GraphingIEEE/WavToDb.py at main · DeepeshPatidar/GraphingIEEE · GitHub

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import numpy as np
import pandas as pd
import os
import re
from scipy.io import wavfile
from scipy.signal import lfilter

# ================= USER SETTINGS =================
cal_wav = r"D:\Project\IITDelhi\onosokki\till\REC\CAL_94dB_1kHz_TEST1.wav"  # calibration tone WAV file
test_wav_folder = r"D:\Project\IITDelhi\onosokki\till\REC\input2"
output_folder = r"D:\Project\IITDelhi\onosokki\till\REC\output1"

dt = 0.01          # 10 ms
p_ref = 20e-6      # reference pressure (Pa)
p_cal = 1.0        # 94 dB = 1 Pa
cal_duration = 5  # seconds of calibration tone
# =================================================

os.makedirs(output_folder, exist_ok=True)

# ---------- A-WEIGHTING FILTER ----------
def A_weighting(fs):
    f1, f2, f3, f4 = 20.598997, 107.65265, 737.86223, 12194.217
    A1000 = 1.9997

    nums = [(2*np.pi*f4)**2 * (10**(A1000/20)), 0, 0, 0, 0]
    dens = np.polymul(
        [1, 4*np.pi*f4, (2*np.pi*f4)**2],
        np.polymul(
            [1, 4*np.pi*f1, (2*np.pi*f1)**2],
            np.polymul([1, 2*np.pi*f3], [1, 2*np.pi*f2])
        )
    )
    return nums, dens

# ---------- READ CALIBRATION WAV ----------
fs_cal, cal_sig = wavfile.read(cal_wav)

if cal_sig.ndim > 1:
    cal_sig = cal_sig[:, 0]

cal_sig = cal_sig.astype(np.float64)

N_cal = int(fs_cal * cal_duration)
rms_cal = np.sqrt(np.mean(cal_sig[:N_cal]**2))

cal_gain = p_cal / rms_cal
print(f"Calibration gain applied: {cal_gain:.6e} Pa/unit")

# ---------- PROCESS TEST WAV FILES ----------
for fname in os.listdir(test_wav_folder):
    if not fname.lower().endswith(".wav"):
        continue

    wav_path = os.path.join(test_wav_folder, fname)
    fs, sig = wavfile.read(wav_path)

    if sig.ndim > 1:
        sig = sig[:, 0]

    sig = sig.astype(np.float64) * cal_gain  # convert to Pa

    # A-weighting
    b, a = A_weighting(fs)
    sig_A = lfilter(b, a, sig)

    samples_per_block = int(fs * dt)
    n_blocks = len(sig_A) // samples_per_block

    time = []
    dBA = []

    for i in range(n_blocks):
        block = sig_A[i*samples_per_block:(i+1)*samples_per_block]
        rms = np.sqrt(np.mean(block**2))
        spl = 20 * np.log10(rms / p_ref + 1e-12)
        time.append(i * dt)
        dBA.append(spl)

    # Extract run ID (A025 etc.)
    m = re.search(r"(A\d+)", fname)
    run_id = m.group(1) if m else "UNK"

    df = pd.DataFrame({
        f"dB_{run_id}": dBA,
        "Time": time
    })

    out_csv = fname.replace(".wav", "_dBA_10ms.csv")
    df.to_csv(os.path.join(output_folder, out_csv), index=False)

    print(f"Processed: {fname}")

print("✅ All Pass-by and Coast-by WAV files converted successfully.")