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Aaron-AALG committed Nov 8, 2022
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======================

![python-version](https://img.shields.io/badge/python->=3.8-green.svg)
[![pypi-version](https://img.shields.io/pypi/v/spectromap.svg)](https://pypi.python.org/pypi/spectromap/)
[![pypi-version](https://img.shields.io/pypi/v/spectromap.svg)](https://pypi.python.org/pypi/uwtopsis/)
![license](https://img.shields.io/pypi/l/spectromap.svg)
[![Downloads](https://static.pepy.tech/personalized-badge/spectromap?period=total&units=international_system&left_color=grey&right_color=orange&left_text=Downloads)](https://pepy.tech/project/spectromap)
[![Downloads](https://static.pepy.tech/personalized-badge/spectromap?period=total&units=none&left_color=grey&right_color=orange&left_text=Downloads)](https://pepy.tech/project/spectromap)

SpectroMap is a peak detection algorithm that computes the constellation map (or audio fingerprint) of a given signal.

![img](Images/peak_search.png)

This packages was developed as an application in music plagiarism: [A proposal to compare the similarity between musical products. One more step for automated plagiarism detection?](https://doi.org/10.1007/978-3-031-07015-0_16), in order to reduce the computational cost that imply music comparisons.

Installation
======================

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condition = 2 # Axis to analyze (0: Time, 1: Frequency, 2: Time+Frequency)
id_peaks, peaks = peak_search(spectrogram, fraction, condition)
```

Cite this work
======================

If you use SpectroMap in your research I would appreciate a citation to the following paper:

```bibtex
@misc{https://doi.org/10.48550/arxiv.2211.00982,
doi = {10.48550/ARXIV.2211.00982},
url = {https://arxiv.org/abs/2211.00982},
author = {López-García, Aarón},
title = {SpectroMap: Peak detection algorithm for audio fingerprinting},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```

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