Cuba, 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
Omaha, 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
Port Orchard, 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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