Enhancing speech quality has become increasingly important in today’s world, especially with the prevalence of noisy environments. This blog post will guide you through building a speech enhancement system that effectively attenuates environmental noise using deep learning techniques.

Introduction

This project aims to create a speech enhancement system that improves the clarity of voices by reducing background noise using deep learning methods. By understanding and applying techniques from speech processing, we can drastically improve audio clarity, making it easier to discern what’s being said even in challenging conditions.

Understanding the Spectrum of Sound

Imagine sound waves are like the different colors in a rainbow. When we talk, our voice creates a unique pattern, similar to how light creates a specific hue. To represent these sound patterns effectively, we use a technique called Spectrograms, which are visual representations that plot the strength of various frequency components over time. Just like colors in a picture help us understand the image, Spectrograms help us decipher audio quality.

For our speech enhancement system, we utilize magnitude Spectrograms that highlight the structure of the signal needed for noise reduction. Our ultimate goal is to subtract noise from the signal, creating a clearer audio output.

Preparing the Data

Building an effective enhancement system requires high-quality data. Here’s how you can prepare your datasets:

• Gather clean speech samples and environmental noise from various sources like LibriSpeech and SiSec.
• Focus on environmental noises including foot steps, alarms, fireworks, and more. This diversity helps in building a robust model.
• Sample audio files at 8kHz and create directories for both training and testing datasets. Consider structuring your files as depicted below:

data/
    ├── dataTrain/
    │   ├── noise/
    │   └── voices/
    └── dataTest/
        ├── noise/
        └── voices/

Running the Data Creation

With your data organized, you can begin the creation process:

1. Modify paths in the args.py file to match your folder structure.
2. Set the number of samples you want using the nb_samples variable (default is 50; for production, consider using 40,000 or more).
3. Run the command: python main.py --mode=data_creation. This will blend voices and noise, creating the necessary spectrograms.

Training the Model

For our model training, we employ a U-Net architecture, which is a type of convolutional autoencoder designed specifically for denoising tasks. Think of it as a master painter that learns to recreate a colorful masterpiece from a crude sketch.

Follow these steps to train your model:

• Load your training data by setting parameters in args.py.
• Run your training using python main.py --mode=training. Note that you can start from scratch or use pre-trained weights.
• Monitor the training graph and make adjustments as necessary. The ideal loss should keep decreasing, indicating improved performance.

Making Predictions

Once your model is trained, you can use it to denoise audio:

• Convert noisy voice audios into Spectrograms.
• Pass the Spectrograms through your U-Net model to predict the noise model.
• Subtract the predicted noise model from the noisy voice to produce a cleaner output.

The time required for this prediction process is relatively fast, averaging around 80 ms per window when processed via CPU.

Troubleshooting Tips

While setting up your speech enhancement system, you may run into some common issues:

• If you encounter errors regarding missing dependencies, ensure you’ve run pip install -r requirements.txt to install all necessary packages.
• For any discrepancies with the data format, double-check your data directory structure against the recommended layout.
• Adjusting training parameters such as batch_size and epochs might help in reducing training time and improving model accuracy.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

By following this guide, you can build a speech enhancement system that effectively reduces environmental noise, significantly improving audio clarity. The intricacies of audio processing may seem daunting initially, but with patience and practice, you will navigate through these concepts with ease.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.