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= Glossary =

This glossary defines terminology commonly used in '''capture, profiling, neural audio modeling, reamping, and model playback'''.

Terminology is not completely standardized across manufacturers and open-source projects. Different platforms may use words such as ''capture'', ''profile'', ''model'', and ''clone'' for related but technically different processes.

Where a term has a platform-specific meaning, that distinction is noted.

__TOC__

== A ==

=== A1 ===

The original family of standard [[Neural Amp Modeler]] neural-network architectures, retrospectively named '''A1''' following the introduction of [[NAM A2 Architecture|A2]].

A1 includes architectures previously known as Standard, Lite, Feather, and Nano.

A1 models remain supported by current NAM software.

=== A2 ===

The second-generation standard architecture for [[Neural Amp Modeler]], officially released in June 2026.

A2 was developed to improve the relationship between modeling accuracy, computational requirements, training efficiency, and hardware implementation.

See [[NAM A2 Architecture]].

=== A/B comparison ===

Direct comparison between two signal paths.

During capture work, this commonly means comparing:

* Physical device versus trained model
* Original recording versus model output
* Two different models of the same equipment

Accurate A/B comparison requires careful level matching.

=== AIDA-X ===

An open-source neural amplifier modeling and playback project developed by Aida DSP.

AIDA-X uses its own model architecture and should not be confused with NAM merely because both are open-source neural modeling technologies.

See [[Open Capture Technologies]].

=== Amp capture ===

A model representing an amplifier without its physical speaker/cabinet/microphone response.

An amp capture is commonly combined with a cabinet IR or cabinet model during playback.

Also called a '''direct amp capture''' in some contexts.

=== Amp+Cab capture ===

A capture that includes the amplifier together with a speaker/cabinet signal path.

When created using a microphone, the microphone and its placement are also normally part of the resulting sound.

An additional cabinet IR is generally unnecessary during conventional playback.

See [[Capture Types]].

=== Algorithmic modeling ===

Modeling in which a developer designs an algorithm intended to reproduce the behavior of a device or circuit.

This contrasts conceptually with capture technology, where the behavior of physical equipment is measured and a model is derived from those measurements.

Modern products may combine algorithmic and capture-based techniques.

=== Aliasing ===

Unwanted frequencies produced when nonlinear processing generates frequency components above the Nyquist frequency and they fold back into the audible range.

Oversampling is commonly used in nonlinear digital audio processing to reduce aliasing.

=== Architecture ===

The structure of a neural network.

An architecture defines how neural-network layers and processing elements are arranged.

Examples include:

* LSTM
* WaveNet-style architectures
* NAM A1
* NAM A2

Two model files using different architectures are not automatically interchangeable.

=== Audio interface ===

Hardware that converts between analog audio signals and digital audio used by a computer.

In a capture workflow, an audio interface may provide:

* Training-signal output
* Capture return input
* Level control
* Analog-to-digital conversion
* Digital-to-analog conversion

== B ==

=== Batch training ===

Training multiple models as part of one automated workflow.

Batch training can be useful when several recordings have been made from:

* Different amplifier settings
* Different pedals
* Different channels
* Different pieces of equipment

=== Bias ===

An operating condition within an electronic circuit.

In capture discussions, the term is often encountered with tube amplifiers or fuzz circuits whose behavior can change significantly according to bias conditions.

Bias can also have unrelated meanings in machine learning. Context matters.

=== Block ===

A processing element placed within a digital signal chain.

Examples include:

* Amp block
* Capture block
* Cab block
* IR block
* Delay block

Several hardware capture platforms represent captured models as blocks within a larger preset.

=== Boost ===

A device that increases signal level and may also alter frequency response or nonlinear behavior.

Boost pedals are common capture targets.

=== Buffer ===

An electronic circuit designed to isolate signal stages and provide appropriate impedance relationships.

A buffer can alter the behavior of impedance-sensitive equipment such as certain vintage fuzz circuits.

Its presence or absence can therefore matter during capture.

== C ==

=== Cab ===

Abbreviation for '''speaker cabinet'''.

In modeling systems, ''Cab'' can also refer to a digital cabinet model.

See [[Cabinets and IRs]].

=== Calibration ===

The process of establishing a known relationship between digital signal level and analog signal level.

In capture systems, calibration can help ensure that a model receives approximately the same operating level during playback that the physical equipment received during measurement.

See [[Gain Staging and Calibration]].

=== Capture ===

The measurement process used to characterize physical audio equipment for creation of a digital model.

The word may also be used informally for the resulting model.

Platform terminology varies.

For example:

* Neural DSP — Neural Capture
* Line 6 — Clone
* Kemper — PROFILE
* TONEX — Tone Model
* NAM — model

=== Capture data ===

Recorded measurement information used to train or construct a model.

Capture data is not necessarily the finished model.

Conceptually:

'''capture data → training/modeling process → model'''

=== Capture signal ===

A specially prepared audio signal sent through physical equipment during the measurement process.

Also called:

* Training signal
* Stimulus
* Excitation signal
* Test signal

Different capture systems use different signals.

=== Capture signal chain ===

The complete physical path through which the capture signal travels.

For example:

'''interface → reamp box → amplifier → load box → interface'''

or:

'''capture hardware → amplifier → cabinet → microphone → capture hardware'''

Everything within the measured signal path can potentially influence the resulting capture.

See [[Capture Signal Chain]].

=== Capture type ===

The portion of a signal chain represented by a model.

Common capture types include:

* Pedal
* Amp
* Amp+Cab
* Preamp
* Complete rig

See [[Capture Types]].

=== Clone ===

Line 6's term for a model created using [[Line 6 Proxy]].

Current Proxy Clone categories include:

* Amp+Cab
* Amp
* Preamp
* Distortion

The word ''clone'' may also be used generically in discussion, but '''Clone''' has a specific meaning within the Proxy ecosystem.

=== Cloud training ===

Model training performed on remote computing infrastructure rather than entirely on the user's local computer or capture hardware.

Examples include workflows used by:

* Neural Capture V2
* Line 6 Proxy

Cloud training can provide greater computational resources but requires access to the associated online service.

=== Colab ===

Common abbreviation for '''Google Colaboratory'''.

Colab provides cloud-hosted Python notebook execution and has been widely used for neural-model training.

NAM and several open-source audio-modeling projects have provided Colab-based training workflows.

=== Conditioned model ===

A neural model that receives one or more control parameters in addition to the audio signal.

Conceptually:

'''audio + control value → model → output'''

This can allow one model to represent behavior across multiple physical control positions.

Contrast with [[#Snapshot model|snapshot model]].

=== Convolution ===

A mathematical operation widely used in digital signal processing.

Cabinet impulse-response playback typically uses convolution to apply the measured response contained in an IR to an audio signal.

=== Cortex Cloud ===

Neural DSP's online service for Cortex content.

It is used for sharing and managing Cortex material and performs the cloud-based training required for Neural Capture V2.

=== Cortex Control ===

Neural DSP's desktop application for managing Cortex hardware.

It also participates in the Neural Capture V2 creation workflow.

=== CPU ===

'''Central Processing Unit.'''

In capture playback, CPU requirements influence:

* Number of models that can run simultaneously
* Buffer size
* Latency
* Oversampling
* Available effects processing

Different neural architectures can have substantially different CPU requirements.

=== CustomTone ===

Line 6's community service for sharing compatible presets and Proxy Clones.

== D ==

=== dB ===

'''Decibel.'''

A logarithmic unit used to describe ratios such as signal level or gain.

The meaning depends on the reference being used.

=== dBFS ===

'''Decibels relative to Full Scale.'''

A digital level measurement.

0 dBFS represents the maximum digital level available before numerical clipping in a conventional fixed reference system.

Values below full scale are expressed as negative numbers.

=== dBu ===

A voltage-based audio level measurement referenced to 0.775 volts RMS.

Calibration information in some capture systems may use dBu to describe the relationship between digital and analog levels.

=== Device under test ===

The physical equipment being measured.

Often abbreviated '''DUT'''.

Examples include:

* Amplifier
* Pedal
* Preamp
* Compressor
* Complete rig

=== Direct capture ===

A capture made without a physical speaker/microphone response.

For an amplifier this commonly means taking the output through an appropriate load box or direct-output system.

A cabinet model or IR is normally required when playing a direct amp capture through a full-range monitoring system.

=== Direct Injection box ===

See [[#DI box|DI box]].

=== DI box ===

'''Direct Injection box.'''

A device commonly used to interface an instrument-level, high-impedance source with a balanced microphone-level input.

A DI box and reamp box perform broadly opposite interface functions, although specific equipment may provide additional capabilities.

=== DSP ===

'''Digital Signal Processing''' or, depending on context, the processing resources available for digital signal processing.

Hardware modelers often describe limits in terms of available DSP.

=== DUT ===

See [[#Device under test|Device under test]].

== E ==

=== Embedded system ===

A dedicated computing platform designed to perform a specific function.

Neural-model players can run on embedded processors inside:

* Pedals
* Amplifiers
* Multi-effects processors
* Dedicated model players

Efficient neural architectures are particularly important for embedded use.

=== Excitation signal ===

See [[#Capture signal|Capture signal]].

== F ==

=== Full-range system ===

A playback system intended to reproduce a broad frequency range relatively neutrally.

Capture users often use full-range monitors or PA speakers when playing models that already include cabinet simulation.

=== FRFR ===

'''Full Range, Flat Response.'''

A term commonly used for speaker systems intended to reproduce modeled guitar rigs without adding the strong tonal filtering of a traditional guitar cabinet.

In practice, no loudspeaker is perfectly flat.

=== Fuzz ===

A strongly nonlinear distortion effect.

Some fuzz circuits are particularly challenging capture targets because their behavior can depend on:

* Source impedance
* Input level
* Guitar volume
* Bias
* Playing dynamics

== G ==

=== Gain staging ===

Management of signal level throughout an audio chain.

In capture work, gain staging is important both for avoiding clipping/noise and for reproducing the operating conditions under which nonlinear equipment was measured.

See [[Gain Staging and Calibration]].

=== Ground lift ===

A facility designed to interrupt an audio ground connection to help eliminate ground-loop hum.

A proper audio ground lift should not be confused with defeating protective mains safety grounding.

'''Never remove protective mains grounding as a method of eliminating hum.'''

=== Ground loop ===

An unwanted current path created by multiple grounding connections.

Ground loops commonly produce hum or other interference in capture and reamping systems.

== H ==

=== Hardware capture ===

A capture workflow in which dedicated hardware performs some or all of the measurement process.

Examples include:

* Kemper PROFILER
* Quad Cortex
* Helix Stadium

The model training itself may occur locally or in the cloud depending on the platform.

=== Hidden layer ===

An internal processing layer within a neural network.

The size and organization of hidden layers are part of the model architecture.

== I ==

=== Impedance ===

Opposition to alternating current, including audio signals.

Impedance relationships can affect the behavior of:

* Guitar pickups
* Pedals
* Reamp devices
* Amplifier inputs
* Audio interfaces

Some equipment, particularly certain fuzz circuits, can respond strongly to source impedance.

=== Impulse response ===

See [[#IR|IR]].

=== Inference ===

Using an already-trained neural network to process new input.

In capture terminology:

'''training''' creates the model.

'''inference''' runs the model.

Real-time capture playback is neural-network inference.

=== Input level ===

The signal level presented to a device or model.

With nonlinear equipment, input level affects behavior as well as output volume.

Changing input level can alter:

* Distortion
* Compression
* Sustain
* Dynamics
* Cleanup

=== IR ===

'''Impulse Response.'''

A representation of the response of a linear system.

In guitar modeling, IRs are commonly used to represent:

'''speaker + cabinet + microphone'''

An IR is fundamentally different from a nonlinear neural amplifier model.

See [[Cabinets and IRs]].

== J ==

=== JSON ===

'''JavaScript Object Notation.'''

A human-readable structured data format.

Some neural model formats use JSON or JSON-based structures.

The fact that two model formats use JSON does not make them compatible.

The [[NAM Model File Format]] is JSON-based.

== L ==

=== Latency ===

Delay between an input signal entering a system and the corresponding output being produced.

Latency matters in two different capture contexts:

'''Recording/training'''

The recorded output must be correctly aligned with the training input.

'''Playback'''

Excessive processing latency can make real-time playing uncomfortable.

See [[Troubleshooting Captures]].

=== Level matching ===

Adjusting two signals so they have closely comparable perceived level.

Level matching is essential when comparing:

* Model versus physical device
* Two models
* Two capture platforms

A slightly louder signal can easily be perceived as better.

=== Linear system ===

A system whose behavior can be represented using linear signal-processing methods.

Speaker/cabinet/microphone responses are often approximated as linear systems and represented using IRs.

Distorting amplifiers and pedals are nonlinear and generally require more complex modeling.

=== Load box ===

A device designed to provide the electrical load required by an amplifier while allowing its output to be recorded or processed without relying solely on a physical loudspeaker.

Load boxes are commonly used when creating direct amplifier captures.

Some load boxes also provide:

* Speaker simulation
* IR processing
* Attenuation
* Line outputs

These functions should be configured appropriately for the intended capture type.

=== LSTM ===

'''Long Short-Term Memory.'''

A recurrent neural-network architecture designed to process sequential information.

LSTM architectures have been used in several neural audio-modeling projects, including GuitarML systems.

== M ==

=== Machine learning ===

A broad class of computational methods in which a system learns relationships from data rather than relying solely on explicitly programmed rules.

Several modern capture technologies use machine learning to reproduce nonlinear audio equipment.

=== Metadata ===

Information stored with or associated with a model.

Useful capture metadata may include:

* Creator
* Equipment manufacturer
* Equipment model
* Capture type
* Equipment settings
* Input calibration
* Output calibration
* Training information
* Capture date

See [[NAM Model File Format]] for NAM-specific metadata.

=== Mic capture ===

A capture whose measured path includes a microphone.

For an amplifier rig this usually means:

'''amplifier → speaker → microphone'''

The microphone and its position become part of the resulting model or captured response.

=== Model ===

A digital representation used to reproduce the behavior of another system.

Within capture technology, a model is the result of training or profiling measurements of physical equipment.

The word is also used more broadly for conventional algorithmic amp and effects models.

Context is important.

=== Model architecture ===

See [[#Architecture|Architecture]].

=== Model conversion ===

Transforming a model intended for one architecture or playback engine into another representation.

Conversion is not necessarily lossless.

A converted model should not automatically be described as executing the original architecture natively.

See [[NAM Playback]].

=== Model file ===

A file containing the information required by a compatible playback engine to run a trained model.

A model file is not necessarily the same thing as the original capture recording.

=== Model player ===

Software or hardware capable of executing a trained model.

Examples range from desktop plugins to dedicated pedals and multi-effects processors.

=== Modeling ===

The broad process of creating a computational representation of physical equipment.

Modeling can include:

* Circuit modeling
* Behavioral modeling
* Machine-learning modeling
* Profiling
* Capture technology

== N ==

=== NAM ===

'''Neural Amp Modeler.'''

An open-source deep-learning system for modeling nonlinear audio equipment.

NAM also commonly refers to the wider ecosystem surrounding the technology.

See [[Neural Amp Modeler]].

=== .nam ===

The conventional file extension for a [[Neural Amp Modeler]] model.

A ''.nam'' file uses the [[NAM Model File Format]].

Not every neural amplifier model is a NAM model.

=== NAM A1 ===

See [[#A1|A1]].

=== NAM A2 ===

See [[#A2|A2]].

=== Native playback ===

Playback in which software or hardware executes the model architecture directly rather than first converting it to another proprietary model representation.

See [[NAM Playback]].

=== Neural Capture ===

Neural DSP's proprietary capture technology used in the Cortex ecosystem.

Current generations include:

* Neural Capture V1
* Neural Capture V2

See [[Neural Capture]].

=== Neural network ===

A computational structure consisting of interconnected processing elements whose parameters are learned from data.

Neural networks can approximate nonlinear relationships and are therefore useful for modeling audio equipment such as amplifiers and pedals.

=== Nonlinear system ===

A system whose output is not simply a scaled or filtered version of its input.

Examples include:

* Distorting amplifier
* Overdrive
* Fuzz
* Compressor
* Saturating preamp

Neural capture technology is particularly useful for reproducing nonlinear behavior.

=== Normalization ===

Adjustment of signal or data level according to a defined reference.

Normalization can mean different things in recording, training, and model playback contexts.

It should not automatically be assumed to be equivalent to calibration.

=== Nyquist frequency ===

Half the digital audio sample rate.

For example, at a 48 kHz sample rate, the Nyquist frequency is 24 kHz.

Frequency components above Nyquist cannot be represented directly at that sample rate.

== O ==

=== Open source ===

Software whose source code is available under a license permitting specified forms of use, inspection, modification, and redistribution.

Open source does not mean:

* No copyright
* No license restrictions
* Universal model compatibility
* Free use for every possible purpose

Always check the project's actual license.

=== Oversampling ===

Processing audio internally at a higher sample rate than the system's nominal rate.

Oversampling is commonly used in nonlinear audio processing to reduce audible aliasing.

It increases computational requirements.

== P ==

=== Pedal capture ===

A model of a physical effects pedal.

Capture technologies commonly support nonlinear pedals such as:

* Boost
* Overdrive
* Distortion
* Fuzz

Support for compressors and other dynamic processors varies by capture architecture.

=== Playback ===

Running a completed model with new audio.

For neural models this is inference.

Playback requirements can differ substantially from training requirements.

See [[NAM Playback]].

=== Preamp capture ===

A model representing a preamplifier or the preamp section of an amplifier.

A preamp capture normally excludes:

* Power amplifier
* Speaker
* Cabinet
* Microphone

unless those elements are deliberately included elsewhere in the measured path.

=== Profile ===

Generic term sometimes used for a captured representation of equipment.

'''PROFILE''' is also Kemper's specific term for the result of the Kemper Profiling process.

See [[Kemper Profiling]].

=== Profiling ===

A term for measuring equipment and creating a digital representation of its behavior.

The term is particularly associated with Kemper, although it is also used generically.

=== Proteus ===

An open-source neural-modeling project developed by GuitarML.

Proteus supports snapshot and conditioned models.

See [[Open Capture Technologies]].

=== Proxy ===

Line 6's capture technology introduced for Helix Stadium.

The resulting models are called '''Clones'''.

See [[Line 6 Proxy]].

== Q ==

=== Quad Cortex ===

Neural DSP's Cortex hardware processor providing amplifier/effects modeling and Neural Capture functionality.

It supports creation and playback of Neural Captures.

== R ==

=== Reactive load ===

A load device designed to present an amplifier with an electrical load whose impedance varies with frequency in a manner intended to resemble a loudspeaker system.

Reactive load boxes are widely used for direct amplifier recording and capture.

=== Reamp box ===

A device used to interface a line-level playback source with equipment designed for an instrument-level input.

Typical applications include sending a recorded signal from an audio interface into:

* Guitar amplifier
* Pedal
* Other instrument-level equipment

A reamp box may provide:

* Level attenuation
* Balanced-to-unbalanced conversion
* Ground isolation
* Appropriate source impedance

See [[Reamping for Capture]].

=== Reamping ===

Sending previously recorded audio back through physical audio equipment.

In capture workflows, reamping allows a standardized training signal to be sent repeatedly through different equipment or settings.

See [[Reamping for Capture]].

=== Reference level ===

A defined signal level used as a known measurement point.

Reference levels are important when relating digital levels to analog voltage.

=== Return ===

The signal coming back from the device under test into the capture or recording system.

For example:

'''interface output → amplifier → load box → interface return'''

=== Rig ===

A collection of equipment forming a usable signal chain.

Depending on context, a rig may include:

* Pedals
* Amplifier
* Cabinet
* Microphone
* Effects

In Kemper terminology, '''Rig''' also has a specific platform meaning.

=== Rig Exchange ===

Kemper's online service for sharing PROFILER Rigs.

=== RMS ===

'''Root Mean Square.'''

A method of describing signal magnitude.

For a sine wave, RMS voltage is commonly used when calculating dBu during calibration.

=== RTNeural ===

An open-source neural-network inference engine optimized for real-time audio.

RTNeural is used by projects including AIDA-X and GuitarML software.

RTNeural itself is not a capture-model format.

== S ==

=== Sample rate ===

The number of digital audio samples processed per second.

Common rates include:

* 44.1 kHz
* 48 kHz
* 88.2 kHz
* 96 kHz

Capture/training systems may require a specific sample rate.

=== Send ===

The signal leaving the capture or recording system and traveling toward the device under test.

For example:

'''interface send → reamp box → amplifier'''

=== Signal chain ===

The ordered path through which an audio signal travels.

The signal chain determines which equipment contributes to a capture.

=== Snapshot ===

See [[#Snapshot model|Snapshot model]].

=== Snapshot model ===

A model representing equipment at a particular configuration.

For example:

'''amplifier gain = 5'''

and:

'''amplifier gain = 8'''

may require separate snapshot models.

Snapshot modeling contrasts with conditioned modeling, in which one model can respond to a control parameter.

=== Stimulus ===

See [[#Capture signal|Capture signal]].

=== Studio capture ===

A capture of studio equipment such as:

* Preamp
* Saturation device
* Channel strip
* Compressor

Whether a particular device can be captured accurately depends on the capabilities of the capture architecture.

=== System identification ===

The engineering process of constructing a mathematical model of a system from observed input and output behavior.

Capture and profiling technologies can be viewed as forms of nonlinear system identification.

== T ==

=== Test signal ===

See [[#Capture signal|Capture signal]].

=== Tone Model ===

IK Multimedia's term for a model created for the [[TONEX]] ecosystem using AI Machine Modeling.

A Tone Model is not a NAM model.

=== ToneNET ===

IK Multimedia's online service for discovering and sharing TONEX Tone Models and related content.

=== Training ===

The computational process that adjusts a model's parameters so its predicted output approximates the measured output of the physical device.

Conceptually:

'''known input + measured output → training → model'''

Training should be distinguished from the physical recording or capture stage.

=== Training data ===

Input/output measurements used to train a model.

For capture technology this commonly consists of:

* Known training input
* Recorded response of the physical equipment

=== Training error ===

A numerical measurement of the difference between model predictions and the target training data.

Lower training error can indicate a closer mathematical fit, but it is not a complete measurement of perceived audio quality.

=== Training signal ===

See [[#Capture signal|Capture signal]].

== V ==

=== V1 ===

The original Neural DSP Neural Capture generation.

V1 training occurs locally on compatible Cortex hardware.

See [[Neural Capture]].

=== V2 ===

The second-generation Neural DSP Neural Capture technology.

V2 uses higher-resolution cloud training and is designed to improve reproduction of difficult dynamic targets including fuzzes and compressors.

See [[Neural Capture]].

=== VST3 ===

A plugin format widely used by digital audio workstations.

Several capture-model players are available as VST3 plugins.

== W ==

=== WaveNet ===

A convolutional neural-network architecture originally developed for audio generation.

WaveNet-style architectures have also been adapted for nonlinear audio-system modeling.

Some GuitarML projects have used WaveNet-based architectures.

== See also ==

* [[Capture Technologies]]
* [[Getting Started with Capture Technology]]
* [[Capture Signal Chain]]
* [[Gain Staging and Calibration]]
* [[Reamping for Capture]]
* [[Creating a Capture]]
* [[Capture Types]]
* [[Cabinets and IRs]]
* [[Troubleshooting Captures]]
* [[Neural Amp Modeler]]
* [[NAM Model File Format]]
* [[NAM A2 Architecture]]
* [[NAM Playback]]
* [[TONEX]]
* [[Kemper Profiling]]
* [[Neural Capture]]
* [[Line 6 Proxy]]
* [[Open Capture Technologies]]

'''[https://namforum.com/ Discuss capture technology and terminology on NAMFORUM]'''

[[Category:Glossary]]
[[Category:Capture technology]]
[[Category:Reference]]

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