我们从Python开源项目中，提取了以下30个代码示例，用于说明如何使用scipy.signal.decimate()。
def harvest_get_downsampled_signal(x, fs, target_fs): decimation_ratio = np.round(fs / target_fs) offset = np.ceil(140. / decimation_ratio) * decimation_ratio start_pad = x[0] * np.ones(int(offset), dtype=np.float32) end_pad = x[-1] * np.ones(int(offset), dtype=np.float32) x = np.concatenate((start_pad, x, end_pad), axis=0) if fs < target_fs: raise ValueError("CASE NOT HANDLED IN harvest_get_downsampled_signal") else: try: y0 = sg.decimate(x, int(decimation_ratio), 3, zero_phase=True) except: y0 = sg.decimate(x, int(decimation_ratio), 3) actual_fs = fs / decimation_ratio y = y0[int(offset / decimation_ratio):-int(offset / decimation_ratio)] y = y - np.mean(y) return y, actual_fs
def fetch_sample_speech_ono(n_samples=None): datapath = os.path.join("ono_wav", "*wav") wav_names = glob.glob(datapath) wav_names = [w for w in wav_names if "EKENWAY" in w] wav_names = [w for w in wav_names if "PAIN" in w] speech = [] logger.info("Loading speech files...") for wav_name in wav_names[:n_samples]: fs, bitw, d = readwav(wav_name) # 24 bit but only 16 used??? d = d.astype('float32') / (2 ** 15) d = sg.decimate(d, 6, ftype="fir")[::6] # decimate to 8k fs = 8000 speech.append(d) return fs, speech, wav_names
def fetch_sample_speech_walla(n_samples=None): datapath = os.path.join("walla_wav", "*wav") names = glob.glob(datapath) speech = [] wav_names = [] logger.info("Loading speech files...") for name in names[:n_samples]: fs, bitw, d = readwav(name) d = d.astype('float32') / (2 ** 15) inds = np.arange(0, len(d), 16000) for i, j in zip(inds[:-1], inds[1:]): dij = d[i:j] dij = sg.decimate(dij, 2, ftype="iir")[::2] # decimate to 8k fs = 8000 speech.append(dij) wav_names.append(name) if len(speech) > 200: break return fs, speech, wav_names
def wavwrite(srcfile, fs, training): try: mat = io.loadmat(srcfile) except ValueError: print('Could not load %s' % srcfile) return dat = mat['dataStruct'][0, 0][0] if ds_factor != 1: dat = signal.decimate(dat, ds_factor, axis=0, zero_phase=True) mn = dat.min() mx = dat.max() mx = float(max(abs(mx), abs(mn))) if training and mx == 0: print('skipping %s' % srcfile) return if mx != 0: dat *= 0x7FFF / mx dat = np.int16(dat) winsize = win_dur * 60 * fs stride = 60 * fs for elec in range(16): aud = dat[:, elec] for win in range(nwin): dstfile = srcfile.replace('mat', str(win) + '.' + str(elec) + '.wav') beg = win * stride end = beg + winsize clip = aud[beg:end] audiolab.wavwrite(clip, dstfile, fs=fs, enc='pcm16')
def downSample(data, sampleRate = 20000, dsType = 'mean'): """ Function that downsamples data. :param data: list including syllables with sample data :param sampleRate: desired samplerate :param dsType: Type of interpolating used for downsampling. Can be mean or IIR, which uses an order 8 Chebyshev type 1 filter (default = mean) :returns syllables: downsampled data, in same format as input data """ syllables = [] for syllable in data: samples = [] for sample in syllable: SR = int(np.round(sample[1]/float(sampleRate))) if dsType == 'mean': pad_size = int(math.ceil(float(sample[0].size)/SR)*SR - sample[0].size) s_padded = np.append(sample[0], np.zeros(pad_size)*np.NaN) s_new = sp.nanmean(s_padded.reshape(-1,SR), axis=1) elif dsType == 'IIR': s_new = ss.decimate(sample[0],SR) samples.append([s_new, sampleRate]) syllables.append(samples) return syllables #%%
def downsampleBy(v, factor): order = 8 # default value for decimate() anyway minSamples = 5 if len(v) < (minSamples * factor): factor = int(len(v) / minSamples) v = signal.decimate(v, factor, n=order) if len(v) >= (order + minSamples): return v[(order - 1):] return v
def downsampleMat(A, rowsBy=1, colsBy=1): if len(A.shape) == 1: return signal.decimate(A, rowsBy, n=(rowsBy-1)) if rowsBy != 1: A = signal.decimate(A, rowsBy, n=(rowsBy-1), axis=0) if colsBy != 1: A = signal.decimate(A, rowsBy, n=(colsBy-1), axis=1) return A # A = A.reshape(-1, 1) # newLen = int(A.shape[0] / float(rowsBy)) # resampled = imresize(A, (newLen, 1)) # return resampled.flatten() # newShape = A.shape / np.array([rowsBy, colsBy], dtype=np.float) # newShape = newShape.astype(np.int) # round to int # return imresize(A, newShape)
def sinusoid_analysis(X, input_sample_rate, resample_block=128, copy=True): """ Contruct a sinusoidal model for the input signal. Parameters ---------- X : ndarray Input signal to model input_sample_rate : int The sample rate of the input signal resample_block : int, optional (default=128) Controls the step size of the sinusoidal model Returns ------- frequencies_hz : ndarray Frequencies for the sinusoids, in Hz. magnitudes : ndarray Magnitudes of sinusoids returned in ``frequencies`` References ---------- D. P. W. Ellis (2004), "Sinewave Speech Analysis/Synthesis in Matlab", Web resource, available: http://www.ee.columbia.edu/ln/labrosa/matlab/sws/ """ X = np.array(X, copy=copy) resample_to = 8000 if input_sample_rate != resample_to: if input_sample_rate % resample_to != 0: raise ValueError("Input sample rate must be a multiple of 8k!") # Should be able to use resample... ? # resampled_count = round(len(X) * resample_to / input_sample_rate) # X = sg.resample(X, resampled_count, window=sg.hanning(len(X))) X = sg.decimate(X, input_sample_rate // resample_to, zero_phase=True) step_size = 2 * round(resample_block / input_sample_rate * resample_to / 2.) a, g, e = lpc_analysis(X, order=8, window_step=step_size, window_size=2 * step_size) f, m = lpc_to_frequency(a, g) f_hz = f * resample_to / (2 * np.pi) return f_hz, m
