142 lines
4.8 KiB
Python
142 lines
4.8 KiB
Python
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import numpy as np
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import time
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from datetime import datetime
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FIFO_PATH = "/tmp/esp32_audio"
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SAMPLE_RATE = 16000
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CHANNELS = 2
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BYTES_PER_SAMPLE = 2
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# Detection and processing params
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BLOCK_FRAMES = 2048 # ~128 ms @16k; large enough to catch an impulse
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IMPULSE_WINDOW = 256 # samples around the detected peak for GCC-PHAT
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BAND_LOW = 1000 # Hz
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BAND_HIGH = 4000 # Hz
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MARGIN = 3.0 # multiplier above rolling baseline for impulse detection
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ALPHA = 0.01 # rolling baseline EMA smoothing
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COOLDOWN = 0.5 # seconds; suppress retriggers from echoes
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# Geometry
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MIC_DISTANCE = 0.20 # meters between microphones
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SPEED_OF_SOUND = 343.0 # m/s
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def read_block(f, block_bytes):
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data = f.read(block_bytes)
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if not data or len(data) < block_bytes:
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return None
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return np.frombuffer(data, dtype=np.int16)
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def bandpass_fft(x, fs, low, high):
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"""Simple FFT band-pass: zero out bins outside [low, high]."""
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n = len(x)
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X = np.fft.rfft(x)
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freqs = np.fft.rfftfreq(n, d=1.0/fs)
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mask = (freqs >= low) & (freqs <= high)
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X_filtered = X * mask
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x_filtered = np.fft.irfft(X_filtered, n=n)
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return x_filtered.astype(x.dtype)
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def gcc_phat(sig, refsig, fs, max_tau=None, interp=1):
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"""
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GCC-PHAT lag estimation between sig and refsig.
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Returns time delay (tau) in seconds.
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"""
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n = sig.shape[0] + refsig.shape[0]
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# FFT
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SIG = np.fft.rfft(sig, n=n)
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REFSIG = np.fft.rfft(refsig, n=n)
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R = SIG * np.conj(REFSIG)
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denom = np.abs(R)
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R = R / (denom + 1e-15)
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cc = np.fft.irfft(R, n=(interp * n))
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if max_tau is None:
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# physical max tau based on mic distance
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max_tau = MIC_DISTANCE / SPEED_OF_SOUND
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max_shift = int(interp * fs * max_tau)
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mid = cc.shape[0] // 2
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cc = np.concatenate((cc[mid - max_shift: mid + max_shift + 1],))
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shift = np.argmax(cc) - max_shift
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tau = shift / float(interp * fs)
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return tau
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def tau_to_angle(tau, mic_distance, speed_of_sound):
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"""
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Convert time difference to angle (-90..+90) assuming linear 2-mic array and far-field.
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"""
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# clamp sin argument to [-1,1]
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arg = (tau * speed_of_sound) / mic_distance
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arg = max(-1.0, min(1.0, arg))
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angle_rad = np.arcsin(arg)
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return np.degrees(angle_rad)
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def main():
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block_bytes = BLOCK_FRAMES * CHANNELS * BYTES_PER_SAMPLE
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baseline = None
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last_trigger = 0.0
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with open(FIFO_PATH, "rb") as f:
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print("Listening: GCC-PHAT + impulse window + band-pass + cooldown")
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while True:
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audio = read_block(f, block_bytes)
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if audio is None:
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continue
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# Split stereo
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left = audio[0::2]
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right = audio[1::2]
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# Compute per-block peak level for rolling baseline
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left_peak = np.max(np.abs(left))
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right_peak = np.max(np.abs(right))
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current_level = (left_peak + right_peak) / 2.0
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if baseline is None:
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baseline = current_level
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continue
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baseline = (1 - ALPHA) * baseline + ALPHA * current_level
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threshold = baseline * MARGIN
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# Cooldown gate
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now = time.time()
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if now - last_trigger < COOLDOWN:
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continue
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# Impulse gate: only proceed if strong spike above rolling threshold
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if max(left_peak, right_peak) <= threshold:
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continue
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# Find impulse index using combined magnitude
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combined = np.abs(left) + np.abs(right)
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peak_idx = int(np.argmax(combined))
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# Window around impulse for robust localization
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half = IMPULSE_WINDOW // 2
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start = max(0, peak_idx - half)
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end = min(len(left), peak_idx + half)
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l_win = left[start:end]
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r_win = right[start:end]
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# Band-pass to 1–4 kHz to reduce low rumble/high hiss
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l_bp = bandpass_fft(l_win.astype(np.float32), SAMPLE_RATE, BAND_LOW, BAND_HIGH)
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r_bp = bandpass_fft(r_win.astype(np.float32), SAMPLE_RATE, BAND_LOW, BAND_HIGH)
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# GCC-PHAT for TDOA
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# cap max_tau to physical limit to avoid spurious peaks
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max_tau = MIC_DISTANCE / SPEED_OF_SOUND
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tau = gcc_phat(l_bp, r_bp, SAMPLE_RATE, max_tau=max_tau, interp=1)
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angle = tau_to_angle(tau, MIC_DISTANCE, SPEED_OF_SOUND)
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# Timestamp and report
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ts = datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]
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louder = "LEFT" if left_peak > right_peak else "RIGHT"
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print(f"[{ts}] Loud impulse: {louder} louder | TDOA={tau*1000:.2f} ms | angle≈{angle:.1f}° "
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f"(baseline={baseline:.1f}, L={left_peak}, R={right_peak})")
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# Arm cooldown
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last_trigger = now
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if __name__ == "__main__":
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main()
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