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