# an object to extract spectral peaks from STFT based spectrogram
peak_extractor = PeakExtractor(maximum_filter_width=150, maximum_filter_height=75)
# an object to generate fingerprints using the association of four spectral peaks
fingerprint_generator = FingerprintGenerator(
frames_per_second=219,
target_zone_width=2,
target_zone_center=4,
number_of_quads_per_second=9,
tolerance=0.0
)
# Data manager object
fingerprint_manager = FingerprintManager(db_path="../../../Databases/Quads_Test_1.db")
count = 1
for i in reference_audios:
audio_title = i.split("/")[5].split(".")[0]
# loading time series audio data of one of reference audio
audio_data = audio_manager.load_audio(audio_path=i, sr=7000)
# computing the spectrogram of time series audio data
spectrogram = stft.compute_spectrogram_magnitude_in_db(audio_data=audio_data)
# extracting spectral peaks from STFT based spectrogram
spectral_peaks = peak_extractor.extract_spectral_peaks(spectrogram=spectrogram)
# generate fingerprints using the association of four spectral peaks
audio_fingerprints = fingerprint_generator.generate_fingerprints(spectral_peaks=spectral_peaks[0],
spectrogram=spectrogram)
# storing fingerprints
fingerprint_manager.store_fingerprints(audio_fingerprints=audio_fingerprints,
spectral_peaks=spectral_peaks[0],
audio_title=audio_title)
print("Done Fingerprinting ", count, audio_title)
count += 1
for i in query_audios:
audio_id = i.split("/")[7].split(".")[0]
# loading a time series audio data from one of the query audio
start = time.time()
audio_data = audio_manager.load_audio(audio_path=i,
sr=7000,
offset=10.0,
duration=t)
# computing stft based spectrogram of time series audio data
spectrogram = stft.compute_spectrogram_magnitude_in_db(
audio_data=audio_data)
# extracting spectral peaks from STFT based spectrogram
spectral_peaks = peak_extractor.spectral_peaks(spectrogram=spectrogram)
# generate quad based fingerprints
audio_fingerprints = fingerprint_generator.generate_fingerprints(
spectral_peaks=spectral_peaks[0],
spectrogram=spectrogram,
number_of_quads_per_second=500)
# query matches
match = fingerprint_manager.query_audio(
audio_fingerprints=audio_fingerprints,
spectral_peaks=spectral_peaks[0],
vThreshold=0.3)
end = time.time()
result = None
if match[0] == "No Match":
result = "False Negative"
else:
if audio_id == match[0]:
result = "True Positive"
else:
from Core import PeakExtractor
from Core import FingerprintGenerator
# source directory
src_dir = "../../../Test_Data/Reference_Audios"
# spectrogram and peak extractor objects
stft = Spectrogram(hop_length=32)
peak_extractor = PeakExtractor()
fingerprint_generator = FingerprintGenerator()
# searching for all .mp3 file under specified source dir
mp3_files = DirManager.find_mp3_files(src_dir=src_dir)
# reading audio data re-sampled at 7KHz for
# audio portion defined by offset and duration parameters
audio_data = AudioManager.load_audio(audio_path=mp3_files[0], offset=10.0, duration=3.0)
# computing spectrogram
spectrogram = stft.compute_stft_magnitude_in_db(audio_data=audio_data)
# extract spectral peaks
spectral_peaks = peak_extractor.extract_spectral_peaks_1(spectrogram=spectrogram)
# generate fingerprints
audio_fingerprints = list()
audio_fingerprints_info = list()
fingerprint_generator.generate_fingerprints(spectral_peaks=spectral_peaks[0],
audio_fingerprints=audio_fingerprints,
audio_fingerprints_info=audio_fingerprints_info)
# audio fingerprints
print("Audio Fingerprints", len(audio_fingerprints))
print(audio_fingerprints)
# audio fingerprints info
print("Audio Fingerprints Raw Data", len(audio_fingerprints))
print(audio_fingerprints_info)