When we look around at popular ML models today, we see a tendency toward computationally extreme solutions. Usually in ML, to make things more accurate, the easiest way is to add more data and make our models bigger. In general, it’s hard to make systems that are both accurate and efficient. To build Basic Pitch, we trained a neural network to predict MIDI note events given audio input. So now they can capture their ideas whenever inspiration strikes and get a head start on their compositions using the instrument of their choice, whether that’s guitar, flugelhorn, or their own voice.Įasy peasy! Well… Does better always have to mean bigger? Bottom: The output of Basic Pitch.īasic Pitch gives musicians and audio producers access to the power and flexibility of MIDI, whether they own specialized MIDI gear or not. The MIDI output can then be imported into a digital audio workstation for further adjustments.Ĭomparing nuance and accuracy using a guitar example. 2022).īy combining these properties, Basic Pitch lets you take input from a variety of instruments and easily turn it into MIDI output, with a high degree of nuance and accuracy. Speed: Basic Pitch is light on resources, and is able to run faster than real time on most modern computers ( Bittner et al.Basic Pitch supports this right out of the box. However, this valuable information is often lost when turning audio into MIDI. Pitch bend detection: Instruments, like guitar or the human voice, allow for more expressiveness through pitch bending: vibrato, glissando, bends, slides, etc.Many systems limit users to only monophonic output (one note at a time, like a single vocal melody), or are built for only one kind of instrument.
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