def algorithm_rhythm_percival14_mod(sound):
"""
Percival, G., & Tzanetakis, G. (2014). Streamlined tempo estimation based on autocorrelation and cross-correlation
with pulses. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(12), 1765-1776.
:param sound: sound dictionary from dataset
:return: dictionary with results
"""
results = dict()
sample_rate = 44100
audio = load_audio_file(file_path=sound[SOUND_FILE_KEY], sample_rate=sample_rate)
# Convert to mono and add silence at the beginning if sound is shorter than 6s (otherwise algorithm fails)
if len(audio.shape) > 1:
audio = audio[:, 0]
try:
from algorithms.Percival14Mod.defs_class import Defs
from algorithms.Percival14Mod.onset_strength import onset_strength_signal
from algorithms.Percival14Mod.beat_period_detection import beat_period_detection
from algorithms.Percival14Mod.accumulator_overall import accumulator_overall
defs = Defs()
oss_sr, oss_data = onset_strength_signal(defs, sample_rate, audio, plot=False)
tempo_lags = beat_period_detection(defs, oss_sr, oss_data, plot=False)
bpm = accumulator_overall(defs, tempo_lags, oss_sr)
results['Percival14Mod'] = {'bpm': bpm}
#tempo_lags = beat_period_detection(defs, oss_sr, oss_data, plot=False, limit_pulse_trains=True)
#bpm = accumulator_overall(defs, tempo_lags, oss_sr)
#results['Percival14ModLimitPulseTrains'] = {'bpm': bpm}
#tempo_lags = beat_period_detection(defs, oss_sr, oss_data, plot=False, skip_calc_pulse_trains=True)
#bpm = accumulator_overall(defs, tempo_lags, oss_sr)
#results['Percival14ModSkipPulseTrains'] = {'bpm': bpm}
except ValueError:
pass
return results
def algorithm_rhythm_percival14_mod(sound):
"""
Percival, G., & Tzanetakis, G. (2014). Streamlined tempo estimation based on autocorrelation and cross-correlation
with pulses. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(12), 1765-1776.
:param sound: sound dictionary from dataset
:return: dictionary with results
"""
results = dict()
sample_rate = 44100
audio = load_audio_file(file_path=sound[SOUND_FILE_KEY], sample_rate=sample_rate)
# Convert to mono and add silence at the beginning if sound is shorter than 6s (otherwise algorithm fails)
if len(audio.shape) > 1:
audio = audio[:, 0]
try:
from algorithms.Percival14Mod.defs_class import Defs
from algorithms.Percival14Mod.onset_strength import onset_strength_signal
from algorithms.Percival14Mod.beat_period_detection import beat_period_detection
from algorithms.Percival14Mod.accumulator_overall import accumulator_overall
defs = Defs()
oss_sr, oss_data = onset_strength_signal(defs, sample_rate, audio, plot=False)
tempo_lags = beat_period_detection(defs, oss_sr, oss_data, plot=False)
bpm = accumulator_overall(defs, tempo_lags, oss_sr)
results['Percival14Mod'] = {'bpm': bpm}
#tempo_lags = beat_period_detection(defs, oss_sr, oss_data, plot=False, limit_pulse_trains=True)
#bpm = accumulator_overall(defs, tempo_lags, oss_sr)
#results['Percival14ModLimitPulseTrains'] = {'bpm': bpm}
#tempo_lags = beat_period_detection(defs, oss_sr, oss_data, plot=False, skip_calc_pulse_trains=True)
#bpm = accumulator_overall(defs, tempo_lags, oss_sr)
#results['Percival14ModSkipPulseTrains'] = {'bpm': bpm}
except ValueError:
pass
return results
def algorithm_rhythm_percival14(sound):
"""
Percival, G., & Tzanetakis, G. (2014). Streamlined tempo estimation based on autocorrelation and cross-correlation
with pulses. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(12), 1765-1776.
:param sound: sound dictionary from dataset
:return: dictionary with results
"""
results = dict()
sample_rate = 44100
audio = load_audio_file(file_path=sound[SOUND_FILE_KEY], sample_rate=sample_rate)
# Convert to mono and add silence at the beginning if sound is shorter than 6s (otherwise algorithm fails)
if len(audio.shape) > 1:
audio = audio[:, 0]
min_duration = 6 # In seconds
minimum_size = min_duration * sample_rate
if audio.shape[0] < minimum_size:
audio = np.append(np.zeros(minimum_size - audio.shape[0]), audio)
try:
from algorithms.Percival14.defs_class import Defs
from algorithms.Percival14.onset_strength import onset_strength_signal
from algorithms.Percival14.beat_period_detection import beat_period_detection
from algorithms.Percival14.accumulator_overall import accumulator_overall
defs = Defs()
oss_sr, oss_data = onset_strength_signal(defs, sample_rate, audio, plot=False)
tempo_lags = beat_period_detection(defs, oss_sr, oss_data, plot=False)
bpm = accumulator_overall(defs, tempo_lags, oss_sr)
results['Percival14'] = {'bpm': bpm}
except ValueError:
pass
return results
def algorithm_rhythm_percival14(sound):
"""
Percival, G., & Tzanetakis, G. (2014). Streamlined tempo estimation based on autocorrelation and cross-correlation
with pulses. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 22(12), 1765-1776.
:param sound: sound dictionary from dataset
:return: dictionary with results
"""
results = dict()
sample_rate = 44100
audio = load_audio_file(file_path=sound[SOUND_FILE_KEY], sample_rate=sample_rate)
# Convert to mono and add silence at the beginning if sound is shorter than 6s (otherwise algorithm fails)
if len(audio.shape) > 1:
audio = audio[:, 0]
min_duration = 6 # In seconds
minimum_size = min_duration * sample_rate
if audio.shape[0] < minimum_size:
audio = np.append(np.zeros(minimum_size - audio.shape[0]), audio)
try:
from algorithms.Percival14.defs_class import Defs
from algorithms.Percival14.onset_strength import onset_strength_signal
from algorithms.Percival14.beat_period_detection import beat_period_detection
from algorithms.Percival14.accumulator_overall import accumulator_overall
defs = Defs()
oss_sr, oss_data = onset_strength_signal(defs, sample_rate, audio, plot=False)
tempo_lags = beat_period_detection(defs, oss_sr, oss_data, plot=False)
bpm = accumulator_overall(defs, tempo_lags, oss_sr)
results['Percival14'] = {'bpm': bpm}
except ValueError:
pass
return results