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""" |
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.. image:: ../logo.png |
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Julius contains different Digital Signal Processing algorithms implemented |
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with PyTorch, so that they are differentiable and available on CUDA. |
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Note that all the modules implemented here can be used with TorchScript. |
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For now, I have implemented: |
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- `julius.resample`: fast sinc resampling. |
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- `julius.fftconv`: FFT based convolutions. |
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- `julius.lowpass`: FIR low pass filter banks. |
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- `julius.filters`: FIR high pass and band pass filters. |
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- `julius.bands`: Decomposition of a waveform signal over mel-scale frequency bands. |
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Along that, you might found useful utilities in: |
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- `julius.core`: DSP related functions. |
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- `julius.utils`: Generic utilities. |
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Please checkout [the Github repository](https://github.com/adefossez/julius) for other informations. |
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For a verification of the speed and correctness of Julius, check the benchmark module `bench`. |
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This package is named in this honor of |
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[Julius O. Smith](https://ccrma.stanford.edu/~jos/), |
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whose books and website were a gold mine of information for me to learn about DSP. Go checkout his website if you want |
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to learn more about DSP. |
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""" |
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from .bands import SplitBands, split_bands |
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from .fftconv import fft_conv1d, FFTConv1d |
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from .filters import bandpass_filter, BandPassFilter |
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from .filters import highpass_filter, highpass_filters, HighPassFilter, HighPassFilters |
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from .lowpass import lowpass_filter, lowpass_filters, LowPassFilters, LowPassFilter |
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from .resample import resample_frac, ResampleFrac |
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