A Real-Time Implementation of a 3D Binaural System based on HRIRs Interpolation

Bruschi, Valeria; Nobili, Stefano; Cecchi, Stefania

doi:10.1109/ispa52656.2021.9552125

Cited by 5 publications

(6 citation statements)

References 4 publications

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“…P logic = P dynamic × number of logics, P register = P dynamic × number of registers (3) Figure 11a shows the time-to-power analysis results. With the power consumption measured by a power analyser, all of the power distribution was determined.…”

Section: Even-odd Buffermentioning

confidence: 99%

“…The digital ANC module has gained significant importance as a key component of audio DSP circuits due to its robustness, low-cost implementation, and adaptability to dynamic environments [2]. Recently, as real-time convolution methodology develops and virtual reality becomes more popular, many audio system applications have been made, including 3D binaural reproduction, which is a method to obtain a desired stereo sound effect by directly controlling the sound in the listener's ears [3]. It is possible to create a 3D acoustic environment that does not exist in the real world.…”

Section: Introductionmentioning

confidence: 99%

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Low-Power FPGA Realization of Lightweight Active Noise Cancellation with CNN Noise Classification

Park

2023

Electronics

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Active noise cancellation (ANC) is the most important function in an audio device because it removes unwanted ambient noise. As many audio devices are increasingly equipped with digital signal processing (DSP) circuits, the need for low-power and high-performance processors has arisen because of hardware resource restrictions. Low-power design is essential because wireless audio devices have limited batteries. Noise cancellers process the noise in real time, but they have a short secondary path delay in conventional least mean square (LMS) algorithms, which makes implementing high-quality ANC difficult. To solve these problems, we propose a fixed-filter noise cancelling system with a convolutional neural network (CNN) classification algorithm to accommodate short secondary path delay and reduce the noise ratio. The signal-to-noise ratio (SNR) improved by 2.3 dB with CNN noise cancellation compared to the adaptive LMS algorithm. A frequency-domain noise classification and coefficient selection algorithm is introduced to cancel the noise for time-varying systems. Additionally, our proposed ANC architecture includes an even–odd buffer that efficiently computes the fast Fourier transform (FFT) and overlap-save (OLS) convolution. The simulation results demonstrate that the proposed even–odd buffer reduces processing time by 20.3% and dynamic power consumption by 53% compared to the single buffer.

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Section: Even-odd Buffermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Low-Power FPGA Realization of Lightweight Active Noise Cancellation with CNN Noise Classification

Park

2023

Electronics

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“…Also, in the interpolation or extrapolation of BRIRs, considering all the details of the early reflection pattern is challenging. Therefore often simplifications are applied (Bruschi et al, 2020; Brinkmann et al, 2020; Müller & Zotter, 2020). For this reason, it is crucial to understand which level of detail is required to provide a room acoustic impression without perceptual discrepancies.…”

Section: Required Accuracy Of Physical Propertiesmentioning

confidence: 99%

“…Besides, for example, semantic and visual information can be used to estimate acoustic properties (Kim et al, 2017, 2020). BRIR synthesis can be realized either by pure simulation, for example, based on ray-tracing (Savioja & Svensson, 2015; Brinkmann et al, 2019), wave-based simulation approaches or delay networks (Alary et al, 2019; Välimäki et al, 2012) or by manipulation of measured impulse responses, like interpolation (Bruschi et al, 2020; Brinkmann et al, 2020), extrapolation (Neidhardt et al, 2018; Sloma et al, 2019; Coleman et al, 2017; Pörschmann et al, 2017) or shaping of the late reverberation tail (Jot & Lee, 2016; Pörschmann & Zebisch, 2012; Arend et al, 2021). Systems that do not rely on a priori knowledge are desired because their use is not limited to rooms or environments for which the predetermined information is available.…”

Section: Basic Technical System For Auditory Augmented Realitymentioning

confidence: 99%

Perceptual Matching of Room Acoustics for Auditory Augmented Reality in Small Rooms - Literature Review and Theoretical Framework

2022

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For the realization of auditory augmented reality (AAR), it is important that the room acoustical properties of the virtual elements are perceived in agreement with the acoustics of the actual environment. This perceptual matching of room acoustics is the subject reviewed in this paper. Realizations of AAR that fulfill the listeners’ expectations were achieved based on pre-characterization of the room acoustics, for example, by measuring acoustic impulse responses or creating detailed room models for acoustic simulations. For future applications, the goal is to realize an online adaptation in (close to) real-time. Perfect physical matching is hard to achieve with these practical constraints. For this reason, an understanding of the essential psychoacoustic cues is of interest and will help to explore options for simplifications. This paper reviews a broad selection of previous studies and derives a theoretical framework to examine possibilities for psychoacoustical optimization of room acoustical matching.

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“…One way of achieving physically more correct interpolation is to first decompose the early SRIR into localized sound events, corresponding to direct sound and early reflections, using ToAs and DoAs estimated from existing parametric SRIR processing methods [1,2,3]. Interpolation then requires finding appropriate mappings between corresponding sound events [4,5,6,7]. In [8,9], dynamic time warping is used for simplifying this task.…”

Section: Introductionmentioning

confidence: 99%

Interpolation of Spatial Room Impulse Responses Using Partial Optimal Transport

Geldert

Meyer-Kahlen

Schlecht

2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Interpolation between spatial room impulse responses (SRIRs) is necessary for dynamic acoustic rendering in which a listener can move with six degrees-of-freedom. The early part of the SRIR consists of sparse direct and reflected sound events, whose arrival time, direction and level vary with receiver position. Interpolation of the spatio-temporal structure necessitates the non-trivial task of mapping corresponding sound events. Instead of finding an exact map, we propose using partial optimal transport to find a coupling between reflections requiring neither estimation of the room geometry nor explicit knowledge of the source-receiver configuration. Each SRIR is first decomposed into a virtual source space. Then, the interpolated impulse response is calculated based on a partial optimal transport coupling obtained with linear programming. We compare the method against two baseline interpolation methods using simulated SRIR data, and show that it best preserves the temporal fine structure of the omnidirectional response.

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A Real-Time Implementation of a 3D Binaural System based on HRIRs Interpolation

Cited by 5 publications

References 4 publications

Low-Power FPGA Realization of Lightweight Active Noise Cancellation with CNN Noise Classification

Low-Power FPGA Realization of Lightweight Active Noise Cancellation with CNN Noise Classification

Perceptual Matching of Room Acoustics for Auditory Augmented Reality in Small Rooms - Literature Review and Theoretical Framework

Interpolation of Spatial Room Impulse Responses Using Partial Optimal Transport

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