MIRaGe: Multichannel Database of Room Impulse Responses Measured on High-Resolution Cube-Shaped Grid

Cmejla, Jaroslav; Kounovsky, Tomas; Gannot, Sharon; Koldovsky, Zbynek; Tandeitnik, Pinchas

doi:10.23919/eusipco47968.2020.9287646

Cited by 9 publications

(8 citation statements)

References 12 publications

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“…In the following section, the experimental setup, the proposed VAE architecture, its training as well as the realization of the considered algorithmic variants is discussed. Datasets: To demonstrate the performance of the proposed method under real-world acoustic conditions, RTFs obtained from the MIRaGe dataset [27] The inputs are processed with three fully-connected layers and swish activation [30] followed by a linear layer postPar for the estimation of the parameters of the encoder distribution q φ (z|h). To enable backpropagation through the network, sampling realizes the 'reparameterization trick' [25]…”

Section: Methodsmentioning

confidence: 99%

Manifold learning-supported estimation of relative transfer functions for spatial filtering

Brendel¹,

Zeitler²,

Kellermann³

2021

Preprint

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Many spatial filtering algorithms used for voice capture in, e.g., teleconferencing applications, can benefit from or even rely on knowledge of Relative Transfer Functions (RTFs). Accordingly, many RTF estimators have been proposed which, however, suffer from performance degradation under acoustically adverse conditions or need prior knowledge on the properties of the interfering sources. While state-of-the-art RTF estimators ignore prior knowledge about the acoustic enclosure, audio signal processing algorithms for teleconferencing equipment are often operating in the same or at least a similar acoustic enclosure, e.g., a car or an office, such that training data can be collected. In this contribution, we use such data to train Variational Autoencoders (VAEs) in an unsupervised manner and apply the trained VAEs to enhance imprecise RTF estimates. Furthermore, a hybrid between classic RTF estimation and the trained VAE is investigated. Comprehensive experiments with real-world data confirm the efficacy for the proposed method.

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Section: Methodsmentioning

confidence: 99%

Manifold learning-supported estimation of relative transfer functions for spatial filtering

Brendel¹,

Zeitler²,

Kellermann³

2021

Preprint

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“…The experiment here is designed to provide an exhaustive test of the compared methods in challenging noisy situations where the target speaker is performing small movements within a confined area. Recordings are simulated using realworld room impulse responses (RIRs) taken from the MIRaGe database [45].…”

Section: A Simulated Roommentioning

confidence: 99%

Auxiliary Function-Based Algorithm for Blind Extraction of a Moving Speaker

Janský¹,

Koldovsky²,

Málek³

et al. 2020

Preprint

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Independent Vector Extraction (IVE) is a modification of Independent Vector Analysis (IVA) for Blind Source Extraction (BSE) to a setup in which only one source of interest (SOI) should be separated from a mixture of signals observed by microphones. The fundamental assumption is that the SOI is independent of the other signals. IVE shows reasonable results; however, its basic variant is limited to static sources. To extract a moving source, IVE has recently been extended by considering the Constant Separating Vector (CSV) mixing model. It enables us to estimate a separating filter that extracts the SOI from a wider spatial area through which the source has moved. However, only slow gradient-based algorithms were proposed in the pioneering papers on IVE and CSV. In this paper, we experimentally verify the applicability of the CSV mixing model and propose new IVE methods derived by modifying the auxiliary function-based algorithm for IVA. Piloted Variants are proposed as well for the methods with partially controllable global convergence. The methods are verified under reverberant and noisy conditions using model-based as well as real-world acoustic impulse responses. They are also verified within the CHiME-4 speech separation and recognition challenge. The experiments corroborate the applicability of the CSV mixing model for the blind moving source extraction as well as the improved convergence of the proposed algorithms.

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“…The BUT Speech@FIT Reverb Database (BUT Re-verbDB) [8] consists of IRs at 31 microphone positions from 2-11 loudspeaker positions in nine rooms. Recently, a dataset of IRs from loudspeakers at cube-shaped grid to linear microphone arrays has been developed [9]. These datasets are designed mainly for evaluating speech enhancement and distant speech recognition methods.…”

Section: Introductionmentioning

confidence: 99%

MESHRIR: A Dataset of Room Impulse Responses on Meshed Grid Points for Evaluating Sound Field Analysis and Synthesis Methods

Koyama¹,

Nishida²,

Kimura³

et al. 2021

2021 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)

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A new impulse response (IR) dataset called "MeshRIR" is introduced. Currently available datasets usually include IRs at an array of microphones from several source positions under various room conditions, which are basically designed for evaluating speech enhancement and distant speech recognition methods. On the other hand, methods of estimating or controlling spatial sound fields have been extensively investigated in recent years; however, the current IR datasets are not applicable to validating and comparing these methods because of the low spatial resolution of measurement points. MeshRIR consists of IRs measured at positions obtained by finely discretizing a spatial region. Two subdatasets are currently available: one consists of IRs in a three-dimensional cuboidal region from a single source, and the other consists of IRs in a two-dimensional square region from an array of 32 sources. Therefore, MeshRIR is suitable for evaluating sound field analysis and synthesis methods. This dataset is freely available at https: //sh01k.github.io/MeshRIR/ with some codes of sample applications.

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MIRaGe: Multichannel Database of Room Impulse Responses Measured on High-Resolution Cube-Shaped Grid

Cited by 9 publications

References 12 publications

Manifold learning-supported estimation of relative transfer functions for spatial filtering

Manifold learning-supported estimation of relative transfer functions for spatial filtering

Auxiliary Function-Based Algorithm for Blind Extraction of a Moving Speaker

MESHRIR: A Dataset of Room Impulse Responses on Meshed Grid Points for Evaluating Sound Field Analysis and Synthesis Methods

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