pyaca

Python scripts accompanying the book "An Introduction to Audio Content Analysis" (www.AudioContentAnalysis.org)

https://github.com/alexanderlerch/pyaca

Science Score: 77.0%

This score indicates how likely this project is to be science-related based on various indicators:

✓
CITATION.cff file
Found CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
Found 3 DOI reference(s) in README
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Academic publication links
Links to: zenodo.org
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Committers with academic emails
1 of 3 committers (33.3%) from academic institutions
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (8.8%) to scientific vocabulary

Keywords

audio-content-analysis audio-features audio-processing music-informatics music-information-retrieval signal-processing

Last synced: 6 months ago · JSON representation ·

Repository

Python scripts accompanying the book "An Introduction to Audio Content Analysis" (www.AudioContentAnalysis.org)

Basic Info

Host: GitHub
Owner: alexanderlerch
License: mit
Language: Python
Default Branch: master
Homepage:
Size: 3.74 MB

Statistics

Stars: 171
Watchers: 8
Forks: 40
Open Issues: 2
Releases: 0

Topics

audio-content-analysis audio-features audio-processing music-informatics music-information-retrieval signal-processing

Created over 6 years ago · Last pushed 9 months ago

Metadata Files

Readme Funding License Citation

README.md

GitHub top language PyPI - Python Version GitHub release (latest SemVer) GitHub issues GitHub last commit GitHub

pyACA

Python scripts accompanying the book "An Introduction to Audio Content Analysis". The source code shows example implementations of basic approaches, features, and algorithms for music audio content analysis.

All implementations are also available in: * Matlab: ACA-Code * C++: libACA

functionality

The top-level functions are (alphabetical):

computeBeatHisto: calculates a simple beat histogram

computeChords: simple chord recognition

computeFeature: calculates instantaneous features

computeFingerprint: audio fingerprint extraction

computeKey: calculates a simple key estimate

computeMelSpectrogram: computes a mel spectrogram

computeNoveltyFunction: simple onset detection

computePitch: calculates a fundamental frequency estimate

computeSpectrogram: computes a magnitude spectrogram

The names of the additional functions follow the following conventions:

Feature*: instantaneous features

Pitch*: pitch tracking approach

Novelty*: novelty function computation

Tool*: additional helper functions and basic algorithms such as

Blocking of audio into overlapping blocks

Pre-processing audio

Conversion (freq2bark, freq2mel, freq2midi, mel2freq, midi2freq)

Filterbank (Gammatone)

Gaussian Mixture Model

Principal Component Analysis

Feature Selection

Dynamic Time Warping

K-Means Clustering

K Nearest Neighbor classification

Non-Negative Matrix Factorization

Viterbi algorithm

documentation

The latest full documentation of this package can be found at https://alexanderlerch.github.io/pyACA.

design principles

Please note that the provided code examples are only intended to showcase algorithmic principles – they are not entirely suitable for practical usage without parameter optimization and additional algorithmic tuning. Rather, they intend to show how to implement audio analysis solutions and to facilitate algorithmic understanding to enable the reader to design and implement their own analysis approaches.

minimal dependencies

The required dependencies are reduced to a minimum, more specifically to only numpy and scipy, for the following reasons: * accessibility, i.e., clear algorithmic implementation from scratch without obfuscation by using 3rd party implementations, * maintainability through independence of 3rd party code. This design choice brings, however, some limitations; for instance, reading of non-RIFF audio files is not supported and the machine learning models are very simple.

readability

Consistent variable naming and formatting, as well as the choice for simple implementations allow for easier parsing. The readability of the source code will sometimes come at the cost of lower performance.

cross-language comparability

All code is matched exactly with Matlab implementations and the equations in the book. This also means that the python code might violate typical python style conventions in order to be consistent.

getting started

installation

console pip install pyACA

code examples

example 1: computation and plot of the Spectral Centroid

```python import pyACA import matplotlib.pyplot as plt

file to analyze

cPath = "c:/temp/test.wav"

extract feature

[v, t] = pyACA.computeFeatureCl(cPath, "SpectralCentroid")

plot feature output

plt.plot(t,np.squeeze(v)) ``` example 2: Computation of two features (here: Spectral Centroid and Spectral Flux)

```python import pyACA

read audio file

cPath = "c:/temp/test.wav" [f_s, afAudioData] = pyACA.ToolReadAudio(cPath)

compute feature

[vsc, t] = pyACA.computeFeature("SpectralCentroid", afAudioData, fs) [vsf, t] = pyACA.computeFeature("SpectralFlux", afAudioData, fs) ```

Owner

Name: Alexander Lerch
Login: alexanderlerch
Kind: user
Location: Atlanta
Company: Georgia Institute of Technology

Website: https://www.AudioContentAnalysis.org
Repositories: 24
Profile: https://github.com/alexanderlerch

Music Information Retrieval and Audio Content Analysis

Citation (citation.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Lerch"
  given-names: "Alexander"
  orcid: "0000-0001-6319-578X"
title: "pyACA for Audio Content Analysis"
version: v0.3.1
doi: 10.5281/zenodo.6310329
date-released: 2022-02-28
url: "https://github.com/alexanderlerch/pyACA"
preferred-citation:
  type: article
  authors:
  - family-names: "Lerch"
    given-names: "Alexander"
    orcid: "0000-0001-6319-578X"
  doi: "10.1016/j.simpa.2022.100349"
  journal: "Software Impacts"
  start: 100349
  title: "libACA, pyACA, and ACA-Code: Audio content analysis in 3 languages"
  year: 2022

GitHub Events

Total

Issues event: 1
Watch event: 16
Push event: 2
Fork event: 2

Last Year

Issues event: 1
Watch event: 16
Push event: 2
Fork event: 2

Committers

Last synced: almost 3 years ago

All Time

Total Commits: 192
Total Committers: 3
Avg Commits per committer: 64.0
Development Distribution Score (DDS): 0.234

Top Committers

Name	Email	Commits
alexanderlerch	a**h@g**u	147
Kaushal Sali	s**l@g**m	44
Richard Yang	R**g@b**m	1

Committer Domains (Top 20 + Academic)

bose.com: 1 gatech.edu: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time

Total issues: 26
Total pull requests: 10
Average time to close issues: 3 months
Average time to close pull requests: about 1 month
Total issue authors: 4
Total pull request authors: 4
Average comments per issue: 0.46
Average comments per pull request: 0.1
Merged pull requests: 9
Bot issues: 0
Bot pull requests: 1

Past Year

Issues: 1
Pull requests: 1
Average time to close issues: N/A
Average time to close pull requests: about 15 hours
Issue authors: 1
Pull request authors: 1
Average comments per issue: 0.0
Average comments per pull request: 0.0
Merged pull requests: 1
Bot issues: 0
Bot pull requests: 1

View more stats

Top Authors

Issue Authors

alexanderlerch (24)
desothier1 (1)
paranoid2droid (1)
zseramnay (1)

Pull Request Authors

kaushalsali (6)
alexanderlerch (3)
dependabot[bot] (2)
RichardYang40148 (1)

Top Labels

Issue Labels

enhancement (16) bug (4) documentation (1) annoyance (1)

Pull Request Labels

bug (2) dependencies (2)

Packages

Total packages: 1
Total downloads:
- pypi 285 last-month

Total dependent packages: 0
Total dependent repositories: 1
Total versions: 11
Total maintainers: 1

pypi.org: pyaca

scripts accompanying the book An Introduction to Audio Content Analysis by Alexander Lerch

Homepage: https://github.com/alexanderlerch/pyACA
Documentation: https://pyaca.readthedocs.io/
License: MIT
Latest release: 0.3.1
published almost 4 years ago

Versions: 11
Dependent Packages: 0
Dependent Repositories: 1
Downloads: 285 Last month

Rankings

Stargazers count: 6.0%

Forks count: 6.8%

Downloads: 8.4%

Dependent packages count: 10.0%

Average: 10.6%

Dependent repos count: 21.7%

Maintainers (1)

alexanderlerch

Last synced: 6 months ago

Dependencies

requirements.txt pypi

matplotlib *
numpy *
scipy *
setuptools-scm *

setup.py pypi

matplotlib *
numpy *
scipy *

.github/workflows/build_documentation.yml actions

EndBug/add-and-commit v8 composite
actions/checkout v2 composite
actions/download-artifact v3 composite
actions/upload-artifact v3 composite
mattnotmitt/doxygen-action edge composite

.github/workflows/build_test_publish.yml actions

actions/checkout master composite
actions/setup-python v5 composite
ncipollo/release-action v1 composite
pypa/gh-action-pypi-publish master composite

pyproject.toml pypi

pyaca

Science Score: 77.0%

Keywords

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

pyACA

functionality

documentation

design principles

minimal dependencies

readability

cross-language comparability

related repositories and links

getting started

installation

code examples

file to analyze

extract feature

plot feature output

read audio file

compute feature

Owner

Citation (citation.cff)

GitHub Events

Total

Last Year

Committers

All Time

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: pyaca

Rankings

Maintainers (1)

Dependencies