def METHOD_NAME(config: DictConfig) -> None:
    """
    Run the music enhancement.
    The baseline system is a dummy processor that returns the input signal.
    Args:
        config (dict): Dictionary of configuration options for enhancing music.
    """
    enhanced_folder = Path("enhanced_signals") / config.evaluate.split
    # Load scenes and listeners depending on config.evaluate.split
    scenes, listener_dict, scenes_listeners = load_listeners_and_scenes(config)
    scene_listener_pairs = make_scene_listener_list(
        scenes_listeners, config.evaluate.small_test
    )
    scene_listener_pairs = scene_listener_pairs[
        config.evaluate.batch :: config.evaluate.batch_size
    ]
    for scene_id, listener_id in tqdm(scene_listener_pairs):
        current_scene = scenes[scene_id]
        listener = listener_dict[listener_id]
        song_path = Path(config.path.music_dir) / f"{current_scene['song_path']}"
        # Read song
        song_waveform, _ = read_mp3(song_path, config.sample_rate)
        out_l, out_r = enhance_song(
            waveform=song_waveform, listener=listener, config=config
        )
        enhanced = np.stack([out_l, out_r], axis=1)
        filename = f"{scene_id}_{listener.id}_{current_scene['song']}.wav"
        enhanced_folder_listener = enhanced_folder / f"{listener.id}"
        enhanced_folder_listener.mkdir(parents=True, exist_ok=True)
        # Clip and save
        if config.soft_clip:
            enhanced = np.tanh(enhanced)
        n_clipped = np.sum(np.abs(enhanced) > 1.0)
        if n_clipped > 0:
            logger.warning(f"Writing {filename}: {n_clipped} samples clipped")
        np.clip(enhanced, -1.0, 1.0, out=enhanced)
        signal_16 = (32768.0 * enhanced).astype(np.int16)
        wavfile.write(
            enhanced_folder_listener / filename, config.sample_rate, signal_16
        )