  Audio fingerprinting is a technique used to identify and match audio
recordings based on their unique characteristics. It involves creating a
condensed representation of an audio signal that can be used to quickly compare
and match against other audio recordings. The fingerprinting process involves
analyzing the audio signal to extract certain features, such as spectral
content, tempo, and rhythm, among other things. In this paper, we present
SpectroMap, an open-source GitHub repository for audio fingerprinting written
in Python programming language. It is composed of a peak search algorithm that
extracts topological prominences from a spectrogram via time-frequency bands.
In this paper, we introduce the algorithm functioning with two experimental
applications in a high-quality urban sound dataset and environmental audio
recordings to describe how it works and how effective it is in handling the
input data. Finally, we have posed two Python scripts that would reproduce the
proposed case studies in order to ease the reproducibility of our audio
fingerprinting system.
