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A tutorial for Sound Source Localization researchers and practitioners. The purpose of this repo is to organize the world’s resources for Sound Source Localization, and make them universally accessible and useful.

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PublicationsToolsDatasetsLearning materials

Use this list with your AI agent

Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:

"Show me ssl+ resources from awesome-sound-source-localization"

Installation instructions →

What's inside

Datasets

Tools

  • Beamformer

    Implementation of the mask-based adaptive beamformer (MVDR, GEVD, MCWF).

  • Data format

    Format tranform between Kaldi, Numpy and Matlab.

  • Data simulation

    Add reverberation, noise or mix speaker.

  • GCC & GCC-Fbank

    Python code to extract features: GCC coefficients and GCCFB.

  • gpuRIR

    Python library for Room Impulse Response (RIR) simulation with GPU acceleration

  • LPS

    Extract log-power-spectrum/magnitude spectrum/log-magnitude spectrum/Cepstral mean and variance normalization.

Publications

Showing a sample of 47 resources. View the full list on GitHub →