def METHOD_NAME(cfg, path) -> None:
    with open(path.scenes_file, encoding="utf-8") as fp:
        scenes = json.load(fp)
    proc_folder = Path(path.exp_folder) / "eval_signals"
    ref_folder = Path(path.ref_folder)
    sii_file = Path(path.exp_folder) / "sii.csv"
    csv_lines = [["signal_ID", "intelligibility_score"]]
    for scene_dict in tqdm(scenes):
        scene = scene_dict["scene"]
        listener = scene_dict["listener"]
        system = scene_dict["system"]
        proc = read_signal(proc_folder / f"{scene}_{listener}_{system}_HL-output.wav")
        clean = read_signal(ref_folder / f"{scene}_target_anechoic.wav")
        ddf = read_signal(proc_folder / f"{scene}_{listener}_{system}_HLddf-output.wav")
        # Calculate channel-specific unit impulse delay due to HL model and audiograms
        delay = find_delay_impulse(ddf, initial_value=int(MSBG_FS / 2))
        if delay[0] != delay[1]:
            logging.info(f"Difference in delay of {delay[0] - delay[1]}.")
        maxdelay = int(np.max(delay))
        # Allow for value lower than 1000 samples in case of unimpaired hearing
        if maxdelay > 2000:
            logging.error("Error in delay calculation for signal time-alignment.")
        # Correct for delays by padding clean signals
        cleanpad = np.zeros((len(clean) + maxdelay, 2))
        procpad = np.zeros((len(clean) + maxdelay, 2))
        if len(procpad) < len(proc):
            raise ValueError("Padded processed signal is too short.")
        cleanpad[int(delay[0]) : int(len(clean) + int(delay[0])), 0] = clean[:, 0]
        cleanpad[int(delay[1]) : int(len(clean) + int(delay[1])), 1] = clean[:, 1]
        procpad[: len(proc)] = proc
        sii = mbstoi(
            cleanpad[:, 0],
            cleanpad[:, 1],
            procpad[:, 0],
            procpad[:, 1],
            cfg["mbstoi"]["sample_rate"],
            cfg["mbstoi"]["grid_coarseness"],
        )
        csv_lines.append([f"{scene}_{listener}_{system}", sii])  # type: ignore
    with open(sii_file, "w", encoding="utf-8") as csv_f:
        csv_writer = csv.writer(
            csv_f, delimiter=",", quotechar='"', quoting=csv.QUOTE_MINIMAL
        )
        for line in csv_lines:
            csv_writer.writerow(line)