pyroomacoustics.utilities module

pyroomacoustics.utilities.angle_from_points(x1, x2)

pyroomacoustics.utilities.clip(signal, high, low)

Clip a signal from above at high and from below at low.

pyroomacoustics.utilities.compare_plot(signal1, signal2, Fs, fft_size=512, norm=False, equal=False, title1=None, title2=None)

pyroomacoustics.utilities.convmtx(x, n)

Create a convolution matrix H for the vector x of size len(x) times n. Then, the result of np.dot(H,v) where v is a vector of length n is the same as np.convolve(x, v).

pyroomacoustics.utilities.dB(signal, power=False)

pyroomacoustics.utilities.fractional_delay(t0)

Creates a fractional delay filter using a windowed sinc function. The length of the filter is fixed by the module wide constant frac_delay_length (default 81).

Parameters:t0 (float) – The delay in fraction of sample. Typically between 0 and 1. Returns: Return type:A fractional delay filter with specified delay.

pyroomacoustics.utilities.fractional_delay_filter_bank(delays)

Creates a fractional delay filter bank of windowed sinc filters

Parameters:delays (1d narray) – The delays corresponding to each filter in fractional samples Returns:

• An ndarray where the ith row contains the fractional delay filter
• corresponding to the ith delay. The number of columns of the matrix
• is proportional to the maximum delay.

pyroomacoustics.utilities.goertzel(x, k)

Goertzel algorithm to compute DFT coefficients

pyroomacoustics.utilities.highpass(signal, Fs, fc=None, plot=False)

Filter out the really low frequencies, default is below 50Hz

pyroomacoustics.utilities.levinson(r, b)

Solve a system of the form Rx=b where R is hermitian toeplitz matrix and b is any vector using the generalized Levinson recursion as described in M.H. Hayes, Statistical Signal Processing and Modelling, p. 268.

Parameters:

• r – First column of R, toeplitz hermitian matrix.
• b – The right-hand argument. If b is a matrix, the system is solved for every column vector in b.

Returns:

Return type:

The solution of the linear system Rx = b.

pyroomacoustics.utilities.low_pass_dirac(t0, alpha, Fs, N)

Creates a vector containing a lowpass Dirac of duration T sampled at Fs with delay t0 and attenuation alpha.

If t0 and alpha are 2D column vectors of the same size, then the function returns a matrix with each line corresponding to pair of t0/alpha values.

pyroomacoustics.utilities.normalize(signal, bits=None)

normalize to be in a given range. The default is to normalize the maximum amplitude to be one. An optional argument allows to normalize the signal to be within the range of a given signed integer representation of bits.

pyroomacoustics.utilities.normalize_pwr(sig1, sig2)

Normalize sig1 to have the same power as sig2.

pyroomacoustics.utilities.prony(x, p, q)

Prony’s Method from Monson H. Hayes’ Statistical Signal Processing, p. 154

Parameters:

• x – signal to model
• p – order of denominator
• q – order of numerator

Returns:

• a – numerator coefficients
• b – denominator coefficients
• err (the squared error of approximation)

pyroomacoustics.utilities.real_spectrum(signal, axis=-1, **kwargs)

pyroomacoustics.utilities.shanks(x, p, q)

Shank’s Method from Monson H. Hayes’ Statistical Signal Processing, p. 154

Parameters:

• x – signal to model
• p – order of denominator
• q – order of numerator

Returns:

• a – numerator coefficients
• b – denominator coefficients
• err – the squared error of approximation

pyroomacoustics.utilities.spectrum(signal, Fs, N)

pyroomacoustics.utilities.time_dB(signal, Fs, bits=16)

Compute the signed dB amplitude of the oscillating signal normalized wrt the number of bits used for the signal.

pyroomacoustics.utilities.to_16b(signal)

converts float 32 bit signal (-1 to 1) to a signed 16 bits representation No clipping in performed, you are responsible to ensure signal is within the correct interval.