ms762wu/EMG_Biometrics_2021
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feat: add mfcc save func to crate NN data in json

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Skudalen 2021-07-02 11:15:12 +02:00
parent f9434a3b66
commit 34d79910cb
4 changed files with 37553 additions and 12 deletions
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16
Handle_emg_data.py
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@ -9,7 +9,7 @@ import sys
sys.path.insert(0, '/Users/Markus/Prosjekter git/Slovakia 2021/python_speech_features/python_speech_features') sys.path.insert(0, '/Users/Markus/Prosjekter git/Slovakia 2021/python_speech_features/python_speech_features')
from python_speech_features.python_speech_features import mfcc from python_speech_features.python_speech_features import mfcc
import json import json
import librosa #import librosa
#from Present_data import get_data #from Present_data import get_data
# Global variables for MFCC # Global variables for MFCC
@ -500,10 +500,6 @@ class CSV_handler:
class DL_data_handler: class DL_data_handler:
JSON_PATH = "mfcc_data.json" JSON_PATH = "mfcc_data.json"
SAMPLE_RATE = None
TRACK_DURATION = None # measured in seconds
#SAMPLES_PER_TRACK = SAMPLE_RATE * TRACK_DURATION
def __init__(self, csv_handler:CSV_handler) -> None: def __init__(self, csv_handler:CSV_handler) -> None:
self.csv_handler = csv_handler self.csv_handler = csv_handler
@ -596,8 +592,6 @@ class DL_data_handler:
"mfcc": [] "mfcc": []
} }
#hop_length = MFCC_STEPSIZE * sample_rate
#num_mfcc_vectors_per_segment = math.ceil(samples_per_subject / hop_length)
raw_data_dict = self.get_samples_dict() raw_data_dict = self.get_samples_dict()
# loop through all subjects to get samples # loop through all subjects to get samples
@ -610,7 +604,7 @@ class DL_data_handler:
print("\nProcessing: {}".format(subject_label)) print("\nProcessing: {}".format(subject_label))
# process all audio files in genre sub-dir # process all audio files in genre sub-dir
for sample in value: for i, (sample) in enumerate(value):
# load audio file # load audio file
signal, sample_rate = sample[0], sample[1] signal, sample_rate = sample[0], sample[1]
@ -618,8 +612,8 @@ class DL_data_handler:
hop_length = MFCC_STEPSIZE * sample_rate hop_length = MFCC_STEPSIZE * sample_rate
# extract mfcc # extract mfcc
mfcc = librosa.feature.mfcc(signal, sample_rate, n_mfcc=NR_COEFFICIENTS, n_fft=n_fft, hop_length=hop_length) #mfcc = librosa.feature.mfcc(signal, sample_rate, n_mfcc=NR_COEFFICIENTS, n_fft=n_fft, hop_length=hop_length)
#mfcc = mfcc_custom(signal, sample_rate, MFCC_WINDOWSIZE, MFCC_STEPSIZE, NR_COEFFICIENTS, NR_MEL_BINS) _, mfcc = mfcc_custom(signal, sample_rate, MFCC_WINDOWSIZE, MFCC_STEPSIZE, NR_COEFFICIENTS, NR_MEL_BINS)
mfcc = mfcc.T mfcc = mfcc.T
print(len(mfcc)) print(len(mfcc))
@ -627,7 +621,7 @@ class DL_data_handler:
#if len(mfcc) == num_mfcc_vectors_per_segment: #if len(mfcc) == num_mfcc_vectors_per_segment:
data["mfcc"].append(mfcc.tolist()) data["mfcc"].append(mfcc.tolist())
data["labels"].append(key) data["labels"].append(key)
print("sample:{}".format(value.index(sample))) print("sample:{}".format(i))
# save MFCCs to json file # save MFCCs to json file
with open(json_path, "w") as fp: with open(json_path, "w") as fp:

2
Present_data.py
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@ -222,7 +222,7 @@ def main():
csv_handler = CSV_handler() csv_handler = CSV_handler()
csv_handler.load_data('soft') csv_handler.load_data('soft')
dl_data_handler = DL_data_handler(csv_handler) dl_data_handler = DL_data_handler(csv_handler)
dl_data_handler.store_samples(10) dl_data_handler.store_samples(2)
dict = dl_data_handler.samples_per_subject dict = dl_data_handler.samples_per_subject
print(len(dict.get(2))) print(len(dict.get(2)))
dl_data_handler.save_mfcc() dl_data_handler.save_mfcc()

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__pycache__/Handle_emg_data.cpython-38.pyc
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37547
mfcc_data.json Normal file
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