Sound field separation in a mixed acoustic environment using a sparse array of higher order spherical microphones

Fahim, Abdullah; Samarasinghe, Prasanga N.; Abhayapala, Thushara D.

doi:10.1109/hscma.2017.7895580

Cited by 22 publications

(27 citation statements)

References 15 publications

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“…The performance of the proposed algorithm can be enhanced by introducing additional circles of microphones to extract higher order even harmonics modes. A similar concept can be useful in reducing required number of microphones and simplifying the design structure of other sound processing techniques that use distributed higher order microphones [23], [24].…”

Section: Discussionmentioning

confidence: 99%

A Planar Microphone Array for Spatial Coherence-Based Source Separation

Fahim

Samarasinghe

Abhayapala

et al. 2018

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP)

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We proposed a spatial coherence-based PSD estimation and source separation technique in [1] using a 32channel spherical microphone array. While the proposed spherical microphone-based method exhibited a satisfactory performance in separating multiple sound sources in a reverberant environment, the use of a large number of microphones remains an issue for some practical considerations. In this paper, we investigate an alternative array structure to achieve spatial coherence-based source separation using a planar microphone array. This method is particularly useful in separating a limited number of sound sources in a mixed acoustic scene. The simplified array structure we used here can easily be integrated with many commercial acoustical instruments such as smart home devices to achieve better speech enhancements.

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Section: Discussionmentioning

confidence: 99%

A Planar Microphone Array for Spatial Coherence-Based Source Separation

Fahim

Samarasinghe

Abhayapala

et al. 2018

2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP)

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“…Hereafter, we abbreviate (h, /) as a single symbol H to simplify the notation. The primary noise field P(k, R, H) on the sphere S 2 consists of two parts: the primary outgoing field P O (k, R, H) and the primary incoming field P I (k, R, H) 9,14 Pðk; R; HÞ ¼ (…”

Section: Problem Formulationmentioning

confidence: 99%

“…1 We formulate the problem of tonal noise field control in a room as estimating and canceling the outgoing field produced by the primary source. 14 We reduce the primary outgoing field by superposing it with the secondary outgoing field. As the secondary outgoing field is independent of room reverberations, the proposed system removes the need for real-time room reverberation characteristics identification, i.e., online secondary path estimation.…”

Section: Introductionmentioning

confidence: 99%

Active control of outgoing noise fields in rooms

Zhang

Abhayapala

2018

The Journal of the Acoustical Society of America

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Current active noise control systems can cancel noises in a duct effectively. However, they are insufficient for suppressing complex noise fields in time-varying rooms. This paper develops an active noise control system that can cancel tonal noise fields produced by a primary source in a room. The problem of tonal noise field control is formulated as estimating and canceling the outgoing field on a sphere surrounding the primary source. The proposed system limits the energy of the primary source radiating out of the sphere, thereby creating a global quiet zone inside the room. In addition, it removes the need for online secondary path estimation with reduced influence on desired sound fields in the room. A method for estimating the outgoing field on a sphere is presented, together with a wave-domain algorithm for controlling the outgoing field. Simulations and hardware demonstrations show the proposed system can reduce tonal noise fields in a room and over a wide frequency range.

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“…On the other hand, non-parametric methods [ 16 , 17 , 18 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ] aim to numerically estimate the acoustic field. In this category, the sound field is typically modelled as a linear combination of solutions of the wave equation [ 27 ], e.g., plane wave [ 25 , 26 ] or spherical wave [ 22 , 23 , 24 , 28 , 29 ] expansions. Alternatively, RIRs are expressed through their modal expansion [ 16 , 17 ] or exploiting the equivalent source method (ESM) [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

Deep Prior Approach for Room Impulse Response Reconstruction

Pezzoli

Perini

Bernardini

et al. 2022

Sensors

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In this paper, we propose a data-driven approach for the reconstruction of unknown room impulse responses (RIRs) based on the deep prior paradigm. We formulate RIR reconstruction as an inverse problem. More specifically, a convolutional neural network (CNN) is employed prior, in order to obtain a regularized solution to the RIR reconstruction problem for uniform linear arrays. This approach allows us to avoid assumptions on sound wave propagation, acoustic environment, or measuring setting made in state-of-the-art RIR reconstruction algorithms. Moreover, differently from classical deep learning solutions in the literature, the deep prior approach employs a per-element training. Therefore, the proposed method does not require training data sets, and it can be applied to RIRs independently from available data or environments. Results on simulated data demonstrate that the proposed technique is able to provide accurate results in a wide range of scenarios, including variable direction of arrival of the source, room T60, and SNR at the sensors. The devised technique is also applied to real measurements, resulting in accurate RIR reconstruction and robustness to noise compared to state-of-the-art solutions.

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Sound field separation in a mixed acoustic environment using a sparse array of higher order spherical microphones

Cited by 22 publications

References 15 publications

A Planar Microphone Array for Spatial Coherence-Based Source Separation

A Planar Microphone Array for Spatial Coherence-Based Source Separation

Active control of outgoing noise fields in rooms

Deep Prior Approach for Room Impulse Response Reconstruction

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