ImageToSpectrogramPrediction

This project to convert audio to spectrogram images and train models that predict spectrograms from images (or vice-versa). This repository contains preprocessing utilities, dataset folders, and an example script AudioToSpectrogram.py.

Features

• Convert audio files to spectrogram images
• Organized dataset folders for source/target and train/test
• Starter script for conversion and experimentation

Repository structure

• AudioToSpectrogram.py - Example script to generate spectrograms from audio (project entrypoint)
• gearbox/
  • train/ - training audio files
  • source_test/ - source test audio files
  • target_test/ - target test audio files
• SpectrogramImage/
  • train/ - generated spectrogram images for training

Requirements

• Python 3.8+
• Typical packages used in audio & ML workflows (install as needed):

pip install numpy matplotlib librosa pillow tqdm
# plus your ML framework (tensorflow or torch) as required

If you prefer a pinned set of dependencies, create a requirements.txt and install with pip install -r requirements.txt.

Quick start

1. Create a virtual environment and install dependencies:

python -m venv .venv
# Windows PowerShell
.\.venv\Scripts\Activate.ps1
pip install numpy matplotlib librosa pillow tqdm

2. Convert audio to spectrogram images (example):

python AudioToSpectrogram.py

Note: AudioToSpectrogram.py may accept arguments depending on your local edits. Open the file and follow the usage comments near its top.

Dataset layout

• Place raw audio files to be converted inside gearbox/train/ for training, and gearbox/source_test/ / gearbox/target_test/ for test examples.
• Generated spectrogram images should be saved under SpectrogramImage/train/ (the project may include code that automatically writes here).

Training & Evaluation

• Use the prepared spectrogram images as input/targets for your model.
• Training scripts are not included by default — adapt your own training loop using your preferred framework (TensorFlow / PyTorch).

Tips

• Use librosa for audio loading and spectrogram generation.
• Normalize spectrograms before feeding them to a neural network.
• Keep train/test splits consistent between gearbox/ and SpectrogramImage/ folders.

Contributing

Contributions welcome — please open issues or pull requests for improvements, bug fixes, or additional scripts (training, evaluation, or dataset utilities).

Contact

If you have questions or want help extending this project, open an issue or contact the repository owner.

ImageToSpectrogramPrediction

Project to convert audio and image inputs into spectrogram representations and to train/predict spectrogram images using an autoencoder-style model.

Table of contents

• Overview
• Repository structure
• Dataset layout
• Pretrained model
• Installation
• Usage
  • Generate spectrograms from audio
  • Train / Notebooks
  • Inference (use pretrained model)
• Model & training details
• Tips and troubleshooting
• Contributing
• License

Overview

This repo contains code and notebooks for working with spectrogram images: creating spectrograms from audio, training an image-to-spectrogram model autoencoder

The goal is to predict or reconstruct spectrogram images from input data (images or audio-derived inputs) using convolutional autoencoder approaches. The repository includes example datasets, training notebooks, a utility script to create spectrogram images from audio, and a pretrained model file.

Repository structure

• AudioToSpectrogram.py — utility script to convert audio files to spectrogram images (example usage in notebooks).
• spectrogram_autoencoder_section00.h5 — provided pretrained Keras model weights.
• train.ipynb — primary training notebook (data preparation, model definition, training loop).
• traditional_train.ipynb — alternate training notebook (traditional approach / variations).
• test.ipynb — quick testing / evaluation examples.
• SpectrogramImage/ — folder containing spectrogram image datasets (split folders inside).
• gearbox/ — another dataset collection (similar structure to SpectrogramImage).

Inside each dataset folder (e.g., SpectrogramImage/ and gearbox/):

• train/ — training images.
• source_test/ — source inputs for test/validation.
• target_test/ — ground-truth spectrograms for test/validation.

Dataset layout and conventions

• Input images (source) are typically single-channel (grayscale) spectrogram images or other image representations.
• Target images are spectrogram images to be predicted/reconstructed by the model.
• Filenames for source/target pairs should match where applicable so the notebooks' loader can pair them for supervised training.

Installation

Recommended: create a Python virtual environment and install the packages below. The project targets Python 3.8+.

Example (Windows PowerShell):

python -m venv .venv
.\.venv\Scripts\Activate.ps1
pip install --upgrade pip
pip install tensorflow numpy matplotlib librosa pillow opencv-python scikit-image h5py tqdm jupyter

If you prefer, install a single-line requirements list:

pip install tensorflow numpy matplotlib librosa pillow opencv-python scikit-image h5py tqdm jupyter

Note: tensorflow will pull the correct CPU/GPU wheel if available. For GPU support on Windows, install the compatible CUDA/cuDNN for your TensorFlow version.

Usage

Generate spectrograms from audio

• Use AudioToSpectrogram.py or the notebooks to convert WAV (or other audio) files into spectrogram images. Notebooks include example conversion code and visualization.

Training (notebooks)

• Open train.ipynb or traditional_train.ipynb in Jupyter to run full training pipelines. The notebooks contain the data-loading, preprocessing, model definition, training loops, and checkpoints.

Inference (load pretrained model)

Example Python snippet to load the included model and predict on a single input image:

from tensorflow.keras.models import load_model
import numpy as np
from PIL import Image

# Load model
model = load_model('spectrogram_autoencoder_section00.h5')

# Load a single grayscale image (example path)
img = Image.open('SpectrogramImage/source_test/example.png').convert('L')
arr = np.array(img).astype('float32') / 255.0

# reshape to (1, H, W, 1) — adjust depending on the model's expected input shape
arr = arr[np.newaxis, ..., np.newaxis]

# Predict
pred = model.predict(arr)

# Convert prediction back to image and save
pred_img = (pred[0, ..., 0] * 255.0).clip(0, 255).astype('uint8')
Image.fromarray(pred_img).save('predicted_spectrogram.png')

Adjust shape manipulation above if your model expects a different input size or channel order; the notebooks show exact preprocessing used during training.

Model & training details

• Architecture: convolutional encoder-decoder (autoencoder) — several Conv2D + Downsampling layers in the encoder and mirrored Conv2DTranspose / Upsampling layers in the decoder.
• Loss: mean squared error (MSE) between predicted and ground-truth spectrogram images.
• Optimizer: Adam (commonly used; initial LR ~1e-3 or 1e-4 for fine-tuning).
• Metrics: visual inspection, PSNR, or structural similarity (SSIM) can be useful for evaluation.

Training tips

• Normalize input and target images to [0, 1] (float32) before training.
• Use data augmentation where helpful (small random crops, flips, time/frequency augmentations for audio-derived spectrograms).
• Start with small batches and reduce LR if the validation loss plateaus.

Tips and troubleshooting

• Out-of-memory (OOM) errors: lower batch_size or reduce image resolution.
• If predictions look noisy, try training longer, adding regularization, or using a lower learning rate.
• Ensure the image preprocessing during inference exactly matches the preprocessing in the notebooks (resizing, scaling, channel order).

Contributing

Contributions are welcome. Good first steps:

• Add requirements.txt listing exact versions used for reproducibility.
• Provide small sample audio files and expected spectrograms in a samples/ folder for quick testing.
• Add a small script or CLI wrapper around AudioToSpectrogram.py to make conversion easier.

License

This repository does not include a license file. If you plan to share or publish, add a LICENSE file describing the intended license.

---

If you'd like, I can:

• add a requirements.txt with pinned versions,
• add a small inference script predict.py that wraps the load/predict/save snippet,
• or update one of the notebooks to include a step-by-step quickstart using the included pretrained model.# [Project Name] – Audio-to-Spectrogram Anomaly Prediction

Deep Learning Pipeline for Detecting Anomalies in Audio Through Spectrogram Representations

This project converts raw audio signals into spectrogram images and applies deep-learning–based autoencoder models to detect anomalies through reconstruction error & Mahalanobis distance–based score evaluation. The system is designed for applications such as fault detection in machinery, environmental monitoring, equipment sound diagnostics, and pattern irregularity discovery in audio data.

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🚀 Key Features

• Audio → Spectrogram conversion using Short-Time Fourier Transform (STFT)
• Spectrogram preprocessing pipeline: grayscale conversion, resizing, normalization
• Autoencoder training pipeline to learn normal patterns
• Mahalanobis-based anomaly scoring for robust irregularity detection
• Visualization of anomaly clusters using KMeans
• 3-level severity classification: Normal, Medium anomaly, Severe anomaly
• Modular, extensible data and model structure

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🔄 Data Flow Pipeline

Raw Audio (.wav, .mp3)
        │
        ▼
Short-Time Fourier Transform (STFT)
        │
        ▼
Spectrogram Matrix → Image Conversion
        │
        ▼
Grayscale Normalized Spectrograms
        │                 │
        │                 └─► Test Data → Autoencoder Reconstruction → Error
        ▼
Autoencoder Training (Normal Patterns)
        │
        ▼
Latent Space Extraction → PCA → Mahalanobis Score
        │
        ▼
KMeans / Thresholding → 3-level Classification

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🧹 Preprocessing Steps

Step	Operation	Purpose
Load audio	librosa.load()	signal extraction
STFT	librosa.stft()	frequency-time representation
Convert to dB	librosa.amplitude_to_db()	visual clarity
Resize	cv2.resize()	model input spatial consistency
Normalize	img/255.0	training stability
Expand dims	(H,W,1)	channel format for CNN

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🧠 Model Architecture (Autoencoder)

Input → Conv2D → MaxPool → Conv2D → MaxPool → Latent Space
        ↓                                           ↓
   Conv2DTranspose ← Upsample ← Conv2DTranspose ← Output

• Loss function: Mean Squared Error (MSE)
• Optimizer: Adam, lr=1e-3
• Output: reconstructed spectrogram image

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📊 Anomaly Scoring Logic

Metric	Description	Notes
Reconstruction error	pixel-wise diff	unstable alone
Mahalanobis Distance	distance in latent PCA space	strong anomaly signal
PCA reduction	dimensionality stabilization	avoids covariance issues

Anomaly range example from dataset:

Normal: 3.7 – 9.0
Medium anomaly: 9.0 – 15.0
Severe anomaly: >15.0

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📈 Cluster Visualization

Cluster centers help determine anomaly separation:

Cluster 0 → Low (Normal)
Cluster 1 → Mid  (Medium Anomaly)
Cluster 2 → High (Severe Anomaly)

Visualized via 1D scatter of scores vs. cluster labels.

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🔎 Evaluation Example

Image: section_00_target_test_anomaly.png
Anomaly Score: 18.62
Classification: SEVERE ANOMALY.

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🧪 Usage Guide

Convert audio → spectrogram

python scripts/AudioToSpectrogram.py

Train model — via notebook

notebooks/train.ipynb

Run inference

notebooks/test.ipynb

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📌 Future Enhancements

• Add GRU-based audio domain prediction
• Integrate real-time anomaly alert system
• Add attention bottleneck for saliency mapping
• Deploy with FastAPI + stream inference

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🏷 Hashtags

#DeepLearning #AudioAnalysis #Spectrogram #AnomalyDetection #Mahalanobis #Autoencoder #MachineLearning #AudioProcessing

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🤝 Contributing

Contributions are welcome — submit PRs for model improvements, dataset scripts, visualization tools, or deployment setups.

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👨‍💻 Maintainer

Feel free to open issues for collaboration, debugging, or improvements.

File updated: README.md