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 🚀 **SSLAM** is a self-supervised learning framework designed to enhance audio representation quality for both **polyphonic(multiple overlapping sounds)** and monophonic soundscapes. Unlike traditional SSL models that focus on monophonic data, SSLAM introduces a novel **source retention loss** and **audio mixture training**, significantly improving performance on real-world polyphonic audio.
 
-🔗  **[Github](https://github.com/ta012/SSLAM) | [Paper](https://openreview.net/pdf?id=odU59TxdiB) | [Open Review](https://openreview.net/forum?id=odU59TxdiB) | [Pre-trained Models(HuggingFace)](https://huggingface.co/ta012/SSLAM) | [Pre-trained Models(Google Drive)](https://drive.google.com/drive/folders/1G0icv-hdqDEqnfP4EFszMXhFnWWM09gT?usp=sharing) **
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+🔗  **[Github](https://github.com/ta012/SSLAM) | [Paper](https://openreview.net/pdf?id=odU59TxdiB) | [Open Review](https://openreview.net/forum?id=odU59TxdiB) |[🤗 Models](https://huggingface.co/ta012/SSLAM) | [Models(Google Drive)](https://drive.google.com/drive/folders/1G0icv-hdqDEqnfP4EFszMXhFnWWM09gT?usp=sharing)**
 
 # 📋 Table of Contents
 - [Why SSLAM?](#why-sslam)