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sma_processing's Introduction

Spherical Microphone Array Processing Toolbox

Summary

  • This repository contains a library for some of the features commonly used in spherical array microphone processing.
  • These features are mostly used for direction-of-arrival (DOA) and six-degrees of freedom (6DoF) problems
  • It is easy to add new features, datasets, microphones.
  • This repository consists of two main interfaces: features as library and main script as feature extractor using config files.
  • main.py is for extracting batch features from emulations of selected anechoic/music files on SMIR dataset over different positions and rooms.
  • Batch extraction can easily be done for readily-prepared classes for datasets and microphones using integrated configuration system via hydra.

Supported

Features

SMIR datasets

Microphones

How to use

python main.py --help

Tasks

  • Add support for real SMA recordings
  • Fix fimin/fimax passing everywhere and discard empty npy portions
  • Build a pipeline system
  • Implement analyse functions
  • wandb integration for analyse?
  • Integrate room simulations

References

  • B. Rafaely, Fundamentals of Spherical Array Processing, vol. 8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. doi: 10.1007/978-3-662-45664-4.
  • O. Nadiri and B. Rafaely, "Localization of Multiple Speakers under High Reverberation using a Spherical Microphone Array and the Direct-Path Dominance Test," in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 22, no. 10, pp. 1494-1505, Oct. 2014, doi: 10.1109/TASLP.2014.2337846.
  • O. Olgun, H. Hacihabiboglu, "Data-driven Threshold Selection for Direct Path Dominance Test, Proceedings of the 23rd International Congress on Acoustics, 2019, pp. 3313–3320.
  • M. B. Coteli and H. Hacihabiboglu, “Sparse Representations With Legendre Kernels for DOA Estimation and Acoustic Source Separation,” IEEE/ACM Trans. Audio Speech Lang. Process., vol. 29, pp. 2296–2309, 2021, doi: 10.1109/TASLP.2021.3091845.

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