Acoustic scene classification

The goal of Acoustic Scene Classification (ASC) is to classify the actual environment into one of the provided three predefined classes (indoor, outdoor, in-vehicle) characterized with the acoustic captured by a single digital microphone.The demo runs on a small form factor board Sensor Tile that comes along with a smartphone application connected through Bluetooth Low Energy.

We used the FP-AI-SENSING1 function pack to build this example, running on an STEVAL-STLKT01V1 board. The ASC configuration captures audio at a 16 kHz (16-bit, 1 channel) rate using the on-board MEMS microphone.Every millisecond, a DMA interrupt is received with the last 16 PCM audio samples. These samples are then accumulated in a sliding window consisting of 1024 samples with a 50% overlap. For every 512 samples (i.e.,32 ms), the buffer is injected into the ASC preprocessing for feature extraction.The ASC preprocessing extracts audio features into a LogMel (30x32) spectrogram.
For computational efficiency and memory management optimization, the step is divided into two routines:

• The first part computes one of the 32 spectrogram columns from the time domain input signal into the Mel scale using FFT and Filter bank applications (30 mel bands).
• The second part, when all 32 columns have been calculated (i.e., after 1024 ms), a log scaling is applied to the mel scaled spectrogram, creating the input feature for the ASC convolutional neural network.

Every 1024ms, the (30x32) LogMel spectrogram is fed to the ASC convolutional neural network input, which can then classify the output labels: indoor, outdoor and in-vehicle.