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.

翻译：音频指纹是一种基于独特特征识别和匹配音频记录的技术。它涉及创建音频信号的压缩表示，用于快速比较和匹配其他音频记录。指纹处理过程通过分析音频信号提取特定特征，如频谱内容、速度和节奏等。本文介绍了SpectroMap——一个用Python编写的开源GitHub音频指纹仓库。该算法由峰值搜索算法组成，通过时频带从频谱图中提取拓扑显著特征。本文通过两个实验应用（高质量城市声音数据集和环境音频记录）展示算法运作方式及其处理输入数据的有效性。最后，我们提供了两个Python脚本用于复现所提出的案例研究，以促进音频指纹系统的可复现性。