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Merge pull request #36 from UCSD-E4E/master
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Fixed standard deviation calculation errors and allowed normalization of local score arrays
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@JacobGlennAyers
JacobGlennAyers authored Mar 24, 2021
2 parents 1a853a1 + 9764300 commit d464692
Showing 1 changed file with 40 additions and 21 deletions.
61 changes: 40 additions & 21 deletions microfaune_local_score.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,8 +19,13 @@


# function that encapsulates many different isolation techniques to the dictionary isolation_parameters
def isolate(local_scores, SIGNAL, SAMPLE_RATE, audio_dir, filename,isolation_parameters,manual_id = "bird"):
def isolate(local_scores, SIGNAL, SAMPLE_RATE, audio_dir, filename,isolation_parameters,manual_id = "bird", normalize_local_scores = False):

# normalize the local scores so that the max value is 1.
if normalize_local_scores == True:
local_scores_max = max(local_scores)
for ndx in range(len(local_scores)):
local_scores[ndx] = local_scores[ndx]/local_scores_max
# initializing the output dataframe that will contain labels across a single clip
isolation_df = pd.DataFrame()

Expand Down Expand Up @@ -70,12 +75,15 @@ def steinberg_isolate(local_scores, SIGNAL, SAMPLE_RATE, audio_dir, filename,iso
elif isolation_parameters["threshold_type"] == "mean" or isolation_parameters["threshold_type"] == "average":
thresh = np.mean(local_scores) * isolation_parameters["threshold_const"]
elif isolation_parameters["threshold_type"] == "standard deviation":
thresh = np.std(local_scores) * isolation_parameters["threshold_const"]
elif isolation_parameters["threshold_type"] == "pure" and (isolation_parameters["threshold_const"] < 0 or isolation_parameters["threshold_const"] > 1):
print("A pure threshold must be between [0,1]")
return
thresh = np.mean(local_scores) + (np.std(local_scores) * isolation_parameters["threshold_const"])
elif isolation_parameters["threshold_type"] == "pure":
thresh = isolation_parameters["threshold_const"]
if thresh < 0:
print("Threshold is less than zero, setting to zero")
thresh = 0
elif thresh > 1:
print("Threshold is greater than one, setting to one.")
thresh = 1

# how many samples one score represents
# Scores meaning local scores
Expand Down Expand Up @@ -142,12 +150,15 @@ def simple_isolate(local_scores, SIGNAL, SAMPLE_RATE, audio_dir, filename, isola
elif isolation_parameters["threshold_type"] == "mean" or isolation_parameters["threshold_type"] == "average":
thresh = np.mean(local_scores) * isolation_parameters["threshold_const"]
elif isolation_parameters["threshold_type"] == "standard deviation":
thresh = np.std(local_scores) * isolation_parameters["threshold_const"]
elif isolation_parameters["threshold_type"] == "pure" and (isolation_parameters["threshold_const"] < 0 or isolation_parameters["threshold_const"] > 1):
print("A pure threshold must be between [0,1]")
return
thresh = np.mean(local_scores) + (np.std(local_scores) * isolation_parameters["threshold_const"])
elif isolation_parameters["threshold_type"] == "pure":
thresh = isolation_parameters["threshold_const"]
if thresh < 0:
print("Threshold is less than zero, setting to zero")
thresh = 0
elif thresh > 1:
print("Threshold is greater than one, setting to one.")
thresh = 1

# calculate original duration
old_duration = len(SIGNAL) / SAMPLE_RATE
Expand Down Expand Up @@ -205,12 +216,15 @@ def stack_isolate(local_scores, SIGNAL, SAMPLE_RATE, audio_dir, filename, isolat
elif isolation_parameters["threshold_type"] == "mean" or isolation_parameters["threshold_type"] == "average":
thresh = np.mean(local_scores) * isolation_parameters["threshold_const"]
elif isolation_parameters["threshold_type"] == "standard deviation":
thresh = np.std(local_scores) * isolation_parameters["threshold_const"]
elif isolation_parameters["threshold_type"] == "pure" and (isolation_parameters["threshold_const"] < 0 or isolation_parameters["threshold_const"] > 1):
print("A pure threshold must be between [0,1], exiting function")
return
thresh = np.mean(local_scores) + (np.std(local_scores) * isolation_parameters["threshold_const"])
elif isolation_parameters["threshold_type"] == "pure":
thresh = isolation_parameters["threshold_const"]
if thresh < 0:
print("Threshold is less than zero, setting to zero")
thresh = 0
elif thresh > 1:
print("Threshold is greater than one, setting to one.")
thresh = 1

# calculate original duration
old_duration = len(SIGNAL) / SAMPLE_RATE
Expand Down Expand Up @@ -281,7 +295,7 @@ def stack_isolate(local_scores, SIGNAL, SAMPLE_RATE, audio_dir, filename, isolat


## Function that applies the moment to moment labeling system to a directory full of wav files.
def generate_automated_labels(bird_dir, isolation_parameters, weight_path=None, Normalized_Sample_Rate = 44100):
def generate_automated_labels(bird_dir, isolation_parameters, weight_path=None, Normalized_Sample_Rate = 44100, normalize_local_scores = False):
"""
Function that applies the moment to moment labeling system to a directory full of wav files.
Expand Down Expand Up @@ -342,7 +356,7 @@ def generate_automated_labels(bird_dir, isolation_parameters, weight_path=None,

try:
# Running moment to moment algorithm and appending to a master dataframe.
new_entry = isolate(local_scores[0], SIGNAL, SAMPLE_RATE, bird_dir, audio_file, isolation_parameters, manual_id = "bird")
new_entry = isolate(local_scores[0], SIGNAL, SAMPLE_RATE, bird_dir, audio_file, isolation_parameters, manual_id = "bird", normalize_local_scores=normalize_local_scores)
#print(new_entry)
if annotations.empty == True:
annotations = new_entry
Expand All @@ -360,8 +374,9 @@ def generate_automated_labels(bird_dir, isolation_parameters, weight_path=None,
def annotation_duration_statistics(df):
# Reading in the Duration column of the passed in dataframe as a Python list
annotation_lengths = df["DURATION"].to_list()
# Converting the Python list to a numpy array
# converting to numpy array which has more readily available statistics functions
annotation_lengths = np.asarray(annotation_lengths)
# Converting the Python list to a numpy array
entry = {'COUNT' : np.shape(annotation_lengths)[0],
'MODE' : stats.mode(np.round(annotation_lengths,2))[0][0],
'MEAN' : np.mean(annotation_lengths),
Expand All @@ -375,7 +390,7 @@ def annotation_duration_statistics(df):
return pd.DataFrame.from_dict([entry])


def local_line_graph(local_scores,clip_name, sample_rate,samples, automated_df=None, human_df=None,log_scale = False, save_fig = False):
def local_line_graph(local_scores,clip_name, sample_rate,samples, automated_df=None, human_df=None,log_scale = False, save_fig = False, normalize_local_scores = False):
"""
Function that produces graphs with the local score plot and spectrogram of an audio clip. Now integrated with Pandas so you can visualize human and automated annotations.
Expand All @@ -396,7 +411,11 @@ def local_line_graph(local_scores,clip_name, sample_rate,samples, automated_df=N
duration = samples.shape[0]/sample_rate
# Calculating the number of local scores outputted by Microfaune
num_scores = len(local_scores)

# the case for normalizing the local scores between [0,1]
if normalize_local_scores == True:
local_scores_max = max(local_scores)
for ndx in range(num_scores):
local_scores[ndx] = local_scores[ndx]/local_scores_max
## Making sure that the local score of the x-axis are the same across the spectrogram and the local score plot
step = duration / num_scores
time_stamps = np.arange(0, duration, step)
Expand Down Expand Up @@ -453,7 +472,7 @@ def local_line_graph(local_scores,clip_name, sample_rate,samples, automated_df=N
# TODO rework function so that instead of generating the automated labels, it takes the automated_df as input
# same as it does with the manual dataframe.

def local_score_visualization(clip_path,weight_path = None, human_df = None,automated_df = False, isolation_parameters = None,log_scale = False, save_fig = False):
def local_score_visualization(clip_path,weight_path = None, human_df = None,automated_df = False, isolation_parameters = None,log_scale = False, save_fig = False, normalize_local_scores = False):

"""
Wrapper function for the local_line_graph function for ease of use. Processes clip for local scores to be used for
Expand Down Expand Up @@ -500,11 +519,11 @@ def local_score_visualization(clip_path,weight_path = None, human_df = None,auto
if human_df is None:
human_df = pd.DataFrame
if automated_df == True:
automated_df = isolate(local_score[0],SIGNAL, SAMPLE_RATE,"Doesn't","Matter",isolation_parameters)
automated_df = isolate(local_score[0],SIGNAL, SAMPLE_RATE,"Doesn't","Matter",isolation_parameters, normalize_local_scores = normalize_local_scores)
else:
automated_df = pd.DataFrame()

local_line_graph(local_score[0].tolist(),clip_path,SAMPLE_RATE,SIGNAL,automated_df,human_df,log_scale = log_scale, save_fig = save_fig)
local_line_graph(local_score[0].tolist(),clip_path,SAMPLE_RATE,SIGNAL,automated_df,human_df,log_scale = log_scale, save_fig = save_fig, normalize_local_scores = normalize_local_scores)



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