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')