Morse Decoder AI

🇺🇸 United States
Yurii Kvasha  | 
42
ASO score
Text
54/100
Reviews
0/100
Graphic
60/100
Other
0/100
rating
App Rating
2.4
rating
Votes
11
rating
App Age
2y
rating
Last Update
Jan 08, 2024

Compare with Category Top Apps

Metrics
Current App
Category Top Average
Difference
Rating
2.44
4.43
-45%
Number of Ratings (Voted)
11
127.4K
-100%
App Age
1y 11m
9y 11m
-80%
In-app Purchases Price
$0
$53
Update Frequency
708d
107d
+560%
Title Length
16
25
-36%
Subtitle Length
25
27
-7%
Description Length
2 579
2 806
-8%
Number of Screenshots
936
1354
-31 %
Size
60MB
160MB
-63 %

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Ranking Keywords in United States

Keywords App Rank
Illustration
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Text ASO

Title (
Characters: 16 of 30
)
Morse Decoder AI
Subtitle (
Characters: 25 of 30
)
Neural network technology
Description (
Characters: 2579 of 4000
)
The Morse Decoder AI is an application for decoding Morse code signals using artificial intelligence. The application is designed for use by amateur radio enthusiasts for educational purposes. The Morse decoder listens to the audio stream through the microphone or line input and, upon detecting Morse code signals, decodes them into text. The neural network of the application is trained to decode signals with a speed ranging from 10 to 40 words per minute within a frequency range of 200 Hz to 900 Hz. The application supports two modes of operation: direct (default) and tone filtering mode. In direct mode, the neural network will attempt to decipher Morse code signals in the audio range of 250 Hz to 900 Hz. This mode is suitable for confident reception of Morse code at levels of 7db-9db on the S-meter. The tone filtering mode is ideal for decoding Morse code from noisy weak signals in the presence of radio interference. The audio input signal is first filtered using band-pass filters before being passed to the neural network for decoding. This mode allows for the decoding of faint signals, but it requires the accurate specification of the signal's tone frequency. Each radio amateur selects their own CW tone frequency in the transceiver settings, and it is important to tune precisely to the carrier frequency using the ZIN/SPOT button in YAESU transceivers, or the AUTOTUNE button in ICOM transceivers. There are 3 band-pass filter options available: 25Hz, 50Hz, 150Hz. If you can accurately determine the CW signal tone frequency, using the 25Hz filter, you can decode very faint Morse code signals. The application offers two neural network options: A and B, which can be easily switched in the interface. Network A is recommended for use with stable signal transmission with a constant duration of dots and dashes, while network B is recommended when using a straight key, where the duration of dots and dashes may vary. You have the ability to switch between these networks in real time and observe how each network hears and decodes Morse code. It is important to monitor the level of the incoming audio signal, for which the application provides a sound level indicator. Ensure that the signal is not too quiet or too loud. It is recommended to maintain the signal around -7db, which is sufficient for decoding. Keep in mind that higher audio frequencies are quieter than lower frequencies. Additionally, the application provides various color themes, allowing radio enthusiasts to customize the appearance of the application for comfortable use. }
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Visual ASO

Screenshots

Rating & Reviews

Reviews Overview
🧐 Coming Soon…
Rating
2.4
11 voters

Some Latest Reviews

A.LGA
04 May, 2025
1
Декодирует очень плохо. Morse Expert на Андроид делает это намного лучше.
mshibatani
07 Feb, 2025
1
I couldn’t manage to launch this app. This is completely useless. My environment is macOS 15.3(24D60) on Mac Studio M2 Max.
Leejohnsonster
23 Nov, 2024
4
Working fine on a pileup on 20M. It’s nice just lean the iPad on the rig and let it do it’s thing!

Other

Additional Information
Rating:
2.44
Voted: 11
App Store Link:
Website:
Email: -
Privacy Policy:
Categories: Travel, Utilities
Size: 59MB
App Age: 2 years
Release Date: Jan 01, 2024
Last Update: Jan 08, 2024
Version: 1.1
Version history
1.1
Jan 08, 2024
- Added Band Pass filter for decoding weak signals - Improved neural networks type A and B
1.0
Jan 01, 2024