Active listening room compensation for massive multichannel sound reproduction systems using wave-domain adaptive filtering

Spors, Sascha; Buchner, Herbert; Rabenstein, Rudolf; Herbordt, Wolfgang

doi:10.1121/1.2737669

Cited by 84 publications

(63 citation statements)

References 32 publications

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“…Adaptive methods have been applied to Wave Field Synthesis, as is discussed in the works, among others, of S. Spors et al [Spo06], [SBRH07] and P.A. Gauthier et al…”

Section: Wave Field Synthesismentioning

confidence: 99%

Sound Field Reproduction

Kim

Choi

2013

Sound Visualization and Manipulation

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“…Adaptive methods have been applied to Wave Field Synthesis, as is discussed in the works, among others, of S. Spors et al [Spo06], [SBRH07] and P.A. Gauthier et al…”

Section: Wave Field Synthesismentioning

confidence: 99%

Sound Field Reproduction

Kim

Choi

2013

Sound Visualization and Manipulation

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“…For room equalization, a brief description of the filtered-x scheme is given for both single-channel and multichannel AE system in [7]. Also for multichannel AE systems, a decoupled version of the filtered-x LMS is presented in [11], and a wave-domain algorithm in [13].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Filtered-x Algorithms for Room Equalization Based on Block-Based Combination Schemes

Fúster

Diego

Azpicueta-Ruiz

et al. 2016

IEEE/ACM Trans. Audio Speech Lang. Process.

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Abstract-Room equalization has become essential for sound reproduction systems to provide the listener with the desired acoustical sensation. Recently, adaptive filters have been proposed as an effective tool in the core of these systems. In this context, this paper introduces different novel schemes based on the combination of adaptive filters idea: a versatile and flexible approach that permits obtaining adaptive schemes combining the capabilities of several independent adaptive filters.In this way, we have investigated the advantages of a scheme called combination of block-based adaptive filters which allows a blockwise combination splitting the adaptive filters into nonoverlapping blocks. This idea was previously applied to the plant identification problem, but has to be properly modified to obtain a suitable behavior in the equalization application. Moreover, we propose a scheme with the aim of further improving the equalization performance using the a priori knowledge of the energy distribution of the optimal inverse filter, where the block filters are chosen to fit with the coefficients energy distribution. Furthermore, the biased block-based filter is also introduced as a particular case of the combination scheme, especially suited for low signal-to-noise ratios (SNRs) or sparse scenarios. Although the combined schemes can be employed with any kind of adaptive filter, we employ the filtered-x improved proportionate normalized least mean square (Fx-IPNLMS) algorithm as basis of the proposed algorithms, allowing to introduce a novel combination scheme based on partitioned block schemes where different blocks of the adaptive filter use different parameter settings. Several experiments are included to evaluate the proposed algorithms in terms of convergence speed and steady-state behavior for different degrees of sparseness and SNRs.

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“…It consists in carrying out simultaneously many convolutions of different audio channels. This provides a multichannel convolution that allows to achieve with different filters well known acoustic effects like: 3D spatial sound, crosstalk cancellation, room compensation [1], loudspeakers equalization, etc. [2].…”

Section: Introductionmentioning

confidence: 99%

Real-time massive convolution for audio applications on GPU

et al. 2011

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Massive convolution is the basic operation in multichannel acoustic signal processing. This field has experienced a major development in recent years. One reason for this has been the increase in the number of sound sources used in playback applications available to users. Another reason is the growing need to incorporate new effects and to improve the hearing experience. Massive convolution requires high computing capacity. GPUs offer the possibility of parallelizing these operations. This allows us to obtain the processing result in much shorter time and to free up CPU resources. One important aspect lies in the possibility of overlapping the transfer of data from CPU to GPU and vice versa with the computation, in order to carry out real-time applications. Thus, a synthesis of 3D sound scenes could be achieved with only a peer-to-peer music streaming environment using a simple GPU in your computer, while the CPU in the computer is being used for other tasks. Nowadays, these effects are obtained in theaters or funfairs at a very high cost, requiring a large quantity of resources. Thus, our work focuses on two mains points: to describe an efficient massive convolution implementation and to incorporate this task to real-time multichannel-sound applications.

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Active listening room compensation for massive multichannel sound reproduction systems using wave-domain adaptive filtering

Cited by 84 publications

References 32 publications

Sound Field Reproduction

Sound Field Reproduction

Adaptive Filtered-x Algorithms for Room Equalization Based on Block-Based Combination Schemes

Real-time massive convolution for audio applications on GPU

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