%对语音信号进行截短的代码 %加矩形窗时的倒谱的代码 %加矩形窗时的复倒谱的代码

对语音信号进行截短的代码： ```python import librosa # load audio signal signal, sr = librosa.load('audio.wav') # set start and end time in seconds start_time = 1.0 end_time = 3.0 # convert to sample indices start_index = int(start_time * sr) end_index = int(end_time * sr) # extract the truncated signal truncated_signal = signal[start_index:end_index] ``` 加矩形窗时的倒谱的代码： ```python import numpy as np import librosa # load audio signal signal, sr = librosa.load('audio.wav') # set window size and hop length window_size = 1024 hop_length = 512 # apply rectangular window signal_windowed = signal * np.hamming(window_size) # calculate magnitude spectrum magnitude_spectrum = np.abs(librosa.stft(signal_windowed, n_fft=window_size, hop_length=hop_length)) # calculate power spectrum power_spectrum = magnitude_spectrum ** 2 # apply mel filterbank mel_basis = librosa.filters.mel(sr=sr, n_fft=window_size, n_mels=40) mel_spectrum = np.dot(mel_basis, power_spectrum) # calculate log mel spectrum log_mel_spectrum = librosa.power_to_db(mel_spectrum) # calculate cepstral coefficients cepstral_coefficients = librosa.feature.mfcc(S=log_mel_spectrum, n_mfcc=13) # apply lifter to cepstral coefficients lifter = 22 lifter_vector = 1 + (lifter / 2) * np.sin(np.pi * np.arange(cepstral_coefficients.shape[0]) / lifter) lifter_matrix = np.tile(lifter_vector[:, np.newaxis], (1, cepstral_coefficients.shape[1])) cepstral_coefficients_lifted = cepstral_coefficients * lifter_matrix ``` 加矩形窗时的复倒谱的代码： ```python import numpy as np import librosa # load audio signal signal, sr = librosa.load('audio.wav') # set window size and hop length window_size = 1024 hop_length = 512 # apply rectangular window signal_windowed = signal * np.hamming(window_size) # calculate magnitude spectrum magnitude_spectrum = np.abs(librosa.stft(signal_windowed, n_fft=window_size, hop_length=hop_length)) # calculate phase spectrum phase_spectrum = np.angle(librosa.stft(signal_windowed, n_fft=window_size, hop_length=hop_length)) # calculate power spectrum power_spectrum = magnitude_spectrum ** 2 # apply mel filterbank mel_basis = librosa.filters.mel(sr=sr, n_fft=window_size, n_mels=40) mel_spectrum = np.dot(mel_basis, power_spectrum) # calculate log mel spectrum log_mel_spectrum = librosa.power_to_db(mel_spectrum) # calculate cepstral coefficients cepstral_coefficients = librosa.feature.mfcc(S=log_mel_spectrum, n_mfcc=13) # calculate lifter lifter = 22 lifter_vector = 1 + (lifter / 2) * np.sin(np.pi * np.arange(cepstral_coefficients.shape[0]) / lifter) # apply lifter to cepstral coefficients lifter_matrix = np.tile(lifter_vector[:, np.newaxis], (1, cepstral_coefficients.shape[1])) cepstral_coefficients_lifted = cepstral_coefficients * lifter_matrix # apply inverse DCT to liftered cepstral coefficients cepstral_coefficients_lifted_idct = np.real(np.fft.ifft(cepstral_coefficients_lifted, axis=0)) # apply exponential to liftered cepstral coefficients cepstral_coefficients_lifted_exponential = np.exp(cepstral_coefficients_lifted_idct) # apply inverse Fourier transform to exponential of liftered cepstral coefficients cepstral_coefficients_lifted_exponential_ifft = np.fft.ifft(cepstral_coefficients_lifted_exponential, axis=0) # calculate real part of complex cepstral coefficients real_part = np.real(cepstral_coefficients_lifted_exponential_ifft) # calculate imaginary part of complex cepstral coefficients imaginary_part = np.imag(cepstral_coefficients_lifted_exponential_ifft) # combine real and imaginary parts to form complex cepstral coefficients complex_cepstral_coefficients = real_part + 1j * imaginary_part # apply Fourier transform to complex cepstral coefficients cepstral_coefficients_complex_fft = np.fft.fft(complex_cepstral_coefficients, axis=0) # calculate phase spectrum of complex cepstral coefficients phase_spectrum_complex_cepstral = np.angle(cepstral_coefficients_complex_fft) # calculate magnitude spectrum of phase spectrum of complex cepstral coefficients magnitude_spectrum_phase_spectrum_complex_cepstral = np.abs(librosa.stft(phase_spectrum_complex_cepstral, n_fft=window_size, hop_length=hop_length)) # calculate complex cepstral coefficients of phase spectrum of complex cepstral coefficients cepstral_coefficients_phase_spectrum_complex_cepstral = np.fft.ifft(np.log(magnitude_spectrum_phase_spectrum_complex_cepstral), axis=0) # apply exponential to complex cepstral coefficients of phase spectrum of complex cepstral coefficients cepstral_coefficients_phase_spectrum_complex_cepstral_exponential = np.exp(cepstral_coefficients_phase_spectrum_complex_cepstral) # apply Fourier transform to exponential of complex cepstral coefficients of phase spectrum of complex cepstral coefficients cepstral_coefficients_phase_spectrum_complex_cepstral_exponential_fft = np.fft.fft(cepstral_coefficients_phase_spectrum_complex_cepstral_exponential, axis=0) # calculate complex cepstral coefficients of phase spectrum of complex cepstral coefficients complex_cepstral_coefficients_phase_spectrum_complex_cepstral = np.real(cepstral_coefficients_phase_spectrum_complex_cepstral_exponential_fft) # calculate real cepstral coefficients of phase spectrum of complex cepstral coefficients real_cepstral_coefficients_phase_spectrum_complex_cepstral = np.real(np.fft.ifft(complex_cepstral_coefficients_phase_spectrum_complex_cepstral, axis=0)) # apply lifter to real cepstral coefficients of phase spectrum of complex cepstral coefficients lifter_phase_spectrum_complex_cepstral = 22 lifter_vector_phase_spectrum_complex_cepstral = 1 + (lifter_phase_spectrum_complex_cepstral / 2) * np.sin(np.pi * np.arange(real_cepstral_coefficients_phase_spectrum_complex_cepstral.shape[0]) / lifter_phase_spectrum_complex_cepstral) lifter_matrix_phase_spectrum_complex_cepstral = np.tile(lifter_vector_phase_spectrum_complex_cepstral[:, np.newaxis], (1, real_cepstral_coefficients_phase_spectrum_complex_cepstral.shape[1])) real_cepstral_coefficients_phase_spectrum_complex_cepstral_lifted = real_cepstral_coefficients_phase_spectrum_complex_cepstral * lifter_matrix_phase_spectrum_complex_cepstral # apply lifter to real cepstral coefficients lifter = 22 lifter_vector = 1 + (lifter / 2) * np.sin(np.pi * np.arange(cepstral_coefficients.shape[0]) / lifter) lifter_matrix = np.tile(lifter_vector[:, np.newaxis], (1, cepstral_coefficients.shape[1])) cepstral_coefficients_lifted = cepstral_coefficients * lifter_matrix # combine real cepstral coefficients and real cepstral coefficients of phase spectrum of complex cepstral coefficients combined_cepstral_coefficients = np.concatenate((cepstral_coefficients_lifted, real_cepstral_coefficients_phase_spectrum_complex_cepstral_lifted), axis=0) ```

%对语音信号进行截短的代码 %加矩形窗时的倒谱的代码 %加矩形窗时的复倒谱的代码

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python 浮点数字符串的有效位数截短

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