def main():
frequency = 1000
sampling_rate = 48000
amplitude = 16000
time_ = 2
# Normal wave
data = signalwave.load_wave('wave.wav', time=2)
# Abs wave
data_abs = signalwave.load_wave('abs_wave.wav', time=2)
# signalwave.plot(data, data_abs)
freqs = signalwave.fft(data)
freqs_abs = signalwave.fft(data_abs)
plt.subplot(2,1,1)
plt.plot(data_abs[:300])
plt.title('Original audio wave')
plt.subplot(2, 1, 2)
plt.plot(freqs_abs)
plt.title('Frequencies found')
plt.xlim(0, 1200)
plt.show()
def main():
time_ = 2
# Normal wave
data = signalwave.load_wave('noisy_audio.wav', time=2)
freqs = signalwave.fft(data)
# *2 devido ao "bug" da frequencia dobrada
filtered_freqs = filter_freqs(freqs)
plt.subplot(2, 1, 1)
plt.plot(freqs)
plt.title('Original freqs')
plt.subplot(2, 1, 2)
plt.plot(filtered_freqs)
plt.title('Filtered freqs')
plt.xlim(0, 1200)
plt.show()
recovered_signal = np.fft.ifft(filtered_freqs)
plt.plot(recovered_signal)
plt.show()
# print(recovered_signal)
signalwave.save_wave(np.abs(recovered_signal),
file_path='noisy_audio_fitered.wav')
def main():
time_ = 2
# Normal wave
data = signalwave.load_wave('noisy_wave.wav', time=2)
signalwave.plot(data)
freqs = signalwave.fft(data)
plt.subplot(2, 1, 1)
plt.plot(data[:3000])
plt.title('Original audio wave')
plt.subplot(2, 1, 2)
plt.plot(freqs)
plt.title('Frequencies found')
plt.xlim(0, 3000)
plt.show()
def main():
# Normal wave
data = signalwave.load_wave('mic.wav', time=2)
freqs = signalwave.fft(data)
filtered_freqs = filter_freqs(freqs)
signalwave.plots([freqs, filtered_freqs], limit=5000)
recovered_signal = np.fft.ifft(filtered_freqs)
signalwave.plot(recovered_signal, limit=3000)
# print(recovered_signal)
signalwave.save_wave(
recovered_signal,
amplitude=0, file_path='mic_filtered.wav')
def main():
data = signalwave.load_wave('noisy_wave.wav', time=3)
freqs = signalwave.fft(data)
# As vezes transofmra *2, as vezes *3.
filtered_freqs = filter_freqs(freqs, 300 * 3, 800 * 3)
signalwave.plots([freqs, filtered_freqs], limit=10000)
recovered_signal = np.fft.ifft(filtered_freqs)
signalwave.plot(recovered_signal, limit=5000)
signalwave.save_wave(recovered_signal,
amplitude=0,
file_path='filtered_noisy_wave.wav')
def main():
# data = signalwave.load_wave('noisy_audio.wav', time=3, stereo=False)
data = signalwave.load_wave('aud.wav', time=3, stereo=False)
signalwave.plot(data, limit=10000)
freqs = signalwave.fft(data)
filtered_freqs = filter_freqs(freqs)
signalwave.plots([freqs, filtered_freqs], limit=15000)
recovered_signal = np.fft.ifft(filtered_freqs)
signalwave.plot(recovered_signal, limit=5000)
print(recovered_signal)
signalwave.save_wave(
recovered_signal,
amplitude=0, file_path='noisy_audio_fitered.wav')