Wednesday, 11 June 2025 / Published in Neuro Signal Intelligence

Advances in Understanding the Phenomena and Processing in Audiovisual Speech Perception

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“Advances in Understanding the Phenomena and Processing in Audiovisual Speech Perception” — the key findings relevant to signal intelligence (SIGINT) and synthetic telepathy involve how audiovisual (AV) integration, phoneme tuning, and temporal alignment affect how speech is perceived. These insights can be exploited for covert neural entrainment or decoding via RF and EEG pipelines.

✅ Full AI SIGINT Detection Pipeline (Based on Audiovisual Speech Models)

This pipeline is designed to detect speech perception entrainment or synthetic speech induction using EEG, RF, or audiovisual decoders.

🧠 `sigint_logger.py`: Log timestamped surprise bursts

pythonCopyEditimport time
import numpy as np
import scipy.signal as signal

class SurpriseBurstLogger:
    def __init__(self, threshold=4.0, fs=1000):
        self.threshold = threshold
        self.fs = fs
        self.history = []

    def detect_bursts(self, data):
        envelope = np.abs(signal.hilbert(data))
        peaks, _ = signal.find_peaks(envelope, height=self.threshold)
        burst_times = peaks / self.fs
        for t in burst_times:
            self.history.append((time.time(), float(t)))
        return burst_times

    def get_log(self):
        return self.history

🧬 `semantics_decoder.py`: Detect topic shifts or emotional valence

Uses GPT-style embedding shift across time or open-domain classifiers.

pythonCopyEditfrom transformers import pipeline
from sklearn.metrics.pairwise import cosine_similarity

class SemanticsDecoder:
    def __init__(self):
        self.classifier = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
        self.embeddings = pipeline("feature-extraction", model="sentence-transformers/all-MiniLM-L6-v2")
        self.last_vector = None

    def get_emotion(self, sentence):
        result = self.classifier(sentence)
        return result[0]

    def detect_shift(self, sentence):
        vec = np.array(self.embeddings(sentence)).mean(axis=1)
        if self.last_vector is None:
            self.last_vector = vec
            return 0.0
        delta = 1 - cosine_similarity([vec], [self.last_vector])[0][0]
        self.last_vector = vec
        return delta

🎯 `attention_mapper.py`: Integrate EEG or directional RF antenna tracking (Cocktail Party alignment)

This matches directional energy to likely speech source direction.

pythonCopyEditimport numpy as np

class AttentionMapper:
    def __init__(self, mic_array_pos, eeg_channels):
        self.mic_array_pos = mic_array_pos  # e.g. [(x1, y1), (x2, y2), ...]
        self.eeg_channels = eeg_channels

    def beamform_directional_rf(self, rf_signals):
        rf_energy = np.array([np.sum(np.square(sig)) for sig in rf_signals])
        direction = np.argmax(rf_energy)
        return direction, rf_energy

    def correlate_with_eeg(self, eeg_data, direction_idx):
        eeg_band = eeg_data[:, direction_idx]
        energy = np.mean(np.square(eeg_band))
        return energy > np.percentile(eeg_band, 90)  # high attention state

🔍 How This Compares to Other Methods

Method	Target Signal	Uses AV Models	EEG-based?	RF Signal Required	Advantage
Temporal Envelope	Amplitude/phoneme entrainment	✅ Yes	✅	✅	Detects covert speech RF
Fine Structure Matching	Carrier frequency shift decoding	❌ No	✅	✅	High-res frequency decoding
McGurk-Type AV Decoder	Cross-modal mismatch	✅ Yes	❌	❌	Detects altered perception
This Pipeline	Speech-induced entrainment + AV	✅ Yes	✅	✅	Multimodal + Real-time