So WTF is an Audio Embedding Model?

Hi there, everyone! This is my first blog post, and it's referencing a project I've been working on! It's a family of audio embedding models! I wanted to make this blogpost to explain what an audio embedding model is, and how it can be used.

What It Is

An audio embedding model is a type of model designed to turn audio data into a numerical, or vector value, known as an embedding. These embeddings capture important features in the audio, allowing other models to learn more efficiently.

How It Works

1. Spectrogram Input: The process starts with converting the audio signal into a spectrogram, a visual representation of the spectrum of frequencies in a sound signal as it varies with time.
2. Neural Network Processing: The spectrogram is then fed into a neural network. This network can be a convolutional neural network (CNN), recurrent neural network (RNN), or a transformer model. (Our model is a basic feed-forward MLP-like model)
3. Output Embedding: The neural network processes the spectrogram and outputs a fixed-size vector, often 1024 dimensions (we use a size of 1280) , which captures the most important information from the audio. It's like magic – an audio file is turned into a concise and informative value!

What Can Audio Embedding Model Be Used For

Audio embedding models have a large range of applications, like:

• Speech Recognition: Converting spoken language into text by understanding and processing the audio input.
• Music Recommendation: Analyzing and recommending music tracks based on audio features.
• Sound Classification: Identifying and categorizing different types of sounds, such as animal noises, musical instruments, or environmental sounds.
• Speaker Identification: Recognizing and verifying the identity of a speaker from their voice.
• Audio Search and Retrieval: Enabling efficient search through audio databases by comparing embeddings.