Late, than: The relative highness or lowness of a sound
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As always, if you would like to view the code in Jupyter notebooks, you can find everything on my GitHub. What is Pitch?
The higher the sound, the higher the pitch and the lower the sound, the lower the pitch. Simple enough, right? To fully understand pitch, we need to understand the pitch classes and octaves. Pitch classes are the letters that are associated with each sound.
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We would keep going up one white key until we hit the key G. As we ascend with each white key, we inevitably cycle through the letters over and over. However, when we hit the second A key after the G, it is important to note that it is not the same sound we started at originally. The pitch class is the same; it is the A key.
However the sound is higher than before, indicating we are in the next higher octave. This is because the black key is also before D. As for the case research paper michelangelo when there is no black key between two white keys, you do not have to worry about that for this article as that involves more music theory than what is necessary.
An octave is the number specifying which group of pitches we are in. If we repeated our the relative highness or lowness of a sound knowing we were in A3, the next A key we hit is A4. Again, this should not be too hard to wrap your head around.
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The middle key of the keyboard is C4 and it is generally used as a reference for which octave you are in. It is important to note that the number does not increase after every A value, but rather after every C value. About Chromagrams Now that we understand pitch in Music, we can dive into chroma filters which acts as the basis of our chromagrams. The Chroma filters can be derived from the Chroma filter bank.
The filter bank aims to project all the energy of the recorded sound into 12 bins i. By disregarding octave, we can then create a heat-map of how the pitch changes over time, which is an important aspect of MIR. For example, should you decide to teach an LSTM how to play music, these features would be very useful as they would tell the neural network how the pitch would likely change over time.
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Photo by Author By taking the dot product of our Fourier based spectrogram by taking the STFT and this filter bank, we can then map the songs in question onto the set of pitches we discussed prior. Luckily, thanks to librosa the function to create the chromagram has been created for us already. Note that we do not have to use the STFT on the audio as our basis, but that is outside the scope of this article. Photo by author Now that we can see how the pitch changes over time, we can see some interesting observations in each genre. Conclusion By now, you should have a good understanding of what pitch is, how we classify each pitch, what the chroma filter aims to do, and how we visualize chromagrams.]
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