Spatial accuracy of binaural synthesis from rigid spherical microphone array recordings

Salvador, César D.; Sakamoto, Shuichi; Treviño, Jorge; Suzuki, Yôiti

doi:10.1250/ast.38.23

Cited by 7 publications

(7 citation statements)

References 32 publications

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“…These methods are categorized into the HRTF modeling approach and the microphone signal modeling approach, as mentioned in [6]. So-called binaural Ambisonics is categorized in the latter category, examples of which can be found in [5,[7][8][9][10][11][12][13]. Audio output signals radiated from loudspeakers in a spherical array can be directly synthesized with HRTFs.…”

Section: Previous Workmentioning

confidence: 99%

Binaural Ambisonics: Its optimization and applications for auralization

Otani

Shigetani

Mitsuishi

et al. 2020

Acoust. Sci. & Tech.

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To better understand acoustic environment and the resulting auditory perception, it is essential to capture, analyze, and reproduce a sound field as a three-dimensional physical phenomenon because spatial aspects of auditory perception play important roles in various situations in our lives. Some approaches have been proposed to achieve the three-dimensional capture and reproduction of acoustic fields. Among them, Higher-Order Ambisonics (HOA) based on spherical harmonics expansion enables the capture and reproduction of a directivity pattern of incoming sound waves. On the basis of HOA, three-dimensional auditory space can be presented to a listener typically via a spherical loudspeaker array. In addition, binaural synthesis emulating the loudspeaker presentation enables HOA reproduction with a set of headphones or several loudspeakers by employing crosstalk cancellation. Thus, we are developing an HOA-based binaural reproduction/auralization system with head tracking. This system is aimed at realizing the reproduction and auralization of a sound field, including one excited by the listener's own voice. In this paper, we review the topics related to the reproduction and auralization of the sound field and introduce the HOA-based binaural synthesis system we have developed, as well as our works on sweet-spot expansion in HOA decoding and selfvoice reproduction/auralization.

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Section: Previous Workmentioning

confidence: 99%

Binaural Ambisonics: Its optimization and applications for auralization

Otani

Shigetani

Mitsuishi

et al. 2020

Acoust. Sci. & Tech.

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“…The entries of the associated matrix are acoustic transfer functions from the positions involved in the HRTF dataset to the positions of microphones. Examples of binaural systems based on this approach are the binaural system obtained when combining the BPLIC [30] and ADVISE systems [31,32], and the binaural ambisonic systems treated in [33,46,48,50,51,54,55].…”

Section: Binaural Synthesis From Microphone Array Recordings and Hrtfmentioning

confidence: 99%

“…These modal representations enjoy popularity because they provide an encoding and decoding scheme of directional sound information at scalable resolutions [33,34,[45][46][47][48][49][50][51][52][53][54][55]. A directional resolution at a specific scale can be associated with a single parameter that is called the order, which is denoted by # in this section.…”

Section: Combination Matrices For Spherical Arraysmentioning

confidence: 99%

“…The first method relies on a diagonalization of C HRTF and is known as binaural modal beamforming [34,52,53]. The second method relies on a diagonalization of C mic and is known as binaural ambisonics [33,48,50]. In both cases, the pseudo-inverses obtained by analytic diagonalization can be formulated as follows:…”

Section: Combination Matrices For Spherical Arraysmentioning

confidence: 99%

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Design theory for binaural synthesis: Combining microphone array recordings and head-related transfer function datasets

Salvador

Sakamoto

Treviño

et al. 2017

Acoust. Sci. & Tech.

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Signal processing methods that accurately synthesize sound pressure at the ears are important in the development of spatial audio devices for personal use. This paper reviews the current methods and focuses on a promising class of these methods that rely on combining the spatial information available in microphone array recordings and datasets of head-related transfer functions (HRTFs). These two kinds of spatial information enable the consideration of dynamic and individual auditory localization cues during binaural synthesis. A general formulation for such a class of methods is presented in terms of a linear system of equations, whose associated matrix is composed of acoustic transfer functions that relate the positions of microphones and HRTFs. Based on this formulation, it is shown that most of the existing methods under consideration can be classified into two prominent approaches: 1) the HRTF modeling approach and 2) the microphone signal modeling approach. An important relation between these two approaches is evidenced in the general formulation: when one approach arises from the solution to an overdetermined system, the other corresponds to an underdetermined system, and vice versa. Illustrative examples of binaural synthesis from spherical arrays are provided by means of simulations. Underdetermined systems generally achieve better performance than overdetermined ones.

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“…However, hardware and feasibility restrictions limit the § This research is supported by an Australian Government Research Training Program (RTP) Scholarship, and Facebook Reality Labs. continuous space that can be recorded, and as a result, during reproduction the listener is usually stuck in the fixed acoustic perspective of the microphone [7].…”

Section: Introductionmentioning

confidence: 99%

Sound Field Translation and Mixed Source Model for Virtual Applications with Perceptual Validation

Birnie,

Abhayapala,

Tourbabin

et al. 2020

Preprint

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Non-interactive and linear experiences like cinema film offer high quality surround sound audio to enhance immersion, however the listener's experience is usually fixed to a single acoustic perspective. With the rise of virtual reality, there is a demand for recording and recreating real-world experiences in a way that allows for the user to interact and move within the reproduction. Conventional sound field translation techniques take a recording and expand it into an equivalent environment of virtual sources. However, the finite sampling of a commercial higher order microphone produces an acoustic sweet-spot in the virtual reproduction. As a result, the technique remains to restrict the listener's navigable region. In this paper, we propose a method for listener translation in an acoustic reproduction that incorporates a mixture of near-field and far-field sources in a sparsely expanded virtual environment. We perceptually validate the method through a Multiple Stimulus with Hidden Reference and Anchor (MUSHRA) experiment. Compared to the planewave benchmark, the proposed method offers both improved source localizability and robustness to spectral distortions at translated positions. A cross-examination with numerical simulations demonstrated that the sparse expansion relaxes the inherent sweet-spot constraint, leading to the improved localizability for sparse environments. Additionally, the proposed method is seen to better reproduce the intensity and binaural room impulse response spectra of near-field environments, further supporting the strong perceptual results.

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Spatial accuracy of binaural synthesis from rigid spherical microphone array recordings

Cited by 7 publications

References 32 publications

Binaural Ambisonics: Its optimization and applications for auralization

Binaural Ambisonics: Its optimization and applications for auralization

Design theory for binaural synthesis: Combining microphone array recordings and head-related transfer function datasets

Sound Field Translation and Mixed Source Model for Virtual Applications with Perceptual Validation

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