In this paper, we address a system that generates a bass line from a chord backing played on the electric guitar in an audio-to-audio manner. Yielding bass lines for guitar chord backings would be helpful for amateur musicians composing band music. Conventional music arrangement systems targeted MIDI-like symbolic music representations, but accurately obtaining symbolic representations from guitars takes work. To solve this problem, we attempt an audio-to-audio approach; Once the user gives an audio recording of the guitar’s chord backing, the system extracts some audio features (spectrogram, mel-spectrogram, or chromagram) and then generates an audio signal of bass lines using a convolutional neural network. The experimental results showed that the model with chromagrams generates bass lines the most robustly.

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An Audio-to-Audio Approach to Generate Bass Lines from Guitar’s Chord Backing

  • Tomoo Kouzai,
  • Tetsuro Kitahara

摘要

In this paper, we address a system that generates a bass line from a chord backing played on the electric guitar in an audio-to-audio manner. Yielding bass lines for guitar chord backings would be helpful for amateur musicians composing band music. Conventional music arrangement systems targeted MIDI-like symbolic music representations, but accurately obtaining symbolic representations from guitars takes work. To solve this problem, we attempt an audio-to-audio approach; Once the user gives an audio recording of the guitar’s chord backing, the system extracts some audio features (spectrogram, mel-spectrogram, or chromagram) and then generates an audio signal of bass lines using a convolutional neural network. The experimental results showed that the model with chromagrams generates bass lines the most robustly.