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ToneAdapt - Guitar Tone Match

ToneAdapt - Guitar Tone Match

Kyan Santiago-Calling

Music免费v1.1.1
App Store
评分

4.7

408 条评分

星级

★★★★★

最近更新

2026年5月6日

发布日期

2026年4月10日

更新内容

v1.1.1

Bug Fixes

应用信息

开发者
Kyan Santiago-Calling
分类
Music
价格
免费
版本
1.1.1
App ID
6761394160

简介

ToneAdapt is your personal tone matching engine for the electric guitar or bass guitar. Pick any song's guitar tone, enter your specific gear, and get the original gear used + tone settings, along with custom amp, guitar, and pedal settings adapted to your exact setup in seconds. We aggregate real tone and gear data from across the entire internet — forums, gear reviews, artist rig rundowns, manufacturer specs, and community knowledge — to build the most comprehensive guitar tone database available. ToneAdapt then translates those tonal characteristics into precise settings for your amp, guitar, and pedals. No more copying YouTube amp settings that sound nothing like the original — ToneAdapt accounts for the differences in EQ curves, gain staging, and frequency response across gear brands. Whether you're dialing in Hendrix on a bedroom amp, matching tones for a cover band, or exploring new sounds on your modeling rig, ToneAdapt gives you the exact settings to get there with your gear. ToneAdapt Pro requires a recurring subscription to access unlimited tone adaptations and our full gear database. With ToneAdapt Pro, you get: Unlimited Tone Adaptations: Match any famous guitar tone to your specific gear. 3,600+ Gear Database: Amps, guitars, and pedals from every major brand. Data-Driven Matching: Settings derived from real-world tone data aggregated from thousands of sources, adapted to your exact gear combination. Community Tones: Access tones shared by 90,000+ guitarists. Subscription automatically renews unless cancelled 24hrs before your renewal date in the App Store. Privacy Policy: https://www.toneadapt.com/privacy Terms of Use (EULA): https://www.apple.com/legal/internet-services/itunes/dev/stdeula/

下载量预测

专业 · 预览

预估总下载量

30K20K58K
保守估计乐观估计

20K

低 / 月

30K

预估 / 月

58K

高 / 月

基于408 条评分
假设评分率1.4%
应用年龄1 个月

基于评分数量 ÷ 类别评分率估算,实际下载量误差可达 ±50%,与 Sensor Tower 方法一致。