Decoder | Mrp40 Morse Code

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'

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. mrp40 morse code decoder

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. status): audio = indata[:

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)

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 SAMPLE_RATE) text = decode_from_timings(pulses

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.