.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/plot_audio_playback.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note :ref:`Go to the end ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_plot_audio_playback.py: ============== Audio playback ============== This notebook demonstrates how to use IPython's audio playback to play audio signals through your web browser. .. GENERATED FROM PYTHON SOURCE LINES 11-15 .. code-block:: Python # Code source: Brian McFee # License: ISC .. GENERATED FROM PYTHON SOURCE LINES 16-17 We'll need numpy and matplotlib for this example .. GENERATED FROM PYTHON SOURCE LINES 17-30 .. code-block:: Python import numpy as np import matplotlib.pyplot as plt # sphinx_gallery_thumbnail_path = '_static/playback-thumbnail.png' import librosa import librosa.display # We'll need IPython.display's Audio widget from IPython.display import Audio # We'll also use `mir_eval` to synthesize a signal for us import mir_eval.sonify .. GENERATED FROM PYTHON SOURCE LINES 31-38 Playing a synthetic sound ------------------------- The IPython Audio widget accepts raw numpy data as audio signals. This means we can synthesize signals directly and play them back in the browser. For example, we can make a sine sweep from C3 to C5: .. GENERATED FROM PYTHON SOURCE LINES 38-48 .. code-block:: Python sr = 22050 y_sweep = librosa.chirp(fmin=librosa.note_to_hz('C3'), fmax=librosa.note_to_hz('C5'), sr=sr, duration=1) Audio(data=y_sweep, rate=sr) .. raw:: html

.. GENERATED FROM PYTHON SOURCE LINES 49-54 Playing a real sound -------------------- Of course, we can also play back real recorded sounds in the same way. .. GENERATED FROM PYTHON SOURCE LINES 54-59 .. code-block:: Python y, sr = librosa.load(librosa.ex('trumpet')) Audio(data=y, rate=sr) .. raw:: html

.. GENERATED FROM PYTHON SOURCE LINES 60-68 Sonifying pitch estimates ------------------------- As a slightly more advanced example, we can use sonification to directly observe the output of a fundamental frequency estimator. We'll do this using `librosa.pyin` for analysis, and `mir_eval.sonify.pitch_contour` for synthesis. .. GENERATED FROM PYTHON SOURCE LINES 68-79 .. code-block:: Python # Using fill_na=None retains the best-guess f0 at unvoiced frames f0, voiced_flag, voiced_probs = librosa.pyin(y, sr=sr, fmin=librosa.note_to_hz('C2'), fmax=librosa.note_to_hz('C7'), fill_na=None) # To synthesize the f0, we'll need sample times times = librosa.times_like(f0) .. GENERATED FROM PYTHON SOURCE LINES 80-87 mir_eval's synthesizer uses negative f0 values to indicate unvoiced regions. We'll make an array vneg which is 1 for voiced frames, and -1 for unvoiced frames. This way, `f0 * vneg` will leave voiced estimates unchanged, and negate the frequency for unvoiced frames. .. GENERATED FROM PYTHON SOURCE LINES 87-94 .. code-block:: Python vneg = (-1)**(~voiced_flag) # And sonify the f0 using mir_eval y_f0 = mir_eval.sonify.pitch_contour(times, f0 * vneg, sr) Audio(data=y_f0, rate=sr) .. raw:: html

.. GENERATED FROM PYTHON SOURCE LINES 95-108 Sonifying mixtures ------------------ Finally, we can also use the Audio widget to listen to combinations of signals. This example runs the onset detector over the original test clip, and then synthesizes a click at each detection. We can then overlay the click track on the original signal and hear them both. For this to work, we need to ensure that both the synthesized click track and the original signal are of the same length. .. GENERATED FROM PYTHON SOURCE LINES 108-122 .. code-block:: Python # Compute the onset strength envelope, using a max filter of 5 frequency bins # to cut down on false positives onset_env = librosa.onset.onset_strength(y=y, sr=sr, max_size=5) # Detect onset times from the strength envelope onset_times = librosa.onset.onset_detect(onset_envelope=onset_env, sr=sr, units='time') # Sonify onset times as clicks y_clicks = librosa.clicks(times=onset_times, length=len(y), sr=sr) Audio(data=y+y_clicks, rate=sr) .. raw:: html

.. GENERATED FROM PYTHON SOURCE LINES 123-140 Caveats ------- Finally, some important things to note when using interactive playback: - `IPython.display.Audio` works by serializing the entire audio signal and sending it to the browser in a UUEncoded stream. This may be inefficient for long signals. - `IPython.display.Audio` can also work directly with filenames and URLs. If you're working with long signals, or do not want to load the signal into python directly, it may be better to use one of these modes. - Audio playback, by default, will normalize the amplitude of the signal being played. Most of the time this is what you will want, but sometimes it may not be, so be aware that normalization can be disabled. - If you're working in a Jupyter notebook and want to show multiple Audio widgets in the same cell, you can use ``IPython.display.display(IPython.display.Audio(...))`` to explicitly render each widget. This is helpful when playing back multiple related signals. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 2.285 seconds) .. _sphx_glr_download_auto_examples_plot_audio_playback.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_audio_playback.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_audio_playback.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_