Ukulele chords classification
Analysis of the vibrations produced by the instrument to detect the chord played.
Systems interacts with their environment by emitting various signals. These signals are sources of relevant information reflecting equipment functioning. Being able to understand these signals allows significant optimization capabilities. Machine learning helps make the data generated by the system into meaningful data for Humans.
For example, here a ML library allows to classify vibration pattern to recognize music chords. This approach can easily be adapted to other application to be able to classify various events and then to make smarter solutions.
For example, here a ML library allows to classify vibration pattern to recognize music chords. This approach can easily be adapted to other application to be able to classify various events and then to make smarter solutions.
Approach
We measured the vibration instead of the sound to reduce the impact of background noise
After analysis, a frequency of 2000Hz permit to recognize a chord. We set the accelerometer to 3300Hz (minimum sensor frequency)
We recorded examples of 20 different chords (100 signals per chords)
We created an n-Class Classification model in NanoEdge AI Studio and tested it live on a NUCLEO-L432KC (and a STEVAL-MKI178V1 with LSM6DSL)
After analysis, a frequency of 2000Hz permit to recognize a chord. We set the accelerometer to 3300Hz (minimum sensor frequency)
We recorded examples of 20 different chords (100 signals per chords)
We created an n-Class Classification model in NanoEdge AI Studio and tested it live on a NUCLEO-L432KC (and a STEVAL-MKI178V1 with LSM6DSL)
Sensor
Accelerometer (3 axes): LSM6DSL
Data
20 classes of data 20 Ukulele chords
Signal length 3072 (1024* 3 axes)
Data rate 3300 Hz
Signal length 3072 (1024* 3 axes)
Data rate 3300 Hz
Results
20 classes classification:
99.58% accuracy, 13.9 kB RAM, 82.9 kB Flash
Green points represent well classified signals. Red points represent misclassified signals. The classes are on the abscissa and the confidence of the prediction is on the ordinate
99.58% accuracy, 13.9 kB RAM, 82.9 kB Flash
Resources
Model created with NanoEdge AI Studio
A free AutoML software for adding AI to embedded projects, guiding users step by step to easily find the optimal AI model for their requirements.
Compatible with STM32
The STM32 family of 32-bit microcontrollers based on the Arm Cortex®-M processor is designed to offer new degrees of freedom to MCU users. It offers products combining very high performance, real-time capabilities, digital signal processing, low-power / low-voltage operation, and connectivity, while maintaining full integration and ease of development.
