transform.py

# The Tonnetz-Cad dataset | 2021
# Emmanouil Karystinaios

import numpy as np
import scipy
from scipy.ndimage.filters import gaussian_filter
from scipy.interpolate import interp1d

# transformations of the templates which will make them harder to classify
def pad(x, padding):
    low, high = padding
    p = low + int(np.random.rand()*(high-low+1))
    return np.concatenate([x, np.zeros((p))])

def shear(x, scale=10):
    coeff = scale*(np.random.rand() - 0.5)
    return x - coeff*np.linspace(-0.5,.5,len(x))

def translate(x, max_translation):
    k = np.random.choice(max_translation)
    return np.concatenate([x[-k:], x[:-k]])

def corr_noise_like(x, scale):
    noise = scale * np.random.randn(*x.shape)
    return gaussian_filter(noise, 2)

def iid_noise_like(x, scale):
    noise = scale * np.random.randn(*x.shape)
    return noise

def interpolate(x, N):
    scale = np.linspace(0,1,len(x))
    new_scale = np.linspace(0,1,N)
    new_x = interp1d(scale, x, axis=0, kind='linear')(new_scale)
    return new_x

def transform(x, y, args, eps=1e-8):
    new_x = pad(x+eps, args.padding) # pad
    new_x = interpolate(new_x, args.template_len + args.padding[-1])  # dilate
    new_y = interpolate(y, args.template_len + args.padding[-1])
    new_x *= (1 + args.scale_coeff*(np.random.rand() - 0.5))  # scale
    new_x = translate(new_x, args.max_translation)  #translate
    
    # add noise
    mask = new_x != 0
    new_x = mask*new_x + (1-mask)*corr_noise_like(new_x, args.corr_noise_scale)
    new_x = new_x + iid_noise_like(new_x, args.iid_noise_scale)
    
    # shear and interpolate
    new_x = shear(new_x, args.shear_scale)
    new_x = interpolate(new_x, args.final_seq_length) # subsample
    new_y = interpolate(new_y, args.final_seq_length)
    return new_x, new_y