Mattfig HoseA NAM Capture Pack

Mattfig HoseA NAM Capture Pack product box featuring 48 amp captures from a Friedman Jose-style amplifier, including Plex and Drive channels, compatible with Neural Amp Modeler (NAM), labeled LG, MG, HG, 412, DI, with lock icon, on a green background.

Mattfig HoseA NAM Capture Pack is a collection of 48 Neural Amp Modeling profiles captured from a Friedman Jose amplifier through an FMan 412 cabinet plus DI, offering multiple gain stages and presence modes (Vintage/New) for versatile, expressive guitar tones in modern amp-modeling workflows.

Professional Guitar Tone Profiles For Neural Amp Modeling (NAM)

Mattfig HoseA NAM Capture Pack is a guitar amp profile capture pack designed for use with Neural Amp Modeling (NAM) platforms such as Neural DSP devices and compatible software loaders. Instead of generic presets or static patches, this pack provides 48 professional capture profiles derived from a Friedman Jose amplifier routed through an FMan 412 cabinet and full DI, giving you detailed, expressive tonal options that reflect the real behavior of the amp and speaker rig.

This pack captures multiple gain stages and voicings from the Friedman Jose — a highly regarded amp for rock and high-gain styles — so you get versatile presets for tight metal, hard rock, creamy leads, and modern production tones.

Multiple Gain Voicings And Presence Modes

The capture pack includes profiles for several characteristic Friedman channels and configurations:

  • Dr-Diode — aggressive diode clipping drive ideal for tight metal and melodic solos.
  • Dr (Drive) — natural drive channel tones suited to hard rock and lead phrasing.
  • Plex-Diode — a saturated, character-rich tone (often described as “the sauce”).
  • Plex (Clean/Crunch) — classic Plexi-inspired base tone with broad musical appeal.

Each of these is provided with Vintage Presence (V) — the classic reference voicing — and New Presence (N) — a broader, modern take with enhanced feel and clarity. Profiles span High Gain, Mid Gain, and Low Gain settings to suit a range of musical styles and performance dynamics.

Designed For Flexible Tone Integration

Because these captures are delivered in NAM format, they load into compatible amp modeling systems that use Neural Amp Modeling profiles, letting you combine them with IRs, post-EQ, effects, and multi-cab setups. This gives you realistic amp behavior, nuanced response to playing dynamics, and studio-ready guitar tone options without needing the physical amp or cab.

The Friedman Jose is celebrated for its serious high-gain performance and versatile drive structure, and this pack preserves that personality across its captured settings, making it useful in rock, metal, blues, and fusion contexts alike.

FAQs

  • What format are the profiles?

    They are in NAM capture format for Neural Amp Modeling systems.

  • What amplifier is featured?

    Profiles are captured from a Friedman Jose amplifier through an FMan 412 cab plus direct input.

  • How many profiles are included?

    The pack contains 48 professional capture profiles across multiple gain and presence settings.

  • What styles suit these tones?

    Ideal for rock, hard rock, metal, and lead guitar applications, with dynamic response to playing.

  • Can these be used with other modeling systems?

    They’re designed specifically for NAM-compatible platforms, which may be loaded into Neural DSP and similar environments that accept NAM files.

Mattfig HoseA NAM Capture Pack
mattfig hosea nam capture pack | Plugin Crack

Mattfig HoseA NAM Capture Pack is a collection of 48 Neural Amp Modeling profiles captured from a Friedman Jose amplifier through an FMan 412 cabinet plus DI, offering multiple gain stages and presence modes (Vintage/New) for versatile, expressive guitar tones in modern amp-modeling workflows.

Price: 18.99

Price Currency: USD

Operating System: Windows, macOS

Application Category: Multimedia

Editor's Rating:
4.3

This Post Has One Comment

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    kOJI

    Great, bro! Thanks! lml

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  • Post category:Presets / Sounds
  • Post last modified:March 2, 2026