def what_was_loudness(dataset, narrative, rel_diff=0.1):
questions = [
'What was the <AL> sound?',
'What was the <AL> sound you [heard,listened to]?',
'What was the <AL> sound that you [heard,listened to]?',
'What was the <AL> sound that was heard?',
]
question = str(np.random.choice(questions)) # sample question
loudness = sample_absolute_loudness()
question = question.replace('<AL>', loudness) # insert loudness
question = sanitize_question(question) # correct grammar
lst_events = get_lst_events(narrative)
lst_loudness = get_lst_loudness(narrative)
if 'loud' in question:
est = np.argmax(lst_loudness)
elif 'quiet' in question:
est = np.argmin(lst_loudness)
else:
assert False, \
'Loudness illdefined in Question (what_was_loudness).'
# Assert a good margin in relative loudness
evt_loudness = lst_loudness[est]
x_loudness = [j for i, j in enumerate(lst_loudness) if i != est]
rel_loudness_diff = compute_rel_diff(np.array(x_loudness),
np.array(evt_loudness))
assert np.sum(rel_loudness_diff < rel_diff) <= 0, \
'Question (what_was_loudness) illposed.'
e = lst_events[est]
answer = (str(np.random.choice(dataset['sources'][e])) + ' ' +
str(np.random.choice(dataset['actions'][e])))
return question, answer
def compare_same_loudness_ordinal(dataset, narrative, rel_diff=0.1):
questions = [
'Was the <O1> [sound event,sound] [roughly,approximately] as <L> as the <O2> [sound event,sound]?', # noqa: E501
'Was the <O1> and <O2> [sound events,sounds] [roughly,approximately] as <L>?', # noqa: E501
'[Comparing,Listening to,Hearing] the <O1> [sound event,sound] and the <O2> [sound event,sound], were they [roughly,approximately] as loud?', # noqa: E501
'[Comparing,Listening to,Hearing] the <O1> [sound event,sound] and the <O2> [sound event,sound], did they [roughly,approximately] have the same loudness?', # noqa: E501
'[Comparing,Listening to,Hearing] the <O1> and <O2> [sound events,sounds], were they [roughly,approximately] as loud?', # noqa: E501
'[Comparing,Listening to,Hearing] the <O1> and <O2> [sound events,sounds], did they have [roughly,approximately] the same loudness?', # noqa: E501
]
question = str(np.random.choice(questions)) # sample question
lst_events = get_lst_events(narrative)
number_1, ordinal_1 = sample_number(len(lst_events))
loudness = sample_loudness()
number_2, ordinal_2 = sample_second_number(len(lst_events), number_1)
assert number_1 != number_2, 'Question (compare_same_loudness_ordinal) illposed.'
question = question.replace('<O1>', ordinal_1) # insert ordinal
question = question.replace('<L>', loudness) # insert loudness
question = question.replace('<O2>', ordinal_2) # insert ordinal
question = sanitize_question(question) # correct grammar
lst_loudness = get_lst_loudness(narrative)
e_1_loudness = lst_loudness[number_1 - 1]
e_2_loudness = lst_loudness[number_2 - 1]
rel_loudness_diff = compute_rel_diff(np.array(e_1_loudness),
np.array(e_2_loudness))
# Assert a good margin in relative loudness
assert np.sum(np.logical_and(rel_loudness_diff > rel_diff,
rel_loudness_diff < (2 * rel_diff))) <= 0, \
'Question (compare_same_loudness_ordinal) illposed.'
answer = 'yes' if rel_loudness_diff <= rel_diff else 'no'
return question, answer
def what_was_loudness_relative(dataset, narrative, rel_diff=0.1):
questions = [
'What was the <AL> sound <RO> the <S> <A>?',
'What was the <AL> sound <RO> [hearing,listening to] the <S> <A>?',
'What was the <AL> sound <RO> the <S> <A> was heard?',
]
question = str(np.random.choice(questions)) # sample question
loudness = sample_absolute_loudness()
preposition = sample_preposition()
lst_events = get_lst_events(narrative)
unique_lst_events = [e for e in lst_events if lst_events.count(e) == 1]
assert len(unique_lst_events) > 0, \
'Question (what_was_loudness_relative) illposed.'
event = str(np.random.choice(unique_lst_events))
source = str(np.random.choice(dataset['sources'][event])) # sample source
action = str(np.random.choice(dataset['actions'][event])) # sample action
question = question.replace('<AL>', loudness) # insert loudness
question = question.replace('<RO>', preposition) # insert preposition
question = question.replace('<S>', source) # insert source
question = question.replace('<A>', action) # insert action
question = sanitize_question(question) # correct grammar
assert lst_events.count(event) == 1, \
'Question (what_was_loudness_relative) illposed.'
lst_loudness = get_lst_loudness(narrative)
event_idx = lst_events.index(event)
if 'before' in question:
lst_events_e = lst_events[:event_idx]
lst_events_l = lst_loudness[:event_idx]
elif 'after' in question:
lst_events_e = lst_events[(event_idx + 1):]
lst_events_l = lst_loudness[(event_idx + 1):]
else:
assert False, \
'Preposition illdefined in Question (what_was_loudness_relative).'
assert len(lst_events_e) > 0, \
'Question (what_was_loudness_relative) illposed.'
if 'loud' in question:
est = np.argmax(lst_events_l)
elif 'quiet' in question:
est = np.argmin(lst_events_l)
else:
assert False, \
'Loudness illdefined in Question (what_was_loudness_relative).'
# Assert a good margin in relative loudness
evt_loudness = lst_events_l[est]
x_loudness = [j for i, j in enumerate(lst_events_l) if i != est]
rel_loudness_diff = compute_rel_diff(np.array(x_loudness),
np.array(evt_loudness))
assert np.sum(rel_loudness_diff < rel_diff) <= 0, \
'Question (what_was_loudness_relative) illposed.'
e = lst_events_e[est]
answer = (str(np.random.choice(dataset['sources'][e])) + ' ' +
str(np.random.choice(dataset['actions'][e])))
return question, answer
def what_was_loudness_relative_ordinal(dataset, narrative, rel_diff=0.1):
questions = [
'What was the <AL> sound <RO> the <O> sound?',
'What was the <AL> sound <RO> [hearing,listening to] the <O> sound?',
'What was the <AL> sound <RO> the <O> sound was heard?',
]
question = str(np.random.choice(questions)) # sample question
loudness = sample_absolute_loudness()
preposition = sample_preposition()
lst_events = get_lst_events(narrative)
number, ordinal = sample_number(len(lst_events))
question = question.replace('<AL>', loudness) # insert loudness
question = question.replace('<RO>', preposition) # insert preposition
question = question.replace('<O>', ordinal) # insert ordinal
question = sanitize_question(question) # correct grammar
lst_loudness = get_lst_loudness(narrative)
event_idx = (number - 1)
answer = None
if 'before' in question:
if (event_idx - 1) < 0:
answer = 'nothing'
else:
lst_events_e = lst_events[:event_idx]
lst_events_l = lst_loudness[:event_idx]
elif 'after' in question:
if (event_idx + 1) >= len(lst_events):
answer = 'nothing'
else:
lst_events_e = lst_events[(event_idx + 1):]
lst_events_l = lst_loudness[(event_idx + 1):]
else:
assert False, \
'Preposition illdefined in Question (what_was_loudness_relative_ordinal).'
if answer is None:
assert len(lst_events_e) > 0, \
'Question (what_was_loudness_relative_ordinal) illposed.'
if 'loud' in question:
est = np.argmax(lst_events_l)
elif 'quiet' in question:
est = np.argmin(lst_events_l)
else:
assert False, \
'Loudness illdefined in Question (what_was_loudness_relative_ordinal).'
# Assert a good margin in relative loudness
evt_loudness = lst_events_l[est]
x_loudness = [j for i, j in enumerate(lst_events_l) if i != est]
rel_loudness_diff = compute_rel_diff(np.array(x_loudness),
np.array(evt_loudness))
assert np.sum(rel_loudness_diff < rel_diff) <= 0, \
'Question (what_was_loudness_relative_ordinal) illposed.'
e = lst_events_e[est]
answer = (str(np.random.choice(dataset['sources'][e])) + ' ' +
str(np.random.choice(dataset['actions'][e])))
return question, answer
def compare_loudness(dataset, narrative, rel_diff):
questions = [
'Was the <S1> <A1> <RL> than the <S2> <A2>?',
'Was the sound of the <S1> <A1> <RL> than the sound of the <S2> <A2>?', # noqa: E501
'[Comparing,Listening to,Hearing] the sound of the <S1> <A1> and the sound of the <S2> <A2>, was the former <RL>?', # noqa: E501
'[Comparing,Listening to,Hearing] the sounds of the <S1> <A1> and the <S2> <A2>, was the former <RL>?', # noqa: E501
'[Comparing,Listening to,Hearing] the sound of the <S2> <A2> and the sound of the <S1> <A1>, was the latter <RL>?', # noqa: E501
'[Comparing,Listening to,Hearing] the sounds of the <S2> <A2> and the <S1> <A1>, was the latter <RL>?', # noqa: E501
]
question = str(np.random.choice(questions)) # sample question
lst_events = get_lst_events(narrative)
unique_lst_events = [e for e in lst_events if lst_events.count(e) == 1]
assert len(unique_lst_events) > 0, 'Question (compare_loudness) illposed.'
event_1 = str(np.random.choice(unique_lst_events)) # sample event
source_1 = str(np.random.choice(dataset['sources'][event_1]))
action_1 = str(np.random.choice(dataset['actions'][event_1]))
rel_loudness = sample_rel_loudness()
x_unique_lst_events = [e for e in unique_lst_events if e != event_1]
assert len(x_unique_lst_events) > 0, \
'Question (compare_loudness) illposed.'
event_2 = str(np.random.choice(x_unique_lst_events)) # sample event
source_2 = str(np.random.choice(dataset['sources'][event_2]))
action_2 = str(np.random.choice(dataset['actions'][event_2]))
assert lst_events.count(event_1) == 1, \
'Question (compare_loudness) illposed.'
assert lst_events.count(event_2) == 1, \
'Question (compare_loudness) illposed.'
assert event_1 != event_2, 'Question (compare_loudness) illposed.'
question = question.replace('<S1>', source_1) # insert source
question = question.replace('<A1>', action_1) # insert action
question = question.replace('<RL>', rel_loudness) # insert loudness
question = question.replace('<S2>', source_2) # insert source
question = question.replace('<A2>', action_2) # insert action
question = sanitize_question(question)
lst_loudness = get_lst_loudness(narrative)
e_1_loudness = lst_loudness[lst_events.index(event_1)]
e_2_loudness = lst_loudness[lst_events.index(event_2)]
# Assert a good margin in relative loudness
rel_loudness_diff = compute_rel_diff(np.array(e_1_loudness),
np.array(e_2_loudness))
assert np.sum(rel_loudness_diff < rel_diff) <= 0, \
'Question (compare_loudness) illposed.'
if 'quiet' in question:
answer = 'yes' if e_1_loudness < e_2_loudness else 'no'
elif 'loud' in question:
answer = 'yes' if e_1_loudness > e_2_loudness else 'no'
else:
assert False, 'Loudness illdefined in Question (compare_loudness).'
return question, answer
def compare_same_loudness(dataset, narrative, rel_diff=0.1):
questions = [
'Was the <S1> <A1> [roughly,approximately] as <L> as the <S2> <A2>?', # noqa: E501
'Was the sound of the <S1> <A1> [roughly,approximately] as <L> as the sound of the <S2> <A2>?', # noqa: E501
'Was the sound of the <S1> <A1> [roughly,approximately] the same loudness as the sound of the <S2> <A2>?', # noqa: E501
'[Comparing,Listening to,Hearing] the sound of the <S1> <A1> and the sound of the <S2> <A2>, did they [roughly,approximately] have the same loudness?', # noqa: E501
'[Comparing,Listening to,Hearing] the sounds of the <S1> <A1> and the <S2> <A2>, did they [roughly,approximately] have the same loudness?', # noqa: E501
]
question = str(np.random.choice(questions)) # sample question
lst_events = get_lst_events(narrative)
unique_lst_events = [e for e in lst_events if lst_events.count(e) == 1]
assert len(unique_lst_events) > 0, \
'Question (compare_same_loudness) illposed.'
event_1 = str(np.random.choice(unique_lst_events)) # sample event
source_1 = str(np.random.choice(dataset['sources'][event_1]))
action_1 = str(np.random.choice(dataset['actions'][event_1]))
loudness = sample_loudness()
x_unique_lst_events = [e for e in unique_lst_events if e != event_1]
assert len(x_unique_lst_events) > 0, \
'Question (compare_same_loudness) illposed.'
event_2 = str(np.random.choice(x_unique_lst_events)) # sample event
source_2 = str(np.random.choice(dataset['sources'][event_2]))
action_2 = str(np.random.choice(dataset['actions'][event_2]))
assert lst_events.count(event_1) == 1, \
'Question (compare_same_loudness) illposed.'
assert lst_events.count(event_2) == 1, \
'Question (compare_same_loudness) illposed.'
assert event_1 != event_2, 'Question (compare_same_loudness) illposed.'
question = question.replace('<S1>', source_1) # insert source
question = question.replace('<A1>', action_1) # insert action
question = question.replace('<L>', loudness) # insert loudness
question = question.replace('<S2>', source_2) # insert source
question = question.replace('<A2>', action_2) # insert action
question = sanitize_question(question)
lst_loudness = get_lst_loudness(narrative)
e_1_loudness = lst_loudness[lst_events.index(event_1)]
e_2_loudness = lst_loudness[lst_events.index(event_2)]
rel_loudness_diff = compute_rel_diff(np.array(e_1_loudness),
np.array(e_2_loudness))
# Assert a good margin in relative loudness
assert np.sum(np.logical_and(rel_loudness_diff > rel_diff,
rel_loudness_diff < (2 * rel_diff))) <= 0, \
'Question (compare_same_loudness) illposed.'
answer = 'yes' if rel_loudness_diff <= rel_diff else 'no'
return question, answer
def compare_same_loudness_event_ordinal(dataset, narrative, rel_diff=0.1):
questions = [
'Was the <S> <A> [roughly,approximately] as <L> as the <O> [sound event,sound]?', # noqa: E501
'[Comparing,Listening to,Hearing] the <S> <A> and the <O> [sound event,sound], were they [roughly,approximately] as loud?', # noqa: E501
'[Comparing,Listening to,Hearing] the sound of the <S> <A> and the <O> [sound event,sound], were they [roughly,approximately] as loud?', # noqa: E501
'[Comparing,Listening to,Hearing] the <S> <A> and the <O> [sound event,sound], did they [roughly,approximately] have the same loudness?', # noqa: E501
'[Comparing,Listening to,Hearing] the sound of the <S> <A> and the <O> [sound event,sound], did they [roughly,approximately] have the same loudness?', # noqa: E501
'Was the <O> [sound event,sound] [roughly,approximately] as <L> as the <S> <A>?', # noqa: E501
'[Comparing,Listening to,Hearing] the <O> [sound event,sound] and the <S> <A>, were they [roughly,approximately] as loud?', # noqa: E501
'[Comparing,Listening to,Hearing] the <O> [sound event,sound] and the sound of the <S> <A>, were they [roughly,approximately] as loud?', # noqa: E501
'[Comparing,Listening to,Hearing] the <O> [sound event,sound] and the <S> <A>, did they [roughly,approximately] have the same loudness?', # noqa: E501
'[Comparing,Listening to,Hearing] the <O> [sound event,sound] and the sound of the <S> <A>, did they [roughly,approximately] have the same loudness?', # noqa: E501
]
question = str(np.random.choice(questions)) # sample question
lst_events = get_lst_events(narrative)
unique_lst_events = [e for e in lst_events if lst_events.count(e) == 1]
assert len(unique_lst_events) > 0, \
'Question (compare_same_loudness_event_ordinal) illposed.'
event = str(np.random.choice(unique_lst_events)) # sample event
source = str(np.random.choice(dataset['sources'][event]))
action = str(np.random.choice(dataset['actions'][event]))
loudness = sample_loudness()
number, ordinal = sample_second_number(len(lst_events),
lst_events.index(event) + 1)
assert lst_events.count(event) == 1, \
'Question (compare_same_loudness_event_ordinal) illposed.'
assert lst_events.index(event) != (number - 1), \
'Question (compare_same_loudness_event_ordinal) illposed.'
question = question.replace('<S>', source) # insert source
question = question.replace('<A>', action) # insert action
question = question.replace('<L>', loudness) # insert loudness
question = question.replace('<O>', ordinal) # insert ordinal
question = sanitize_question(question) # correct grammar
lst_loudness = get_lst_loudness(narrative)
e_1_loudness = lst_loudness[lst_events.index(event)]
e_2_loudness = lst_loudness[number - 1]
rel_loudness_diff = compute_rel_diff(np.array(e_1_loudness),
np.array(e_2_loudness))
# Assert a good margin in relative loudness
assert np.sum(np.logical_and(rel_loudness_diff > rel_diff,
rel_loudness_diff < (2 * rel_diff))) <= 0, \
'Question (compare_same_loudness_event_ordinal) illposed.'
answer = 'yes' if rel_loudness_diff <= rel_diff else 'no'
return question, answer
def compare_loudness_ordinal_event(dataset, narrative, rel_diff=0.1):
questions = [
'Was the <O> [sound event,sound] <RL> than the <S> <A>?',
'Was the <O> [sound event,sound] <RL> than the sound of the <S> <A>?', # noqa: E501
'[Comparing,Listening to,Hearing] the <O> [sound event,sound] and the <S> <A>, was the former <RL>?', # noqa: E501
'[Comparing,Listening to,Hearing] the <S> <A> and the <O> [sound event,sound], was the latter <RL>?', # noqa: E501
]
question = str(np.random.choice(questions)) # sample question
lst_events = get_lst_events(narrative)
unique_lst_events = [e for e in lst_events if lst_events.count(e) == 1]
assert len(unique_lst_events) > 0, \
'Question (compare_loudness_ordinal_event) illposed.'
event = str(np.random.choice(unique_lst_events)) # sample event
source = str(np.random.choice(dataset['sources'][event]))
action = str(np.random.choice(dataset['actions'][event]))
rel_loudness = sample_rel_loudness()
number, ordinal = sample_second_number(len(lst_events),
lst_events.index(event) + 1)
assert lst_events.count(event) == 1, \
'Question (compare_loudness_ordinal_event) illposed.'
assert lst_events.index(event) != (number - 1), \
'Question (compare_loudness_ordinal_event) illposed.'
question = question.replace('<S>', source) # insert source
question = question.replace('<A>', action) # insert action
question = question.replace('<RL>', rel_loudness) # insert loudness
question = question.replace('<O>', ordinal) # insert ordinal
question = sanitize_question(question) # correct grammar
lst_loudness = get_lst_loudness(narrative)
e_1_loudness = lst_loudness[number - 1]
e_2_loudness = lst_loudness[lst_events.index(event)]
# Assert a good margin in relative loudness
rel_loudness_diff = compute_rel_diff(np.array(e_1_loudness),
np.array(e_2_loudness))
assert np.sum(rel_loudness_diff < rel_diff) <= 0, \
'Question (compare_loudness_ordinal_event) illposed.'
if 'quiet' in question:
answer = 'yes' if e_1_loudness < e_2_loudness else 'no'
elif 'loud' in question:
answer = 'yes' if e_1_loudness > e_2_loudness else 'no'
else:
assert False, \
'Loudness illdefined in Question (compare_loudness_ordinal_event).'
return question, answer
def how_many_sounds_loudness_event(dataset, narrative, rel_diff=0.1):
questions = ['How many [sound events,sounds] [roughly,approximately] as <L> as the <S> <A>?', # noqa: E501
'How many [sound events,sounds] that are [roughly,approximately] as <L> as the <S> <A> [did,could] you [hear,listen to]?', # noqa: E501
'How many [sound events,sounds] that are [roughly,approximately] as <L> as the <S> <A> have you heard?', # noqa: E501
'How many [sound events,sounds] that have [roughly,approximately] the same loudness as the <S> <A>?', # noqa: E501
'How many [sound events,sounds] that have [roughly,approximately] the same loudness as the <S> <A> [did,could] you [hear,listen to]?', # noqa: E501
'How many [sound events,sounds] that have [roughly,approximately] the same loudness as the <S> <A> have you heard?', # noqa: E501
'What is the number of [sound events,sounds] [roughly,approximately] as <L> as the <S> <A>?', # noqa: E501
'What is the number of [sound events,sounds] that are [roughly,approximately] as <L> as the <S> <A> [did,could] you [hear,listen to]?', # noqa: E501
'What is the number of [sound events,sounds] that are [roughly,approximately] as <L> as the <S> <A> have you heard?', # noqa: E501
'What is the number of [sound events,sounds] that have [roughly,approximately] the same loudness as the <S> <A>?', # noqa: E501
'What is the number of [sound events,sounds] that have [roughly,approximately] the same loudness as the <S> <A> [did,could] you [hear,listen to]?', # noqa: E501
'What is the number of [sound events,sounds] that have [roughly,approximately] the same loudness as the <S> <A> have you heard?', # noqa: E501
'There is [a,an] <S> <A>; how many [sound events,sounds] that are [roughly,approximately] as <L>?', # noqa: E501
'There is [a,an] <S> <A>; what is the number of [sound events,sounds] that are [roughly,approximately] as <L>?', # noqa: E501
]
question = str(np.random.choice(questions)) # sample question
loudness = sample_loudness() # sample loudness
lst_events = get_lst_events(narrative)
unique_lst_events = [e for e in lst_events if lst_events.count(e) == 1]
assert len(unique_lst_events) > 0, \
'Question (how_many_sounds_loudness_event) illposed.'
event = str(np.random.choice(unique_lst_events))
source = str(np.random.choice(dataset['sources'][event])) # sample source
action = str(np.random.choice(dataset['actions'][event])) # sample action
question = question.replace('<L>', loudness) # insert loudness
question = question.replace('<S>', source) # insert source
question = question.replace('<A>', action) # insert action
question = sanitize_question(question) # correct grammar
assert lst_events.count(event) == 1, \
'Question (how_many_sounds_loudness_event) illposed.'
lst_loudness = get_lst_loudness(narrative)
event_idx = lst_events.index(event)
evt_loudness = lst_loudness[event_idx]
x_loudness = [j for i, j in enumerate(lst_loudness) if i != event_idx]
rel_loudness_diff = compute_rel_diff(np.array(x_loudness),
np.array(evt_loudness))
# Assert a good margin in relative loudness
assert np.sum(np.logical_and(rel_loudness_diff > rel_diff,
rel_loudness_diff < (2 * rel_diff))) <= 0, \
'Question (how_many_sounds_loudness_event) illposed.'
answer = numbers_to_words(np.sum(rel_loudness_diff <= rel_diff))
return question, answer
def was_there_similar_loudness(dataset, narrative, rel_diff=0.1):
questions = [
'Were there any sounds [roughly,approximately] as <L> as the <S> <A>?', # noqa: E501
'Were there any sounds that were [roughly,approximately] as <L> as the <S> <A>?', # noqa: E501
'Were there any sounds that were [roughly,approximately] the same loudness as the <S> <A>?', # noqa: E501
'Was there any sound [roughly,approximately] as <L> as the <S> <A>?', # noqa: E501
'Was there any sound that was [roughly,approximately] as <L> as the <S> <A>?', # noqa: E501
'Was there any sound that was [roughly,approximately] the same loudness as the <S> <A>?', # noqa: E501
'Was there at least a sound [roughly,approximately] as <L> as the <S> <A>?', # noqa: E501
'Was there at least a sound that was [roughly,approximately] as <L> as the <S> <A>?', # noqa: E501
'Was there at least a sound that was [roughly,approximately] the same loudness as <S> <A>?', # noqa: E501
'Was there at least [one,a single] sound [roughly,approximately] as <L> as the <S> <A>?', # noqa: E501
'Was there at least [one,a single] sound that was [roughly,approximately] as <L> as the <S> <A>?', # noqa: E501
'Was there at least [one,a single] sound that was [roughly,approximately] the same loudness as <S> <A>?', # noqa: E501
]
question = str(np.random.choice(questions)) # sample question
loudness = sample_loudness() # sample loudness
lst_events = get_lst_events(narrative)
unique_lst_events = [e for e in lst_events if lst_events.count(e) == 1]
assert len(unique_lst_events) > 0, \
'Question (was_there_similar_loudness) illposed.'
event = str(np.random.choice(unique_lst_events))
source = str(np.random.choice(dataset['sources'][event])) # sample source
action = str(np.random.choice(dataset['actions'][event])) # sample action
question = question.replace('<L>', loudness) # insert loudness
question = question.replace('<S>', source) # insert source
question = question.replace('<A>', action) # insert action
question = sanitize_question(question) # correct grammar
assert lst_events.count(event) == 1, \
'Question (was_there_similar_loudness) illposed.'
lst_loudness = get_lst_loudness(narrative)
event_idx = lst_events.index(event)
evt_loudness = lst_loudness[event_idx]
x_loudness = [j for i, j in enumerate(lst_loudness) if i != event_idx]
rel_loudness_diff = compute_rel_diff(np.array(x_loudness),
np.array(evt_loudness))
# Assert a good margin in relative loudness
assert np.sum(np.logical_and(rel_loudness_diff > rel_diff,
rel_loudness_diff < (2 * rel_diff))) <= 0, \
'Question (was_there_similar_loudness) illposed.'
answer = 'yes' if np.sum(rel_loudness_diff <= rel_diff) >= 1 else 'no'
return question, answer
def compare_loudness_ordinal(dataset, narrative, rel_diff=0.1):
questions = [
'Was the <O1> [sound event,sound] <RL> than the <O2> [sound event,sound]?', # noqa: E501
'[Comparing,Listening to,Hearing] the <O1> [sound event,sound] and the <O2> [sound event,sound], was the former <RL>?', # noqa: E501
'[Comparing,Listening to,Hearing] the <O1> and <O2> [sound events,sounds], was the former <RL>?', # noqa: E501
'[Comparing,Listening to,Hearing] the <O2> [sound event,sound] and the <O1> [sound event,sound], was the latter <RL>?', # noqa: E501
'[Comparing,Listening to,Hearing] the <O2> and <O1> [sound events,sounds], was the latter <RL>?', # noqa: E501
]
question = str(np.random.choice(questions)) # sample question
lst_events = get_lst_events(narrative)
number_1, ordinal_1 = sample_number(len(lst_events))
rel_loudness = sample_rel_loudness()
number_2, ordinal_2 = sample_second_number(len(lst_events), number_1)
assert number_1 != number_2, 'Question (compare_loudness_ordinal) illposed.'
question = question.replace('<O1>', ordinal_1) # insert ordinal
question = question.replace('<RL>', rel_loudness) # insert loudness
question = question.replace('<O2>', ordinal_2) # insert ordinal
question = sanitize_question(question) # correct grammar
lst_loudness = get_lst_loudness(narrative)
e_1_loudness = lst_loudness[number_1 - 1]
e_2_loudness = lst_loudness[number_2 - 1]
# Assert a good margin in relative loudness
rel_loudness_diff = compute_rel_diff(np.array(e_1_loudness),
np.array(e_2_loudness))
assert np.sum(rel_loudness_diff < rel_diff) <= 0, \
'Question (compare_loudness_ordinal) illposed.'
if 'quiet' in question:
answer = 'yes' if e_1_loudness < e_2_loudness else 'no'
elif 'loud' in question:
answer = 'yes' if e_1_loudness > e_2_loudness else 'no'
else:
assert False, 'Loudness illdefined in Question (compare_loudness_ordinal).'
return question, answer
def how_many_sounds_loudness_ordinal(dataset, narrative, rel_diff=0.1):
questions = ['How many [sound events,sounds] [roughly,approximately] as <L> as the <O> sound?', # noqa: E501
'How many [sound events,sounds] that are [roughly,approximately] as <L> as the <O> sound?', # noqa: E501
'How many [sound events,sounds] that are [roughly,approximately] as <L> as the <O> sound [did,could] you [hear,listen to]?', # noqa: E501
'How many [sound events,sounds] that are [roughly,approximately] as <L> as the <O> sound have you heard?', # noqa: E501
'How many [sound events,sounds] that have [roughly,approximately] the same loudness as the <O> sound?', # noqa: E501
'How many [sound events,sounds] that have [roughly,approximately] the same loudness as the <O> sound [did,could] you [hear,listen to]?', # noqa: E501
'How many [sound events,sounds] that have [roughly,approximately] the same loudness as the <O> sound have you heard?', # noqa: E501
'What is the number of [sound events,sounds] [roughly,approximately] as <L> as the <O> sound?', # noqa: E501
'What is the number of [sound events,sounds] that are [roughly,approximately] as <L> as the <O> sound?', # noqa: E501
'What is the number of [sound events,sounds] that are [roughly,approximately] as <L> as the <O> sound [did,could] you [hear,listen to]?', # noqa: E501
'What is the number of [sound events,sounds] that are [roughly,approximately] as <L> as the <O> sound have you heard?', # noqa: E501
'What is the number of [sound events,sounds] that have [roughly,approximately] the same loudness as the <O> sound?', # noqa: E501
'What is the number of [sound events,sounds] that have [roughly,approximately] the same loudness as the <O> sound [did,could] you [hear,listen to]?', # noqa: E501
'What is the number of [sound events,sounds] that have [roughly,approximately] the same loudness as the <O> sound have you heard?', # noqa: E501
]
question = str(np.random.choice(questions)) # sample question
loudness = sample_loudness() # sample loudness
lst_events = get_lst_events(narrative)
number, ordinal = sample_number(len(lst_events))
question = question.replace('<L>', loudness) # insert loudness
question = question.replace('<O>', ordinal) # insert ordinal
question = sanitize_question(question) # correct grammar
lst_loudness = get_lst_loudness(narrative)
evt_loudness = lst_loudness[number - 1]
x_loudness = [j for i, j in enumerate(lst_loudness) if i != (number - 1)]
rel_loudness_diff = compute_rel_diff(np.array(x_loudness),
np.array(evt_loudness))
# Assert a good margin in relative loudness
assert np.sum(np.logical_and(rel_loudness_diff > rel_diff,
rel_loudness_diff < (2 * rel_diff))) <= 0, \
'Question (how_many_sounds_loudness_ordinal) illposed.'
answer = numbers_to_words(np.sum(rel_loudness_diff <= rel_diff))
return question, answer
def was_there_similar_loudness_ordinal(dataset, narrative, rel_diff=0.1):
questions = [
'Were there any sounds [roughly,approximately] as <L> as the <O> sound?', # noqa: E501
'Were there any sounds that were [roughly,approximately] as <L> as the <O> sound?', # noqa: E501
'Were there any sounds that were [roughly,approximately] the same loudness as the <O> sound?', # noqa: E501
'Was there any sound [roughly,approximately] as <L> as the <O> sound?', # noqa: E501
'Was there any sound that was [roughly,approximately] as <L> as the <O> sound?', # noqa: E501
'Was there any sound that was [roughly,approximately] the same loudness as the <O> sound?', # noqa: E501
'Was there at least a sound [roughly,approximately] as <L> as the <O> sound?', # noqa: E501
'Was there at least a sound that was [roughly,approximately] as <L> as the <O> sound?', # noqa: E501
'Was there at least a sound that was [roughly,approximately] the same loudness as the <O> sound?', # noqa: E501
'Was there at least [one,a single] sound that was [roughly,approximately] as <L> as the <O> sound?', # noqa: E501
'Was there at least [one,a single] sound [roughly,approximately] as <L> as the <O> sound?', # noqa: E501
'Was there at least [one,a single] sound that was [roughly,approximately] the same loudness as the <O> sound?', # noqa: E501
]
question = str(np.random.choice(questions)) # sample question
loudness = sample_loudness() # sample loudness
lst_events = get_lst_events(narrative)
number, ordinal = sample_number(len(lst_events))
question = question.replace('<L>', loudness) # insert loudness
question = question.replace('<O>', ordinal) # insert ordinal
question = sanitize_question(question) # correct grammar
lst_loudness = get_lst_loudness(narrative)
evt_loudness = lst_loudness[number - 1]
x_loudness = [j for i, j in enumerate(lst_loudness) if i != (number - 1)]
rel_loudness_diff = compute_rel_diff(np.array(x_loudness),
np.array(evt_loudness))
# Assert a good margin in relative loudness
assert np.sum(np.logical_and(rel_loudness_diff > rel_diff,
rel_loudness_diff < (2 * rel_diff))) <= 0, \
'Question (was_there_similar_loudness_ordinal) illposed.'
answer = 'yes' if np.sum(rel_loudness_diff <= rel_diff) >= 1 else 'no'
return question, answer