nnAudio.Spectrogram.CQT1992v2¶

class nnAudio.Spectrogram.CQT1992v2(sr=22050, hop_length=512, fmin=32.7, fmax=None, n_bins=84, bins_per_octave=12, norm=1, window='hann', center=True, pad_mode='reflect', trainable=False, output_format='Magnitude', verbose=True)¶

Bases: torch.nn.modules.module.Module

This function is to calculate the CQT of the input signal. Input signal should be in either of the following shapes.

(len_audio)
(num_audio, len_audio)
(num_audio, 1, len_audio)

The correct shape will be inferred autommatically if the input follows these 3 shapes. Most of the arguments follow the convention from librosa. This class inherits from torch.nn.Module, therefore, the usage is same as torch.nn.Module.

This alogrithm uses the method proposed in [1]. I slightly modify it so that it runs faster than the original 1992 algorithm, that is why I call it version 2. [1] Brown, Judith C.C. and Miller Puckette. “An efficient algorithm for the calculation of a constant Q transform.” (1992).

Parameters

sr (int) – The sampling rate for the input audio. It is used to calucate the correct fmin and fmax. Setting the correct sampling rate is very important for calculating the correct frequency.
hop_length (int) – The hop (or stride) size. Default value is 512.
fmin (float) – The frequency for the lowest CQT bin. Default is 32.70Hz, which coresponds to the note C0.
fmax (float) – The frequency for the highest CQT bin. Default is None, therefore the higest CQT bin is inferred from the n_bins and bins_per_octave. If fmax is not None, then the argument n_bins will be ignored and n_bins will be calculated automatically. Default is None
n_bins (int) – The total numbers of CQT bins. Default is 84. Will be ignored if fmax is not None.
bins_per_octave (int) – Number of bins per octave. Default is 12.
norm (int) – Normalization for the CQT kernels. 1 means L1 normalization, and 2 means L2 normalization. Default is 1, which is same as the normalization used in librosa.
window (str) – The windowing function for CQT. It uses scipy.signal.get_window, please refer to scipy documentation for possible windowing functions. The default value is ‘hann’.
center (bool) – Putting the CQT keneral at the center of the time-step or not. If False, the time index is the beginning of the CQT kernel, if True, the time index is the center of the CQT kernel. Default value if True.
pad_mode (str) – The padding method. Default value is ‘reflect’.
trainable (bool) –

Determine if the CQT kernels are trainable or not. If True, the gradients for CQT kernels
will also be caluclated and the CQT kernels will be updated during model training. Default value is False.

output_formatstr
Determine the return type. Magnitude will return the magnitude of the STFT result, shape = (num_samples, freq_bins,time_steps); Complex will return the STFT result in complex number, shape = (num_samples, freq_bins,time_steps, 2); Phase will return the phase of the STFT reuslt, shape = (num_samples, freq_bins,time_steps, 2). The complex number is stored as (real, imag) in the last axis. Default value is ‘Magnitude’.
verbose (bool) – If True, it shows layer information. If False, it suppresses all prints

Returns

spectrogram (torch.tensor)
It returns a tensor of spectrograms.
shape = (num_samples, freq_bins,time_steps) if output_format='Magnitude';
shape = (num_samples, freq_bins,time_steps, 2) if output_format='Complex' or 'Phase';

Examples

>>> spec_layer = Spectrogram.CQT1992v2()
>>> specs = spec_layer(x)

Methods

`__init__`	Initializes internal Module state, shared by both nn.Module and ScriptModule.
`forward`	Convert a batch of waveforms to CQT spectrograms.
`forward_manual`	Method for debugging

forward(x, output_format=None)¶

Convert a batch of waveforms to CQT spectrograms.

Parameters

x (torch tensor) –

Input signal should be in either of the following shapes.

(len_audio)
(num_audio, len_audio)

3. (num_audio, 1, len_audio) It will be automatically broadcast to the right shape

forward_manual(x)¶: Method for debugging