One property that puts our planned module apart from modules on the market which as we will get pitch-, gate-, and envelope-information from an input signal, is the usage of Music Information Retrieval (MIR). This relatively young and growing but still young field of research seeks to make music machine-readable with techniques of machine-learning. In todays’ music distribution which is by a big part catered to via streaming services, quick implementation and organization are crucial to monetize media collections and keep up with the market. This rather economic approach to music is merely one benefit to the capabilities of MIR. Things like source separation to create stems, for instance, transcription for notation programs, pitch tracking, tempo estimation and beat tracking for converting audio to MIDI for instance or have the chords of a song detected while playing it or Autotune, or key detection to quickly program quantizers in electronic music devices, can be useful tools in music education and music production and show a useful way to use MIR in an artistic sense.
There are more than methods to retrieve musical information. Some work with Data Source which derives its data mostly from digital audio formats such as .wav, .mp3, .ogg. Though many of those formats are lossy and machine listening is more deceptible to artifacts than the human ear much research in the field involves these in their data. Additionally, more and more metadata is mined from the web and incorporated into MIR for a better understanding of music in its cultural context.
Statistics and Machine learning play also an important role in this field of research. Many of the methods are comparing music to databases and come through that to information about music in question.
For the performance character of our module information retrieval has to come almost immediately from the signal put into the module without taking the computational time of searching databases. Feature representation must be the method in question to gain information quickly through an FFT for instance. Analysis of the music is achieved by summarising which is done by feature extraction. This summary has to give a feature representation that is reduced enough to reach a manageable set of values within a reasonable time frame.
As we ponder over the possibilities of MIR we should ask ourselves what could we retrieve from the signal to gain some knowledge over the expression of the musician playing into the synth. I did a short brainstorming with Prof. Ciciliani and we came up with a few parameters which we decided to make sense in a live performance.
Is the sound noiselike or tonelike?
This would give information about the sound coming from the instrument and if there would be a pitch to extract.
Is the sound bright or dark in its sonic character?
Information about the playing technique and depending on the instrument a form of expression as many instruments emit additional harmonics in the upper registers when played more vigorously.
What is the rate of change?
This can be interpreted in more ways. Over a longer period to get additional modulation after a phrase to create some kind of call and response or a performance reverb if we want to think out of the box. Or in addition to the envelope follower compare the Atack ramps of the signal to create a kind of punch trigger when the playing gets more intense.