AI-Based Fourier Analysis (aifourier)

aifourier_logo

"Machines can learn Fourier analysis."

aifourier is a Python library for AI-based Fourier analysis of audio signals, powered by Sinusoidal Neural Networks (SNN).

SinusoidalNN repo link (made by Jovan also): https://github.com/jovan-AIcoder/SinusoidalNN

Built on top of the SinusoidalNN python package framework, aifourier learns the frequency components of an audio signal through optimization instead of explicitly computing Fourier integrals.

✨ Features

  • 🔊 Analyze audio files (.wav, .mp3, .flac, .ogg)
  • 🌊 Built on top of SinusoidalNN
  • 🧠 Neural Fourier decomposition via optimization
  • 📊 Extract:
    • Frequencies (Hz)
    • Phase shifts
    • Amplitudes
  • ⚙️ Multiple optimizers
    • SGD
    • RMSprop
    • Adam
    • AdamW
    • Adagrad
    • Adadelta
    • Adamax
    • Nadam
    • Ftrl
  • 📈 Training MAE monitoring
  • ➕ Optional positive-frequency filtering
  • ➕ Optional absolute amplitudes
  • 💾 Save trained neural models
  • 📁 Pandas DataFrame output
  • ⚡ Simple one-line API

📦 Installation

pip install aifourier

🚀 Usage

import aifourier as aif

df = aif.analyze(
    "audio.mp3",
    optimizer="adam"
)

print(df.head())

📊 Output

The returned DataFrame contains:

Column Description
Frequencies Learned frequencies (Hz)
Phase shift Learned phase shifts
Amplitudes Learned amplitudes

🧠 How It Works

Instead of evaluating Fourier coefficients analytically,

y(t) ≈ Σ Aᵢ sin(ωᵢ t + φᵢ)

Where:

  • Aᵢ = amplitude
  • ωᵢ = angular frequency
  • φᵢ = phase shift

these parameters are learned directly from data using a Sinusoidal Neural Network.

⚙️ Parameters

aif.analyze(
    audio_path,
    max_modes=10000,
    epochs=256,
    use_phase_shift=True,
    learning_rate=1e-5,
    save_model=None,
    verbose=2,
    positive_freqs_only=True,
    abs_amplitudes=True,
    optimizer="adam",
    show_spectrum=False,
    save_spectrum=None
)
Parameter Description
audio_path Audio file path
max_modes Maximum number of sinusoidal modes
epochs Number of training epochs
use_phase_shift Learn phase shifts
learning_rate Optimizer learning rate
save_model Save trained model
verbose Training verbosity (0, 1, 2)
positive_freqs_only Keep only positive frequencies
abs_amplitudes Return absolute amplitudes
optimizer Training optimizer
show_spectrum Show spectrum plot preview
save_spectrum Save spectrum plot figure to desired folder path

📁 Example

See the examples/ folder:

cd examples
python example.py

Example workflow:

  • Analyze bird.mp3
  • Learn sinusoidal spectrum
  • Export frequency table
  • Plot the learned spectrum

The learned spectrum is shown below.

Spectrum of bird.mp3

🌊 Relationship with SinusoidalNN

SinusoidalNN
        │
        ▼
   aifourier

SinusoidalNN provides the neural network engine.

aifourier provides an easy-to-use interface for AI-based Fourier analysis of audio signals.

💡 Future Ideas

  • Audio reconstruction
  • FFT comparison utilities
  • Real-time microphone analysis
  • Radio signal analysis
  • Oscilloscope support
  • Complex-valued SinusoidalNN

👤 Author

Jovan

2026

📜 License

MIT License

"What Fourier derives analytically, Sinusoidal Neural Networks can approximate through learning."

Project links

Data verified by PyPI on Aug 3, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (5.0.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for jovanAIcoder from gravatar.com jovanAIcoder

Credits

Author: Jovan

GitHub Statistics

Data verified by PyPI on Aug 3, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

MIT License

Requires

Python >=3.8

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

aifourier-4.1.0.tar.gz (5.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

aifourier-4.1.0-py3-none-any.whl (5.8 kB view details)

Uploaded Python 3

File details

Details for the file aifourier-4.1.0.tar.gz.

File metadata

  • Download URL: aifourier-4.1.0.tar.gz
  • Upload date:
  • Size: 5.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for aifourier-4.1.0.tar.gz
Algorithm Hash digest
SHA256 78998a316f075f4092d88eaa7cdad78123a62bb0d92a8cc2bf442d6b30791bca
MD5 603b6cc89f44cd90aed7dc8bec84926d
BLAKE2b-256 3d1f636568f0bf58285995adcd8c2444e74e544e47564e95ff2a197921525f81

See more details on using hashes here.

Provenance

The following attestation bundles were made for aifourier-4.1.0.tar.gz:

Publisher: python-publish.yml on jovan-AIcoder/AI-Based-Fourier-Analysis

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file aifourier-4.1.0-py3-none-any.whl.

File metadata

  • Download URL: aifourier-4.1.0-py3-none-any.whl
  • Upload date:
  • Size: 5.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for aifourier-4.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 15881e59e4f974c05988c1976d5e0eb8e7e01537ee2b210cf1ffc7b94b248a2d
MD5 c231f462af488f2f1c2a2dabc3fc3fc0
BLAKE2b-256 4e86422311483a98a1c3df97628229d5370bc05b58df33279eea9885e475d984

See more details on using hashes here.

Provenance

The following attestation bundles were made for aifourier-4.1.0-py3-none-any.whl:

Publisher: python-publish.yml on jovan-AIcoder/AI-Based-Fourier-Analysis

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page