Spectrograms of heartbeat audio
Spectral engineering is one of the most common techniques in machine learning for time series data. The first step in this process is to calculate a spectrogram of sound. This describes what spectral content (e.g., low and high pitches) are present in the sound over time. In this exercise, you'll calculate a spectrogram of a heartbeat audio file.
We've loaded a single heartbeat sound in the variable audio.
This exercise is part of the course
Machine Learning for Time Series Data in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Import the stft function
____
# Prepare the STFT
HOP_LENGTH = 2**4
spec = ____(audio, hop_length=HOP_LENGTH, n_fft=2**7)