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3.2 Music classification by genre: system diagram

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Music classification by genre system diagram

Music Matcher, a collection of scripts and functions, takes a .wav file input, digitally processes it, and creates an output vector characteristic of the sample. A neural network is trained with 20 songs in each genre. Then it analyzes the new song vectors for patterns and predicts an output classification genre.

Music Matcher takes a .wav file, analyzes it, and outputs a music genre. Our system breaks up a .wav file into twenty .5 second windows. From here, the DSP functions are called for each of the twenty windows. Each one of these twenty windows is analyzed by seven DSP functions:

  • Bandwidth
  • Power Spectral Density
  • Total Power (L-2 norm / L-infinity norm)
  • Spectrogram Smoothness
  • High Pass Filter
  • Beat Detection
  • Frequency Cutoff
The values returned from each of these functions is averaged over all twenty windows to give an average value for each song as well as a standard deviation, which tells us how these qualities change over time. That way, our DSP vector has some measure of how each of the functions changed with time.

First, the neural network is trained with 120 songs, 20 of each genre. After we train the neural network, we give it songs it has never seen, and the output of the system is the classification of genre that the neural network determines.

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can someone explain to me, an ignorant high school student, why the trend of the graph doesn't follow the fact that the higher frequency a sound wave is, the more power it is, hence, making me think the phons output would follow this general trend?
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Nevermind i just realied that the graph is the phons output for a person with normal hearing and not just the phons output of the sound waves power, I should read the entire thing next time
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Follow up question, does anyone know where I can find a graph that accuretly depicts the actual relative "power" output of sound over its frequency instead of just humans hearing
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Source:  OpenStax, Ece 301 projects fall 2003. OpenStax CNX. Jan 22, 2004 Download for free at http://cnx.org/content/col10223/1.5
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