real time spectrogram

05569141 · Steve Tjoa · 598cd6da · 05569141 · 05569141 · 05569141
Commit 05569141 authored Jul 06, 2018 by Steve Tjoa
5 changed files
--- a/index.html
+++ b/index.html
@@ -12027,6 +12027,7 @@ div#notebook {
 <ol>
 <li><a href="pca.html">Principal Component Analysis</a> (<a href="pca.ipynb">ipynb</a>)</li>
 <li><a href="nmf.html">Nonnegative Matrix Factorization</a> (<a href="nmf.ipynb">ipynb</a>)</li>
+<li><a href="nmf_audio_mosaic.html">NMF Audio Mosaicing</a> (<a href="nmf_audio_mosaic.ipynb">ipynb</a>)</li>
 <li><a href="hpss.html">Harmonic-Percussive Source Separation</a> (<a href="hpss.ipynb">ipynb</a>)</li>
 </ol>

@@ -12046,6 +12047,7 @@ div#notebook {
 <div class="inner_cell">
 <div class="text_cell_render border-box-sizing rendered_html">
 <ol>
+<li><a href="realtime_spectrogram.html">Real-time Spectrogram</a> (<a href="realtime_spectrogram.ipynb">ipynb</a>)</li>
 <li><a href="thx_logo_theme.html">THX Logo Theme</a> (<a href="thx_logo_theme.ipynb">ipynb</a>)</li>
 </ol>


--- a/index.ipynb
+++ b/index.ipynb
@@ -204,6 +204,7 @@
   "source": [
    "1.  [Principal Component Analysis](pca.html) ([ipynb](pca.ipynb))\n",
    "1.  [Nonnegative Matrix Factorization](nmf.html) ([ipynb](nmf.ipynb))\n",
+    "1.  [NMF Audio Mosaicing](nmf_audio_mosaic.html) ([ipynb](nmf_audio_mosaic.ipynb))\n",
    "1.  [Harmonic-Percussive Source Separation](hpss.html) ([ipynb](hpss.ipynb))"
   ]
  },
@@ -218,6 +219,7 @@
   "cell_type": "markdown",
   "metadata": {},
   "source": [
+    "1. [Real-time Spectrogram](realtime_spectrogram.html) ([ipynb](realtime_spectrogram.ipynb))\n",
    "1. [THX Logo Theme](thx_logo_theme.html) ([ipynb](thx_logo_theme.ipynb))"
   ]
  }

--- a/realtime_spectrogram.html
+++ b/realtime_spectrogram.html
--- a/realtime_spectrogram.ipynb
+++ b/realtime_spectrogram.ipynb
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[&larr; Back to Index](index.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Real-time Spectrogram"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "tags": [
+     "hide_input"
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
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+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"600\"\n",
+       "            height=\"360\"\n",
+       "            src=\"https://www.youtube.com/embed/ydu12NfZd90\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x104fbc908>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import IPython.display as ipd\n",
+    "ipd.YouTubeVideo('ydu12NfZd90', width=600, height=360)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here is how you can create a real-time spectrogram in your terminal using [PyAudio](https://people.csail.mit.edu/hubert/pyaudio/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To see the example in action, run the script in this repo, `realtime_spectrogram.py`:\n",
+    "\n",
+    "    python3 realtime_spectrogram.py"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The basic idea is simple. For every new audio buffer,\n",
+    "\n",
+    "1. Take an FFT, `x_fft`, of the audio buffer.\n",
+    "2. Compute a `melspectrum` from the `x_fft`.\n",
+    "2. Print a string, `s`, where `s[i]` is `'*'` wherever `melspectrum[i]` is above a threshold."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From here, you can manipulate this basic example to do more sophisticated real-time processing, e.g. involving machine learning models."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[&larr; Back to Index](index.html)"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Tags",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
--- a/realtime_spectrogram.py
+++ b/realtime_spectrogram.py
 """
 musicinformationretrieval.com/realtime_spectrogram.py

-PyAudio example: display a live spectrogram in the terminal.
+PyAudio example: display a live log-spectrogram in the terminal.

 For more examples using PyAudio:
    https://github.com/mwickert/scikit-dsp-comm/blob/master/sk_dsp_comm/pyaudio_helper.py
@@ -11,30 +11,46 @@ import numpy
 import pyaudio
 import time

+# Define global variables.
 CHANNELS = 1
 RATE = 44100
 FRAMES_PER_BUFFER = 1000
 N_FFT = 4096
 SCREEN_WIDTH = 178
+ENERGY_THRESHOLD = 0.4

-BIN_LO = N_FFT*librosa.note_to_hz('C2')/RATE
-BIN_HI = N_FFT*librosa.note_to_hz('C7')/RATE
-FREQ_BINS = numpy.geomspace(BIN_LO, BIN_HI, SCREEN_WIDTH).astype('int')
+# Choose the frequency range of your log-spectrogram.
+F_LO = librosa.note_to_hz('C2')
+F_HI = librosa.note_to_hz('C9')
+M = librosa.filters.mel(RATE, N_FFT, SCREEN_WIDTH, fmin=F_LO, fmax=F_HI)

 p = pyaudio.PyAudio()

 def generate_string_from_audio(audio_data):
+    """
+    This function takes one audio buffer as a numpy array and returns a
+    string to be printed to the terminal.
+    """
+    # Compute real FFT.
    x_fft = numpy.fft.rfft(audio_data, n=N_FFT)
-    X = numpy.log10(1 + 0.1*abs(x_fft))

+    # Compute mel spectrum.
+    melspectrum = M.dot(abs(x_fft))
+
+    # Initialize output characters to display.
    char_list = [' ']*SCREEN_WIDTH

-    for i in range(len(FREQ_BINS)):
-        b = FREQ_BINS[i]
-        if X[b] > 0.3:
+    for i in range(SCREEN_WIDTH):
+
+        # If there is energy in this frequency bin, display an asterisk.
+        if melspectrum[i] > ENERGY_THRESHOLD:
            char_list[i] = '*'
+
+        # Draw frequency axis guidelines.
        elif i % 30 == 29:
            char_list[i] = '|'
+
+    # Return string.
    return ''.join(char_list)

 def callback(in_data, frame_count, time_info, status):
@@ -45,8 +61,8 @@ def callback(in_data, frame_count, time_info, status):
 stream = p.open(format=pyaudio.paFloat32,
                channels=CHANNELS,
                rate=RATE,
-                input=True,
-                output=True,
+                input=True,   # Do record input.
+                output=False, # Do not play back output.
                frames_per_buffer=FRAMES_PER_BUFFER,
                stream_callback=callback)