nnAudio.utils¶

Module containing helper functions such as overlap sum and Fourier kernels generators

Functions

`broadcast_dim`	Auto broadcast input so that it can fits into a Conv1d
`broadcast_dim_conv2d`	Auto broadcast input so that it can fits into a Conv2d
`complex_mul`	Since PyTorch does not support complex numbers and its operation.
`create_cqt_kernels`	Automatically create CQT kernels in time domain
`create_fourier_kernels`	This function creates the Fourier Kernel for STFT, Melspectrogram and CQT.
`create_lowpass_filter`	Calculate the highest frequency we need to preserve and the lowest frequency we allow to pass through.
`downsampling_by_2`	A helper function that downsamples the audio by half.
`downsampling_by_n`	A helper function that downsamples the audio by a arbitary factor n.
`early_downsample`	Return new sampling rate and hop length after early dowansampling
`early_downsample_count`	Compute the number of early downsampling operations
`extend_fbins`	Extending the number of frequency bins from n_fft//2+1 back to n_fft by reversing all bins except DC and Nyquist and append it on top of existing spectrogram
`get_cqt_complex`	Multiplying the STFT result with the cqt_kernel, check out the 1992 CQT paper [1] for how to multiple the STFT result with the CQT kernel [2] Brown, Judith C.C.
`get_early_downsample_params`	Used in CQT2010 and CQT2010v2
`nextpow2`	A helper function to calculate the next nearest number to the power of 2.
`overlap_add`
`prepow2`	A helper function to calculate the next nearest number to the power of 2.
`torch_window_sumsquare`
`uniform_distribution`