def calculate_cluster_distance(sample, database):
distances = list()
sample_feature_vector = MFCC.voice_feature_extraction(sample)
for database_sample in os.listdir(database):
database_feature_vector = MFCC.voice_feature_extraction(
os.path.join(database, database_sample))
distances.append(
DTW.dynamic_time_warping(sample_feature_vector,
database_feature_vector))
return np.mean(distances)
def calculate_within_cluster_distance(filepath):
feature_vectors = list()
samples = os.listdir(filepath)
for sample in samples:
feature_vectors.append(
MFCC.voice_feature_extraction(os.path.join(filepath, sample)))
distances = list()
for i in range(0, len(feature_vectors)):
for j in range(i + 1, len(feature_vectors)):
distances.append(
DTW.dynamic_time_warping(feature_vectors[i],
feature_vectors[j]))
return np.max(distances)