Mrp40 Morse Code Decoder -

MORSE_CODE = '.-': 'A', '-...': 'B', '-.-.': 'C', '-..': 'D', '.': 'E', '..-.': 'F', '--.': 'G', '....': 'H', '..': 'I', '.---': 'J', '-.-': 'K', '.-..': 'L', '--': 'M', '-.': 'N', '---': 'O', '.--.': 'P', '--.-': 'Q', '.-.': 'R', '...': 'S', '-': 'T', '..-': 'U', '...-': 'V', '.--': 'W', '-..-': 'X', '-.--': 'Y', '--..': 'Z', '-----': '0', '.----': '1', '..---': '2', '...--': '3', '....-': '4', '.....': '5', '-....': '6', '--...': '7', '---..': '8', '----.': '9'

MRP40 is a famous Windows-based software decoder known for handling low signal-to-noise ratios and human-generated "fisty" code. This guide will walk you through creating a similar system using digital signal processing (DSP) and machine learning techniques. 1. System Overview The decoder will transform audio input (mic/line-in) into text output with high accuracy under noise. mrp40 morse code decoder

7.1 Fist Character Recognition (Speed Tracking) Human senders vary speed. Continuously update T every few symbols. MORSE_CODE = '

from sklearn.cluster import KMeans def estimate_dot_length(pulses, spaces, fs=8000): # Convert samples to ms pulses_ms = [p * 1000 / fs for p in pulses] spaces_ms = [s * 1000 / fs for s in spaces] all_durations = pulses_ms + spaces_ms System Overview The decoder will transform audio input

def extract_run_lengths(binary_signal): pulses = [] spaces = [] count = 1 current = binary_signal[0] for sample in binary_signal[1:]: if sample == current: count += 1 else: if current == 1: pulses.append(count) else: spaces.append(count) count = 1 current = sample return pulses, spaces MRP40 uses a statistical histogram of all pulse lengths. The shortest cluster = dot length.