今日『資料視覺化』之重要性，也許無須一再強調矣。因是

librosa 的使用者，自知

Display

 

模組之不可或缺也。

自能善用

librosa.display.waveplot

librosa.display.waveplot(y, sr=22050, max_points=50000.0, x_axis=’time’, offset=0.0, max_sr=1000, **kwargs)

Plot the amplitude envelope of a waveform.

If y is monophonic, a filled curve is drawn between [-abs(y), abs(y)].

If y is stereo, the curve is drawn between [-abs(y[1]), abs(y[0])], so that the left and right channels are drawn above and below the axis, respectively.

Long signals (duration >= max_points) are down-sampled to at most max_sr before plotting.

Parameters:

y : np.ndarray [shape=(n,) or (2,n)] audio time series (mono or stereo) sr : number > 0 [scalar] sampling rate of y max_points : postive number or None Maximum number of time-points to plot: if max_points exceeds the duration of y, then y is downsampled. If None, no downsampling is performed. x_axis : str {‘time’, ‘off’, ‘none’} or None If ‘time’, the x-axis is given time tick-marks. offset : float Horizontal offset (in time) to start the waveform plot max_sr : number > 0 [scalar] Maximum sampling rate for the visualization kwargs Additional keyword arguments to matplotlib.pyplot.fill_between

Returns:

pc : matplotlib.collections.PolyCollection The PolyCollection created by fill_between.

 

以及

librosa.display.specshow

librosa.display.specshow(data, x_coords=None, y_coords=None, x_axis=None, y_axis=None, sr=22050, hop_length=512, fmin=None, fmax=None, bins_per_octave=12, **kwargs)

Display a spectrogram/chromagram/cqt/etc.

Parameters:

data : np.ndarray [shape=(d, n)] Matrix to display (e.g., spectrogram) sr : number > 0 [scalar] Sample rate used to determine time scale in x-axis. hop_length : int > 0 [scalar] Hop length, also used to determine time scale in x-axis x_axis : None or str y_axis : None or str Range for the x- and y-axes. Valid types are: None, ‘none’, or ‘off’ : no axis decoration is displayed. Frequency types: ‘linear’, ‘fft’, ‘hz’ : frequency range is determined by the FFT window and sampling rate. ‘log’ : the spectrum is displayed on a log scale. ‘mel’ : frequencies are determined by the mel scale. ‘cqt_hz’ : frequencies are determined by the CQT scale. ‘cqt_note’ : pitches are determined by the CQT scale. All frequency types are plotted in units of Hz. Categorical types: ‘chroma’ : pitches are determined by the chroma filters. Pitch classes are arranged at integer locations (0-11). ‘tonnetz’ : axes are labeled by Tonnetz dimensions (0-5) ‘frames’ : markers are shown as frame counts. Time types: ‘time’ : markers are shown as milliseconds, seconds, minutes, or hours ‘lag’ : like time, but past the half-way point counts as negative values. All time types are plotted in units of seconds. Other: ‘tempo’ : markers are shown as beats-per-minute (BPM) using a logarithmic scale. x_coords : np.ndarray [shape=data.shape[1]+1] y_coords : np.ndarray [shape=data.shape[0]+1] Optional positioning coordinates of the input data. These can be use to explicitly set the location of each element data[i, j], e.g., for displaying beat-synchronous features in natural time coordinates. If not provided, they are inferred from x_axis and y_axis. fmin : float > 0 [scalar] or None Frequency of the lowest spectrogram bin. Used for Mel and CQT scales. If y_axis is cqt_hz or cqt_note and fmin is not given, it is set by default to note_to_hz(‘C1’). fmax : float > 0 [scalar] or None Used for setting the Mel frequency scales bins_per_octave : int > 0 [scalar] Number of bins per octave. Used for CQT frequency scale. kwargs : additional keyword arguments Arguments passed through to matplotlib.pyplot.pcolormesh. By default, the following options are set: rasterized=True shading=’flat’ edgecolors=’None’

Returns:

axes The axis handle for the figure.

 

乎？惟勤作練習耶！