Source Separation and Reconstruction of Spatial Audio Using Spectrogram Factorization

Nikunen, Joonas; Virtanen, Tuomas

doi:10.1002/9781119252634.ch9

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Cited by 3 publications

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“…The time resolution and frequency resolution are constrained by the Heisenberg uncertainty principle. The length of the window determines the time resolution and frequency resolution of the spectrogram [7].…”

Section: Signal Modelmentioning

confidence: 99%

Modulation scheme recognition using convolutional neural network

Zhang

2019

J. eng.

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Section: Signal Modelmentioning

confidence: 99%

Modulation scheme recognition using convolutional neural network

Zhang

2019

J. eng.

View full text Add to dashboard Cite

Modulation Recognition using Wavelet-assisted Convolutional Neural Network

Zhang

2018

2018 International Conference on Advanced Technologies for Communications (ATC)

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An efficient parallel kernel based on Cholesky decomposition to accelerate Multichannel Non-Negative Matrix Factorization

Muñoz-Montoro

Carabias-Orti

Salvati

et al. 2022

Preprint

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Multichannel Source Separation has been a popular topic, and recently proposed methods based on the local Gaussian model (LGM) have provided promising result despite its high computational cost when several sensors are used. The main reason being due to inversion of a spatial covariance matrix, with a complexity of \(O(I^3)\), being \(I\) the number of sensors. This drawback limits the practical application of this approach for tasks such as sound field reconstruction or virtual reality, among others. In this paper, we present a numerical approach to reduce the complexity of the Multichannel NMF to address the task of audio source separation for scenarios with a high number of sensors such as High Order Ambisonics (HOA) encoding. In particular, we propose a parallel multi-architecture driver to compute the multiplicative update rules in MNMF approaches. The proposed driver has been designed to work on both sequential and multi-core computers, as well as Graphics Processing Units (GPUs) and Intel Xeon coprocessors. The proposed software was written in C language and can be called from numerical computing environments. The proposed solution tries to reduce the computational cost of the multiplicative update rules by using the Cholesky decomposition and by solving several triangular equation systems.The proposal has been evaluated for different scenarios with promising results in terms of execution times for both CPU and GPU. To the best of our knowledge, our proposal is the first system that addresses the problem of reducing the computational cost of full-rank MNMF-based systems using parallel and high performance techniques.

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Source Separation and Reconstruction of Spatial Audio Using Spectrogram Factorization

Cited by 3 publications

References 40 publications

Modulation scheme recognition using convolutional neural network

Modulation scheme recognition using convolutional neural network

Modulation Recognition using Wavelet-assisted Convolutional Neural Network

An efficient parallel kernel based on Cholesky decomposition to accelerate Multichannel Non-Negative Matrix Factorization

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