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.
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. mrp40 morse code decoder
def update_speed_estimate(running_pulses, running_spaces, recent_window=20): recent = running_pulses[-recent_window:] + running_spaces[-recent_window:] dot = min(recent) # or 10th percentile return max(dot, 0.5) # avoid zero Use a small language model or dictionary to suggest corrections when timing is ambiguous. 7.3 Waterfall Display & Spectral Analysis Display real-time FFT to let user tune to the signal visually – essential for MRP40 usability. 8. Real-Time Implementation (Pseudocode) import sounddevice as sd def audio_callback(indata, frames, time, status): audio = indata[:, 0] # mono filtered = bandpass_filter(audio) gained = agc(filtered) envelope = np.abs(hilbert(gained)) binary = adaptive_threshold(envelope) pulses, spaces = extract_run_lengths(binary) dot_ms = estimate_dot_length(pulses, spaces, SAMPLE_RATE) text = decode_from_timings(pulses, spaces, dot_ms) print(text, end='', flush=True) MRP40 is a famous Windows-based software decoder known
def adaptive_threshold(envelope, alpha=0.8, beta=1.5, window_ms=100, fs=8000): window = int(window_ms * fs / 1000) local_peak = np.zeros_like(envelope) for i in range(len(envelope)): start = max(0, i - window) end = min(len(envelope), i + window) local_peak[i] = np.max(envelope[start:end]) threshold = alpha * np.median(local_peak) # hysteresis: on if > beta*threshold, off if < threshold return (envelope > beta * threshold).astype(int) Morse code is defined by dot duration – all other timings are multiples. 5.1 Extract Pulse & Space Lengths From the binary signal, measure consecutive high (pulse) and low (space) runs. System Overview The decoder will transform audio input
7.1 Fist Character Recognition (Speed Tracking) Human senders vary speed. Continuously update T every few symbols.
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'
from scipy.signal import butter, filtfilt def bandpass_filter(data, low=400, high=1000, fs=8000): b, a = butter(4, [low, high], btype='band', fs=fs) return filtfilt(b, a, data) MRP40 adapts to varying signal levels. Implement a sliding RMS window.