def METHOD_NAME(
    regtest,
    tmp_path,
    scene: dict | None = None,
    scene_renderer: SceneRenderer = SCENE_RENDERER,
) -> None:
    """Test full CEC2 pipeline"""
    if scene is None:
        scene = SCENE
    target, interferers, anechoic, _head_turn = scene_renderer.generate_hoa_signals(
        scene
    )
    scene_renderer.generate_binaural_signals(
        scene, target, interferers, anechoic, str(tmp_path)
    )
    _, reference = wavfile.read(f"{tmp_path}/S06001_target_anechoic_CH1.wav")
    _, signal = wavfile.read(f"{tmp_path}/S06001_mix_CH1.wav")
    reference = reference.astype(float) / 32768.0
    signal = signal.astype(float) / 32768.0
    # Truncate to 200 ms - i.e. just use part of the signals
    # to speed up the HASPI calculation a little
    signal = signal[:8820, :]
    reference = reference[:8820, :]
    # The data below doesn't really need to be meaningful.
    # The purpose of the test is not to see if the haspi score is reasonable
    # but just to check that the results do not change unexpectedly across releases.
    nalr_cfg = {"nfir": 220, "sample_rate": 44100}
    compressor_cfg = {
        "threshold": 0.35,
        "attenuation": 0.1,
        "attack": 50,
        "release": 1000,
        "rms_buffer_size": 0.064,
    }
    audiogram_l = np.array([45, 50, 60, 65, 60, 65, 70, 80])
    audiogram_r = np.array([45, 45, 60, 70, 60, 60, 80, 80])
    audiogram_cfs = np.array([250, 500, 1000, 2000, 3000, 4000, 6000, 8000])
    audiogram_left = Audiogram(levels=audiogram_l, frequencies=audiogram_cfs)
    audiogram_right = Audiogram(levels=audiogram_r, frequencies=audiogram_cfs)
    sample_rate = 44100
    enhancer = NALR(**nalr_cfg)
    compressor = Compressor(**compressor_cfg)  # type: ignore
    nalr_fir, _ = enhancer.build(audiogram_left)
    out_l = enhancer.apply(nalr_fir, signal[:, 0])
    nalr_fir, _ = enhancer.build(audiogram_right)
    out_r = enhancer.apply(nalr_fir, signal[:, 1])
    out_l, _, _ = compressor.process(out_l)
    out_r, _, _ = compressor.process(out_r)
    enhanced_audio = np.stack([out_l, out_r], axis=1)
    enhanced_audio = np.tanh(enhanced_audio)
    listener = Listener(audiogram_left=audiogram_left, audiogram_right=audiogram_right)
    sii_enhanced = haspi_v2_be(
        reference_left=reference[:, 0],
        reference_right=reference[:, 1],
        processed_left=enhanced_audio[:, 0],
        processed_right=enhanced_audio[:, 1],
        sample_rate=sample_rate,
        listener=listener,
    )
    regtest.write(f"Enhanced audio HASPI score is {sii_enhanced:0.7f}\n")
    # Enhanced audio HASPI score is 0.2994066