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"""
| Description: libf0 utility functions
| Contributors: Sebastian Rosenzweig, Simon Schwär, Meinard Müller
| License: The MIT license, https://opensource.org/licenses/MIT
| This file is part of libf0.
"""
import numpy as np
def sonify_trajectory_with_sinusoid(f0, t, audio_len, confidence=None, Fs=22050, smooth_len=11):
"""
Sonification of trajectory with sinusoidal. Adapted from FMP notebook: C8/C8S2_FundFreqTracking.ipynb
Parameters
----------
f0 : ndarray
F0-trajectory
t : ndarray
Time axis
audio_len : int
Desired audio length in samples
confidence : None or ndarray
Confidence values for amplitude control
Fs : int
Sampling rate
smooth_len : int
Smoothing filter length to avoid clicks in the sonification
Returns
-------
x_soni : ndarray
Sonified F0-trajectory
"""
if confidence is None:
confidence = np.ones_like(f0)
# initialize
x_soni = np.zeros(audio_len)
amplitude_mod = np.zeros(audio_len)
# Computation of hop size
sine_len = int(t[1] * Fs)
t = np.arange(0, sine_len) / Fs
phase = 0
# loop over all F0 values, ensure continuous phase
for idx in np.arange(0, len(f0)):
cur_f = f0[idx]
cur_amp = confidence[idx]
if cur_f == 0:
phase = 0
continue
cur_soni = np.sin(2*np.pi*(cur_f*t+phase))
diff = np.maximum(0, (idx+1)*sine_len - len(x_soni))
if diff > 0:
x_soni[idx * sine_len:(idx + 1) * sine_len - diff] = cur_soni[:-diff]
amplitude_mod[idx * sine_len:(idx + 1) * sine_len - diff] = cur_amp
else:
x_soni[idx*sine_len:(idx+1)*sine_len-diff] = cur_soni
amplitude_mod[idx*sine_len:(idx+1)*sine_len-diff] = cur_amp
phase += cur_f * sine_len / Fs
phase -= 2 * np.round(phase/2)
# filter amplitudes to avoid transients
amplitude_mod = np.convolve(amplitude_mod, np.hanning(smooth_len)/np.sum(np.hanning(smooth_len)), 'same')
x_soni = x_soni * amplitude_mod
return x_soni
def hz_to_cents(F, F_ref=55.0):
"""
Converts frequency in Hz to cents.
Parameters
----------
F : float or ndarray
Frequency value in Hz
F_ref : float
Reference frequency in Hz (Default value = 55.0)
Returns
-------
F_cents : float or ndarray
Frequency in cents
"""
# Avoid division by 0
F_temp = np.array(F).astype(float)
F_temp[F_temp == 0] = np.nan
F_cents = 1200 * np.log2(F_temp / F_ref)
return F_cents
def cents_to_hz(F_cents, F_ref=55.0):
"""
Converts frequency in cents to Hz.
Parameters
----------
F_cents : float or ndarray
Frequency in cents
F_ref : float
Reference frequency in Hz (Default value = 55.0)
Returns
-------
F : float or ndarray
Frequency in Hz
"""
F = F_ref * 2 ** (F_cents / 1200)
# Avoid NaN output
F = np.nan_to_num(F, copy=False, nan=0)
return F
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