def METHOD_NAME(self, n_fft, hop_length, n_mels, n_mfcc):
    sample_rate = 16000
    waveform = get_whitenoise(sample_rate=sample_rate, n_channels=1).to(self.device, self.dtype)
    result = T.MFCC(
        sample_rate=sample_rate,
        n_mfcc=n_mfcc,
        norm="ortho",
        melkwargs={"hop_length": hop_length, "n_fft": n_fft, "n_mels": n_mels},
    ).to(self.device, self.dtype)(waveform)[0]
    melspec = librosa.feature.melspectrogram(
        y=waveform[0].cpu().numpy(),
        sr=sample_rate,
        n_fft=n_fft,
        win_length=n_fft,
        hop_length=hop_length,
        n_mels=n_mels,
        htk=True,
        norm=None,
        pad_mode="reflect",
    )
    expected = librosa.feature.mfcc(
        S=librosa.core.spectrum.power_to_db(melspec), n_mfcc=n_mfcc, dct_type=2, norm="ortho"
    )
    self.assertEqual(result, torch.from_numpy(expected), atol=5e-4, rtol=1e-5)