#!/usr/bin/env python # -*- coding: utf-8 -*- """ Visualizing non-spectral data ============================= This section demonstrates how `specshow` can be used to visualize data other than spectrograms. """ # %% # .. _tutorial-display-nonspectral: # # The `librosa.display.specshow` function was designed primarily for displaying spectrograms, # but it is flexible enough to support a variety of other data types and derived features. # As we will see below, data or features to be visualized often share at least one axis (time or frequency) # with spectrograms, and can therefore be displayed using the same interface. # %% # Chroma # ------ # Chroma features, as introduced in the section on :ref:`harmony `, represent # harmonic content of a signal, with all pitches reduced to a single octave of *pitch classes*. # Typically the time axis is still equivalent to the time axis of a spectrogram, and only the "frequency" axis is different. # # `librosa.display.specshow` can be used to visualize chroma features in the same way as spectrograms, # just by setting `y_axis='chroma'`. # The following example shows a chromagram (bottom) aligned with the corresponding spectrogram (top). import librosa import numpy as np import matplotlib.pyplot as plt from IPython.display import HTML # sphinx_gallery_thumbnail_number = 5 y, sr = librosa.loadx("trumpet") HTML(librosa.util.example_info("trumpet", html=True)) # %% # stft = librosa.stft(y) chroma = librosa.feature.chroma_cqt(y=y, sr=sr, threshold=0.5) fig, ax = plt.subplots(nrows=2, sharex=True) librosa.display.specshow(stft, y_axis="log_oct3", x_axis="time", vscale="dBFS", ax=ax[0]) librosa.display.specshow(chroma, y_axis="chroma", x_axis="time", ax=ax[1]) ax[0].label_outer() # %% # Just like with other notation-related functions, chroma displays can be configured to # spell pitches within a given key or mode. For example, we can display the chromagram above # in the key of `F dorian` by setting `key='F:dorian'` in the call to `specshow`. fig, ax = plt.subplots(figsize=(6, 3)) librosa.display.specshow(chroma, y_axis="chroma", x_axis="time", key="F:dorian", ax=ax) # %% # The data here is unchanged - the only difference in the resulting plot is that the # vertical tick positions and corresponding labels have been shifted to match that # of the specified key. # %% # Tempograms # ---------- # Tempograms are a representation which aims to capture the local rhythmic structure # of a signal. There are fundamentally two kinds of tempogram: # autocorrelation-based and Fourier-based. Both kinds of tempogram share the same # time axis as a spectrogram, but the "frequency" axis is replaced with a *tempo* # axis, which for autocorrelation tempograms is measured in units of frame lag # (smaller lag corresponding to faster tempo), while Fourier tempograms measure in # units of Beats per Minute (BPM). # `specshow` can work with both kinds, as seen below. y, sr = librosa.loadx("nutcracker", duration=45) HTML(librosa.util.example_info("nutcracker", html=True)) # %% # # First the autocorrelation tempogram tempo_ac = librosa.feature.tempogram(y=y, sr=sr) # And now the Fourier tempogram tempo_ft = librosa.feature.fourier_tempogram(y=y, sr=sr) fig, ax = plt.subplots(nrows=2, sharex=True) # Plot the autocorrelation tempogram, but clip negative values to zero for better visualization librosa.display.specshow(np.maximum(tempo_ac, 0), y_axis="tempo", x_axis="time", ax=ax[0]) librosa.display.specshow(tempo_ft, y_axis="fourier_tempo", x_axis="time", vscale="dBFS", top_db=24, ax=ax[1]) ax[0].label_outer() ax[0].set(title="Autocorrelation Tempogram") ax[1].set(title="Fourier Tempogram") # %% # For both kinds of tempogram display, the vertical axis is mapped to units of BPM. # %% # Recurrence plots # ---------------- # Any axis mode can be applied to either the horizontal (x) or vertical (y) axis of a display. # In fact, one can even apply the same axis mode to both axes, which is useful for visualizing *recurrence* or # *self-similarity* matrices. # Recurrence plots are often used in structural analysis of music, and are computed by comparing the signal at # each time `t` to the signal at every other time `t'`. The result is a square matrix of similarity values, which can # be visualized using `specshow` with the same axis mode on both axes. # # In the following example, we'll compute a recurrence matrix based on the chroma features of a signal, which # essentially captures repetitions of harmony at different points in time. # Compute chroma. Threshold of 0.5 is mainly for clarity of visualization chroma = librosa.feature.chroma_cqt(y=y, sr=sr, threshold=0.5) rec = librosa.segment.recurrence_matrix(chroma, mode="affinity") fig, ax = plt.subplots(figsize=(4, 4)) # We'll use a reversed grayscale colormap here so that the relatively few positive values are more visible librosa.display.specshow(rec, y_axis="time", x_axis="time", ax=ax, cmap="gray_r") ax.set(title="Recurrence Plot") # %% # Whenever `specshow` detects that the same axis mode is being applied on both axes, # and the data has equal size along each axis, it will automatically set the aspect ratio of the display to be equal, # so that the resulting plot is square. # # Most of the time this behavior is desirable, but it can sometimes be useful to override it to allow for more flexible # layouts. # # For example, we can extend the above visualization to show the original spectrogram data along with the recurrence plot. # We'll use subplot mosaic to create named subplot axes, and link the axes across related subplots. # In this case, forcing a square aspect ratio on the recurrence plot leads to a smaller subplot with more empty space # between it and the spectrograms, which can make visual alignment more difficult. # By setting `auto_aspect=False`, we can allow the recurrence plot to fill the available space, while still keeping the axes # linked for alignment. stft = librosa.stft(y) fig, ax = plt.subplot_mosaic("ABB;ABB;.CC", figsize=(8, 8), layout="compressed") # Link the axes between subplots ax["B"].sharey(ax["A"]) ax["B"].sharex(ax["C"]) # We'll put the recurrence plot in the upper-right (B subplot) librosa.display.specshow(rec, y_axis="time", x_axis="time", ax=ax["B"], auto_aspect=False, cmap="gray_r") # The regular spectrogram along the bottom (C subplot) librosa.display.specshow(stft, y_axis="log_oct3", x_axis="time", vscale="dBFS", ax=ax["C"]) # And a transposed copy of the spectrogram along the left (A subplot) librosa.display.specshow(stft.T, x_axis="log_oct3", y_axis="time", vscale="dBFS", ax=ax["A"]) # Invert the horizontal axis on the transposed spectrogram so that the lowest frequency # appears on the right side, not the left ax["A"].invert_xaxis() # Hide redundant axis labels and ticks on the inner axes ax["A"].label_outer() ax["B"].label_outer() ax["C"].yaxis.tick_right() ax["C"].yaxis.set_label_position("right") # %% # Summary # ------- # The flexibility of `librosa.display.specshow` allows it to be used for visualizing # a wide variety of data types and derived features. Although the function appears # at first glance to have a staggering number of parameters, most of them are there # to mirror the parameters of the related signal transformation functions for which # we have built-in support.