syllable = np.array(['aa','r'])
avg_bgd, syllable_examples, backgrounds = gtrd.get_syllable_examples_backgrounds_files(train_data_path,
file_indices,
syllable,
num_examples=-1,
verbose=True)
clipped_bgd = np.clip(avg_bgd.E,.1,.4)
np.save(tmp_data_path+'clipped_bgd_102012.npy',clipped_bgd)
padded_examples, lengths = et.extend_examples_to_max(clipped_bgd,syllable_examples,
return_lengths=True)
aar_template, aar_registered = et.register_templates_time_zero(syllable_examples,min_prob=.01)
test_example_lengths = gtrd.get_detect_lengths(data_path+'Test/')
np.save(tmp_data_path+'test_example_lengths_102012.npy',test_example_lengths)
detection_array = np.zeros((test_example_lengths.shape[0],
test_example_lengths.max() + 2),dtype=np.float32)
linear_filter,c = et.construct_linear_filter(aar_template,
clipped_bgd)
# need to state the syllable we are working with
syllable = np.array(['aa','r'])
import template_speech_rec.get_train_data as gtrd log_part_blocks, log_invpart_blocks = gtrd.reorg_parts_for_fast_filtering(parts) log_part_blocks = log_part_blocks.astype(np.float32) log_invpart_blocks = log_invpart_blocks.astype(np.float32) # get the basic examples aar_examples = np.load(tmp_data_path+'aar_examples.npy') aar_lengths = np.load(tmp_data_path + 'aar_lengths.npy') clipped_bgd = np.load(tmp_data_path+'clipped_train_bgd.npy') import template_speech_rec.estimate_template as et aar_template, aar_registered = et.register_templates_time_zero(aar_examples,aar_lengths,min_prob=.01) #test_example_lengths = gtrd.get_detect_lengths(data_path+'Test/') import template_speech_rec.bernoulli_mixture as bm # # we are now going to do the clustering procedure on these # aar_examples = np.load(tmp_data_path+'aar_examples.npy') aar_lengths = np.load(tmp_data_path + 'aar_lengths.npy') clipped_bgd = np.load(tmp_data_path+'clipped_train_bgd.npy')
