#!/usr/bin/env python
# coding: utf-8

# # Introduction to pysptk
# 
# This notebook shows a few typical usages of pysptk, with a focus on a spectral parameter estimation. The steps are composed of:
# 
# - windowing
# - mel-generalized cepstrum analysis
# - visualize spectral envelope estimates
# - F0 estimation
# 
# ## Requirements
# 
# - pysptk: https://github.com/r9y9/pysptk
# - seaborn: https://github.com/mwaskom/seaborn
# - scipy: https://github.com/scipy/scipy

# In[1]:


get_ipython().run_line_magic('pylab', 'inline')


# In[2]:


import matplotlib
import seaborn
seaborn.set_style("dark")
rcParams['figure.figsize'] = (16, 6)


# In[3]:


import numpy as np
import pysptk
from scipy.io import wavfile


# ## Data

# In[4]:


fs, x = wavfile.read(pysptk.util.example_audio_file())
assert fs == 16000


# In[5]:


plot(x)
xlim(0, len(x))
title("raw waveform of example audio file")


# ## Windowing

# In[6]:


# Pick a short segment
pos = 40000
frame_length = 1024

xw = x[pos:pos+frame_length] * pysptk.blackman(frame_length)

plot(xw, linewidth=3.0)
xlim(0, frame_length)
title("a windowed time frame")


# In[7]:


# plotting utility
def pplot(sp, envelope, title="no title"):
    plot(sp, "b-", linewidth=2.0, label="Original log spectrum 20log|X(w)|")
    plot(20.0/np.log(10)*envelope, "r-", linewidth=3.0, label=title)
    xlim(0, len(sp))
    xlabel("frequency bin")
    ylabel("log amplitude")
    legend(prop={'size': 20})


# ## Spectral parameter estimation and visualize it's spectral envelop estimate

# In[8]:


# Compute spectrum 20log|X(w)| for a windowed signal
sp = 20*np.log10(np.abs(np.fft.rfft(xw)))


# In[9]:


mgc = pysptk.mgcep(xw, 20, 0.0, 0.0)
pplot(sp, pysptk.mgc2sp(mgc, 0.0, 0.0, frame_length).real, title="Lineaer frequency cepstrum based envelope")


# In[10]:


mgc = pysptk.mcep(xw, 20, 0.41)
pplot(sp, pysptk.mgc2sp(mgc, 0.41, 0.0, frame_length).real, title="Mel-cepstrum based envelope")


# In[11]:


mgc = pysptk.mgcep(xw, 20, 0.0, -1.0)
pplot(sp, pysptk.mgc2sp(mgc, 0.0, -1.0, frame_length).real, title="LPC cepstrum based envelope")


# In[12]:


mgc = pysptk.mgcep(xw, 20, 0.41, -1.0)
pplot(sp, pysptk.mgc2sp(mgc, 0.41, -1.0, frame_length).real, title="Warped LPC cepstrum based envelope")


# In[13]:


mgc = pysptk.gcep(xw, 20, -0.35)
pplot(sp, pysptk.mgc2sp(mgc, 0.0, -0.35, frame_length).real, title="Generalized cepstrum based envelope")


# In[14]:


mgc = pysptk.mgcep(xw, 20, 0.41, -0.35)
pplot(sp, pysptk.mgc2sp(mgc, 0.41, -0.35, frame_length).real, title="Mel-generalized cepstrum based envelope")


# ## F0 estimation
# 
# pysptk supports two f0 estimation algorithms; SWIPE' and RAPT'. For more detailed information, pleases see the reference manual of the SPTK.

# In[15]:


f0_swipe = pysptk.swipe(x.astype(np.float64), fs=fs, hopsize=80, min=60, max=200, otype="f0")
plot(f0_swipe, linewidth=3, label="F0 trajectory estimated by SWIPE'")

f0 = pysptk.rapt(x.astype(np.float32), fs=fs, hopsize=80, min=60, max=200, otype="f0")
plot(f0, linewidth=3, label="F0 trajectory estimated by RAPT")

xlim(0, len(f0_swipe))
legend(prop={'size': 18})