Creating Sound Detection Applications using AI and Machine Learning

In this blog post, we’ll explore how to build a sound detection application from start to finish using Python, PyTorch, and Torchaudio. We’ll also touch on key concepts such as annotating data, preprocessing audio, training a deep learning model, deploying it for real-time inference, and optimizing for cost and speed.

Use Cases for Sound Detection

Sound detection models can be tailored to many use cases, including:

• Alarms, whistles, horns – Useful in industrial monitoring or public safety systems.
• Gunshot detection – Helps with real-time incident reporting in public areas.
• Vehicle sounds – Traffic monitoring or anomaly detection in autonomous driving systems.
• Baby sounds

Annotating Sound Data

Before you can train a model, you need a well-annotated dataset. Annotating audio manually is time-consuming, but there are tools that make the process easier. One such tool is Edyson, a simple yet powerful audio exploration and annotation tool.

Features of Edyson:

• Automated annotation based on similarity
• Define segment size: Length (in seconds) of audio snippets.
• Define step size: Offset between snippets for overlapping segments.

By using Edyson or similar tools, you can rapidly annotate large datasets, especially for multi-class classification or event-detection tasks.

Preprocessing Audio Data

Before feeding audio into a neural network, you should preprocess it to extract meaningful features. Raw waveforms can be difficult for models to learn from directly.

Key Preprocessing Steps

• Convert to Mono – Combine stereo channels into one.
• Normalize Volume – Bring all audio samples to the same loudness level.
• Compute Mel Spectrogram – A frequency-based visual representation suited for auditory tasks.

Building a Sound Detection Model with PyTorch

With preprocessed Mel spectrograms, you can now train a neural network. Convolutional Neural Networks (CNNs) are commonly used since spectrograms are 2D representations similar to images.

Use a DataLoader with torch.utils.data.Dataset for managing audio samples and their labels.

Deployment Options

Once your model is trained, deployment is the next step. There are a few typical options:

Local Deployment

You can run inference on local machines for quick response times. Ideal for offline or embedded use cases like surveillance systems or on-device audio monitoring.

Cloud Deployment

In cloud deployment, audio is streamed or uploaded, and inference is performed on scalable cloud infrastructure. Useful for centralized systems or heavy processing tasks.

Real-Time Prediction on Live Audio Streams

You can tap into live microphones or audio streams using libraries like sounddevice or pyaudio and apply your model in real-time.

Processing Audio from Video Files

You can extract and process audio from video files using moviepy or ffmpeg.

Once audio is extracted, you can process it like any other WAV file.

Optimizing Inference Time and Reducing Cost

Model optimization is crucial for real-time applications or edge deployment. Key techniques include:

Exporting to ONNX

ONNX (Open Neural Network Exchange) allows you to run models in optimized runtimes like ONNX Runtime, TensorRT, or OpenVINO.

Quantization and Pruning

You can also prune unused parameters or apply quantization to shrink model size and improve latency.

Evaluating Your Model

Use metrics such as:

• Accuracy: For classification tasks.
• Precision/Recall/F1: Especially important in imbalanced datasets.
• Confusion Matrix: Visualize per-class performance.

Wrapping Up

Building sound detection applications with machine learning is not only possible—it’s practical and powerful. From annotating datasets to deploying models on real-time streams, the modern PyTorch ecosystem makes the journey manageable.

Here’s a quick summary of what we covered:

Please do not hesitate to contact us in case you require any assistance with applying machine learning on audio to create innovative applications.