Fractional Octave Filter Bank¶

References¶

Functions¶

class pyfilterbank.octbank.FractionalOctaveFilterbank(sample_rate=44100, order=4, nth_oct=3.0, norm_freq=1000.0, start_band=-19, end_band=13, edge_correction_percent=0.01, filterfun='cffi')[source]¶

Fractional octave filter bank with second order section butterworth band passes.

Parameters:

sample_rate : int Sampling rate of the signals to be filtered. order : int Filter order of the bands. As this are second order sections, it has to be even. Otherweise you’ll get an error. nth_oct : scalar Number of bands per octave. norm_freq : scalar This is the reference frequency for all fractional octaves placed around this band. start_band : int First Band number of fractional octaves below norm_freq. end_band : int Last band number of fractional octaves above norm_freq. edge_correction_percent : scalar Percentage of widening or narrowing the bands. filterfun : {‘cffi’, ‘py’, ‘cprototype’} Function used by the method filter().

Examples

>>> from pyfilterbank import FractionalOctaveFilterbank
>>> from pylab import plt, np
>>>
>>> sample_rate = 44100
>>> ofb = FractionalOctaveFilterbank(sample_rate, order=4)
>>>
>>> x = np.random.randn(4*sample_rate)
>>> y, states = ofb.filter(x)
>>> L = 10 * np.log10(np.sum(y*y,axis=0))
>>> plt.plot(L)

Attributes

center_frequencies	(ndarray)
band_edges	(ndarray) Frequencies at -3 dB point for all band passes. This are the cross sections of the bands if no edge correction applied.
sosmat	(ndarray) Filter coefficient matrix with second order section band passes.
num_bands	(int) Number of frequency bands in the filter bank.
band_widths	(ndarray) The -3 dB band width of each band pass in the filter bank.

Methods

effective_filter_lengths¶

Returns an estimate of the effective filter length

filter(x, ffilt=False, states=None)[source]¶

Filters the input by the settings of the filterbank object.

Parameters:	x : ndarray Input signal (Nx0) ffilt : bool Forward and backward filtering, if Ture. states : dict States of all filter sections in the filterbank. Initial you can states=None before block process.
Returns:	y : ndarray Fractional octave signals of the filtered input x states : dict Dictionary containing all filter section states.

filter_mimo_c(x, states=None)[source]¶

Filters the input by the settings of the filterbank object.

It supports multi channel audio and returns a 3-dim ndarray. Only for real valued signals. No ffilt (backward forward filtering) implemented in this method.

Parameters:	x : ndarray Signal to be filtered. states : ndarray or None States of the filter sections (for block processing).
Returns:	signal : ndarray Signal array (NxBxC), with N samples, B frequency bands and C-signal channels. states : ndarray Filter states of all filter sections.

set_filterfun(filterfun_name)[source]¶

Set the function that is used for filtering with the method self.filter.

Parameters:	filterfun_name : {‘cffi’, ‘py’, ‘cprototype’} Three different filter functions, ‘cffi’ is the fastest, ‘py’ is implemented with lfilter.

class pyfilterbank.octbank.ThirdOctFFTLevel(fmin=30, fmax=17000, nfft=16384, fs=44100, flag_mean=False)[source]¶

Third octave levels by fft. TODO: rename variables TODO: Write Documentation

Methods

pyfilterbank.octbank.centerfreq_to_bandnum(center_freq, norm_freq, nth_oct)[source]¶

Returns band number from given center frequency.

pyfilterbank.octbank.design_sosmat_band_passes(order, band_edges, sample_rate, edge_correction_percent=0.0)[source]¶

Return matrix containig sos coeffs of bandpasses.

Parameters:	order : int Order of the band pass filters. band_edges : ndarray Band edge frequencies for the bandpasses. sample_rate : scalar Sample frequency. edge_correction_percent : scalar Percentage for the correction of the bandedges. Float between -100 % and 100 %. It can be helpfull dependent on the used filter order. p > 0 widens the band passes.
Returns:	sosmat : ndarray Second order section coefficients. Each column is one band pass cascade of coefficients.

pyfilterbank.octbank.design_sosmat_low_pass_high_pass_bounds(order, band_edges, sample_rate)[source]¶

Returns matrix containing sos coeffs of low and highpass. The cutoff frequencies are placed at the first and last band edge.

Note

This funtion is not used anymore.

Parameters:	order : int Order of the band pass filters. band_edges : ndarray Band edge frequencies for the low an highpass. sample_rate : scalar Sample rate.
Returns:	sosdict : ndarray Second order section coefficients, the first column contains the low pass coefs and the second column contains the highpass coeffs.

pyfilterbank.octbank.example_plot()[source]¶

Creates a plot with freqz() of the default FractionalOctaveFilterbank.

pyfilterbank.octbank.find_nominal_freq(center_frequencies, nominal_frequencies)[source]¶

Find the nearest nominal frequencies to a given array.

Parameters:	center_frequencies : ndarray Some frequencies for those the neares neighbours shall be found. nominal_frequencies : ndarray The nominal frequencies we want to get the best fitting values to center_frequencies from.
Returns:	nominal_frequencies : generator object The neares neighbors nomina freqs to the given frequencies.

pyfilterbank.octbank.frequencies_fractional_octaves(start_band, end_band, norm_freq, nth_oct)[source]¶

Return center and band edge frequencies of fractional octaves.

Parameters:	start_band : int The starting center frequency at norm_freq`*2^(`start_band/nth_oct). end_band : int The last center frequency at norm_freq`*2^(`end_band/nth_oct). norm_freq : scalar The center frequency of the band number 0. nth_oct : scalar The distance between the center frequencies. For third octaves nth_oct=3.
Returns:	center_frequencies : ndarray Frequencies spaced in nth_oct from start_band to end_band with the norm_freq at band number 0. band_edges : ndarray Edge frequencies (-3 dB points) of the fractional octave bands. With constant relative Bandwidth.

pyfilterbank.octbank.freqz(ofb, length_sec=6, ffilt=False, plot=True)[source]¶

Computes the IR and FRF of a digital filter.

Parameters:	ofb : FractionalOctaveFilterbank object length_sec : scalar Length of the impulse response test signal. ffilt : bool Backard forward filtering. Effectiv order is doubled then. plot : bool Create Plots or not.
Returns:	x : ndarray Impulse test signal. y : ndarray Impules responses signal of the filters. f : ndarray Frequency vector for the FRF. Y : Frequency response (FRF) of the summed filters.

pyfilterbank.octbank.to_normalized_frequencies(frequencies, sample_rate, clip=True)[source]¶

Returns normalized frequency array.

Parameters:	frequencies : ndarray Vector with given frequencies. sample_rate : scalar The sample rate. Frequencies beyond Nyquist criterion will be truncated.
Returns:	normalized_frequencies : ndarray Normalized, truncated frequency array.