Summary
In this chapter, we have completed our music genre recognition application on the Arduino Nano and Raspberry Pi Pico. Here, our focus has been optimizing the MFCCs feature extraction algorithm through software optimizations, using fixed-point arithmetic. Additionally, we have developed, trained, and deployed an RNN model that relies on the LSTM operator to classify music genres.
Our practical journey started by getting acquainted with fixed-point arithmetic and its role in accelerating the extraction of MFCCs from audio clips. To achieve this objective, the Python CMSIS-DSP played a crucial role, helping us to develop an algorithm in a Python environment that closely resembles the final implementation on the microcontroller.
Following the implementation of the MFCCs feature extraction, our attention shifted to model design. Here, we designed and trained an RNN model based on the LSTM layer with TensorFlow to classify music genres.
Once the model was trained, we...
