new_contents: from bird import preprocessing as pp
  from bird import utils
  
  filename = "datasets/mlsp2013/train/PC10_20090513_054500_0020.wav"
  (fs, x) = utils.read_wave_file(filename)
  (t, f, Sxx) = utils.wave_to_spectrogram(x, fs)
  n_mask = pp.compute_signal_mask(Sxx)
  n_mask_scaled = pp.reshape_binary_mask(n_mask, x.shape[0])
  print(n_mask)
  print(n_mask_scaled)
  Nxx = pp.normalize(Sxx)
  
  def test():
      "Stupid test function"
      L = [i for i in range(100)]
  
  def compute_spectrogram():
  	utils.wave_to_spectrogram(x, fs)
  
  def extract_signal_part():
  	pp.extract_signal_part(Sxx)
  
  def extract_noise_part():
  	pp.extract_noise_part(Sxx)
  
  def extract_masked_part_from_wave():
  	pp.extract_masked_part_from_wave(n_mask_scaled, x)
  
  def compute_noise_mask():
  	pp.compute_noise_mask(Sxx)
  
  def compute_signal_mask():
  	pp.compute_signal_mask(Sxx)
  
  def reshape_binary_mask():
  	pp.reshape_binary_mask(n_mask, 160000)
  
  def preprocess_wave():
  	utils.preprocess_wave(x, fs)
  
  def read_wave_file():
  	utils.read_wave_file(filename)
  
  def mark_cells_times_larger_than_median():
  	pp.mark_cells_times_larger_than_median(Nxx, 3.0)
  
  if __name__=='__main__':
  	import timeit
  	number = 10
  	print("compute_spectrogram():", timeit.timeit("compute_spectrogram()", setup="from __main__ import compute_spectrogram", number=number))
  	print("read_wave_file():", timeit.timeit("read_wave_file()", setup="from __main__ import read_wave_file", number=number))
  	print("mark_cells_times_larger_than_median():", timeit.timeit("mark_cells_times_larger_than_median()", setup="from __main__ import mark_cells_times_larger_than_median", number=number))
  	print("compute_noise_mask():", timeit.timeit("compute_noise_mask()", setup="from __main__ import compute_noise_mask", number=number))
  	print("compute_signal_mask():", timeit.timeit("compute_signal_mask()", setup="from __main__ import compute_signal_mask", number=number))
  	print("reshape_binary_mask():", timeit.timeit("reshape_binary_mask()", setup="from __main__ import reshape_binary_mask", number=number))
  	print("extract_masked_part_from_wave():", timeit.timeit("extract_masked_part_from_wave()", setup="from __main__ import extract_masked_part_from_wave", number=number))
  	print("preprocess_wave():", timeit.timeit("preprocess_wave()", setup="from __main__ import preprocess_wave", number=number))
  
subject: Add a benchmark file