Ponderation curves #32
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| if curve.lower() == "z": | ||
| return segment.dBFS | ||
| if curve.lower() == "a": | ||
| b = np.array([0.25574113, -0.51148225, -0.25574113, 1.0229645, -0.25574113, |
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@jajaxdlol Are these hardcoded numbers only for the 44100 frequency?
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Those are hardcoded for 44100 freq yes. I'm not good at filter design so I have no idea on how to create a function to create curves according to frequency, I wasn't sure if you wanted to add a dependency to another library.
| b = np.array([0.21700856, 0., -0.43401712, 0., 0.21700856]), | ||
| a = np.array([1., -2.13467496, 1.27933353, -0.14955985, 0.0049087]) | ||
| y = np.float32(lfilter(b, a, segment.get_array_of_samples())) | ||
| audio_segment = pydub.AudioSegment(y.tobytes(), frame_rate=44100, sample_width=y.dtype.itemsize, channels=1) |
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Shall we pull the config for frame_rate from self.config.RATE?
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You're right! But for that, we should have weighting curves that depends on frequency.
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@jajaxdlol Thank you for your PR. Looks a very cool feature! I left some comment in your code. I was wondering if these can be applied to different rates |
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If you want we can make it as long as I told you, you don't mind to add dependency to another python library which allows to create filters according to frequency. |
Added some functions that could be helpful to someone.
Also added the possibility of execute module meter.py with the included main directly.