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Functional Ear Trainer

Functional Ear Trainer

Serhii Korchan

Music免费v3.28.0
App Store
评分

4.9

3,500 条评分

星级

★★★★★

最近更新

2025年10月22日

发布日期

2016年3月17日

更新内容

v3.28.0

Added support for iOS 26.

应用信息

开发者
Serhii Korchan
分类
Music
价格
免费
版本
3.28.0
App ID
1088761926

简介

Have you (or maybe one of your friends) ever wanted to learn to transcribe or play music by ear? It is so important for a musician to know what you are hearing. A good musical ear helps when you are composing, improvising, transcribing melodies, or playing with others. Most likely you have already tried different programs to learn to recognize intervals or even to acquire perfect pitch. However, although such programs develop your ear, but can you actually play any melody you hear as soon as you listen to it? Imagine you could understand music... It is like when somebody is talking to you, you not only hear pleasant sounds, but you recognize words and their meaning. One day I came across Alain Benbassat's program called "Functional Ear Trainer" and have been using it ever since. It is based on Alain's method to learn to recognize tones. The main difference between the Functional Ear Trainer and other methods is that it teaches you to distinguish between tones in the context of a particular musical key. You begin to recognize the role (or function) of each tone in this key, which is incredibly similar to its role in other keys of the same scale. And it is *guaranteed* anyone can gradually develop this skill. It does not matter: - who you are - an absolute beginner in music or a virtuoso professional musician; - how old you are - a 3 yo kid or a 90+ adult; - what musical instrument you play (you don't even have to play one). The only requirement is to practice for 10 minutes a day. I was so excited about this ear trainer that I have developed a mobile app based on the Alain Benbassat method. I hope you will find it useful.

下载量预测

专业 · 预览

预估总下载量

259K175K500K
保守估计乐观估计

1K

低 / 月

2K

预估 / 月

4K

高 / 月

基于3,500 条评分
假设评分率1.4%
应用年龄124 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。