argmax () - len ( x1 ) return offset def make_mel ( spectrogram, mel_filter, shorten_factor = 1 ): mel_spec = np. astype ( "float32" )) corrs = - 1e30 corrs = - 1e30 offset = corrs. mean () frame_size = len ( x2 ) half = frame_size // 2 corrs = np. IEEE Transactions on Audio Speech and Language Processing, 08/2007. Real-Time Signal Estimation from Modified Short-Time Fourier Transform Magnitude Spectra. ![]() Auditory Model Inversion for Sound Separation. Malcolm Slaney, Daniel Naar and Richard F. Signal estimation from modified short-time Fourier transform. real ( wave ) / ( total_windowing_sum + 1e-6 ) return wave def xcorr_offset ( x1, x2 ): """ Under MSR-LA License Based on MATLAB implementation from Spectrogram Inversion Toolbox References - D. log10 ( specgram ) # take log specgram, wave_est, ) else : offset = 0 wave += ( win * wave_est ) total_windowing_sum += win wave = np. max () # volume normalize to max 1 specgram = np. abs ( stft ( d, fftsize = fft_size, step = step_size, real = False, compute_onesided = True ) ) if log = True : specgram /= specgram. arange ( size ) / ( size - 1 )) X = X * win X = local_fft ( X ) return X def pretty_spectrogram ( d, log = True, thresh = 5, fft_size = 512, step_size = 64 ): """ creates a spectrogram log: take the log of the spectrgram thresh: threshold minimum power for log spectrogram """ specgram = np. mean () X = overlap ( X, fftsize, step ) size = fftsize win = 0.54 - 0.46 * np. fft cut = None if compute_onesided : cut = fftsize // 2 if mean_normalize : X -= X. arange ( nw ): # "slide" the window along the samples start = i * ss stop = start + ws out = a return out def stft ( X, fftsize = 128, step = 65, mean_normalize = True, real = False, compute_onesided = True ): """ Compute STFT for 1D real valued input X """ if real : local_fft = np. hstack (( X, append )) ws = window_size ss = window_step a = X valid = len ( a ) - ws nw = ( valid ) // ss out = np. zeros (( window_size - len ( X ) % window_size )) X = np. ![]() ![]() # Most of the Spectrograms and Inversion are taken from: def butter_bandpass ( lowcut, highcut, fs, order = 5 ): nyq = 0.5 * fs low = lowcut / nyq high = highcut / nyq b, a = butter ( order, , btype = "band" ) return b, a def butter_bandpass_filter ( data, lowcut, highcut, fs, order = 5 ): b, a = butter_bandpass ( lowcut, highcut, fs, order = order ) y = lfilter ( b, a, data ) return y def overlap ( X, window_size, window_step ): """ Create an overlapped version of X Parameters - X : ndarray, shape=(n_samples,) Input signal to window and overlap window_size : int Size of windows to take window_step : int Step size between windows Returns - X_strided : shape=(n_windows, window_size) 2D array of overlapped X """ if window_size % 2 != 0 : raise ValueError ( "Window size must be even!" ) # Make sure there are an even number of windows before stridetricks append = np.
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