Skip to content Skip to navigation

Connexions

You are here: Home » Content » Detecting Notes or Chords

Navigation

Content Actions

  • Download module PDF
  • Add to ...
    Add the module to:
    • My Favorites
    • A lens
    • An external social bookmarking service
    • My Favorites (What is 'My Favorites'?)
      'My Favorites' is a special kind of lens which you can use to bookmark modules and collections directly in Connexions. 'My Favorites' can only be seen by you, and collections saved in 'My Favorites' can remember the last module you were on. You need a Connexions account to use 'My Favorites'.
    • A lens (What is a lens?)

      Definition of a lens

      Lenses

      A lens is a custom view of Connexions content. You can think of it as a fancy kind of list that will let you see Connexions through the eyes of organizations and people you trust.

      What is in a lens?

      Lens makers point to Connexions materials (modules and collections), creating a guide that includes their own comments and descriptive tags about the content.

      Who can create a lens?

      Any individual Connexions member, a community, or a respected organization.

    • External bookmarks
  • E-mail the author

Recently Viewed

Detecting Notes or Chords

Module by: Rob Smith

Summary: This module demonstrates how we use Matlab to determine if a guitar player is playing single notes or chords.

The input file is divided into 8000 sample lengths with each chunk being processed separately For the 48kHz sample rate we used in our tests, that means each section is 1/6th of a second long. The program takes the FFT of chunk and then uses a low pass filter to smooth out the peaks.

Figure 1: The program correctly detected the first note and found its harmonic. There is one additional peak between the first note and its harmonic, so this is a two note chord.
FFT of Sample Chunk with Notes Marked
FFT of Sample Chunk with Notes Marked (FFT of Signal Chunk 6.png)

The peak detection works by finding the maximum value of the given signal, and then looking for areas where the signal is higher than 1/3rd of that maximum. As the stems on the graph demonstrates, the program marks peaks on their falling edge, right as they pass back below the threshold. This method works fairly well, but will sometimes miss a peak if it isn't as high as the threshold. If I were to lower the threshold, however, noise would sometimes be counted as a peak.

Figure 2: The number of notes per chunk is recorded into a long vector which is returned back to the primary program. This information is then used by the program to determine the appropriate effects levels.
Number of Simultaneous Notes Over Song
Number of Simultaneous Notes Over Song (NotesPlayedOverSong.png)
Figure 3: This is the Matlab code for the function findChord.
Media File: findChord.m

Comments, questions, feedback, criticisms?

Send feedback