On Building Immersive Audio Applications Using Robust Adaptive Beamforming and Joint Audio-Video Source Localization

Beracoechea, Jon Ander; Torres-Guijarro, Soledad; Garcia, Lino; Casajús-Quirós, F.J.

doi:10.1155/asp/2006/40960

Cited by 8 publications

(5 citation statements)

References 23 publications

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“…We used Xilinx XPower to analyze the complete system power consumption, which is shown in the last column of Table 3. In contrast, high-end CPUs that are utilized in similar approaches like [3] and [4], when not in idle mode, normally require tens of Watts, which in essence is an order or two of magnitude difference in favor of our design. Moreover, our design approaches the power figures of DSPs, which are typically designed as low power devices.…”

Section: Resultsmentioning

confidence: 99%

“…Related work: In [3] the authors present a 3D-audio system oriented to future communication applications. It consists of 12 linearly placed microphones connected to an NI-4772 VXI board acquisition hardware board mounted on a standard PC.…”

Section: Background Related Work and Problem Statementmentioning

confidence: 99%

“…Related work on audio systems that exploit the beamforming techniques, reveals that many systems are implemented using desktop PCs [3], [4]. However, such approaches, when adaptive beamforming is applied to extract multiple audio sources, introduce processing bottlenecks because the algorithm may not converge fast enough [5].…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to PC-based related work ( [3], [4]) that requires in the order of 100 Watt power, our prototype, requires approximately 2 Watts, according to Xilinx XPower; • Improvements over related work: We compared our prototype with an OMP-annotated C software implementation running on a Pentium D at 3.4 GHz. Experimental results suggest an application speedup of up to 28 compared to the software implementation.…”

Section: Introductionmentioning

confidence: 99%

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A reconfigurable beamformer for audio applications

Theodoropoulos

Kuzmanov

Gaydadjiev

2009

2009 IEEE 7th Symposium on Application Specific Processors

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Beamforming is a signal processing technique that improves the signal strength received from a specific location. It is already used for many decades in telecommunications, while over the last years, it has been adopted by the audio research society, mostly to enhance speech recognition. In this paper, we propose a scalable organization for a hardware time-invariant beamformer that can be used in small handheld devices and complete 3D-audio systems. Our design can be configured according to the number of input channels. Furthermore, all critical internal modules, such as decimators, FIR filters and interpolators can be adjusted to support various input sampling rates. We developed a hardware prototype in VHDL targeting the Xilinx ML410 board incorporating Virtex4 FX60 FPGA. Following a constrained approach regarding FPGA resource utilization, our hardware prototype occupies 21% of the aforementioned FPGA when instantiating 16 beamforming modules, and consumes approximately 2 Watts of power. Furthermore, our design achieves a speedup of 28 compared to an OMPannotated software implementation running on a Pentium D at 3.4 GHz. We also compared our design against prior related work. Results suggest that it can extract an audio source up to 11 times faster compared to a reconfigurable adaptive beamformer, and up to 19 times faster compared to DSP implementations.

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Section: Resultsmentioning

confidence: 99%

Section: Background Related Work and Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A reconfigurable beamformer for audio applications

Theodoropoulos

Kuzmanov

Gaydadjiev

2009

2009 IEEE 7th Symposium on Application Specific Processors

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“…In Beracoechea et al [2006], the authors describe an immersive audio system that consists of 12 linearly placed microphones. The sound source is tracked through audio and video tracking algorithms, while the beamformer is steered accordingly.…”

Section: Background and Related Workmentioning

confidence: 99%

Custom architecture for multicore audio beamforming systems

Theodoropoulos

Kuzmanov

Gaydadjiev

2013

ACM Trans. Embed. Comput. Syst.

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The audio Beamforming (BF) technique utilizes microphone arrays to extract acoustic sources recorded in a noisy environment. In this article, we propose a new approach for rapid development of multicore BF systems. Research on literature reveals that the majority of such experimental and commercial audio systems are based on desktop PCs, due to their high-level programming support and potential of rapid system development. However, these approaches introduce performance bottlenecks, excessive power consumption, and increased overall cost. Systems based on DSPs require very low power, but their performance is still limited. Custom hardware solutions alleviate the aforementioned drawbacks, however, designers primarily focus on performance optimization without providing a high-level interface for system control and test. In order to address the aforementioned problems, we propose a custom platform-independent architecture for reconfigurable audio BF systems. To evaluate our proposal, we implement our architecture as a heterogeneous multicore reconfigurable processor and map it onto FPGAs. Our approach combines the software flexibility of General-Purpose Processors (GPPs) with the computational power of multicore platforms. In order to evaluate our system we compare it against a BF software application implemented to a low-power Atom 330, a middle-ranged Core2 Duo, and a high-end Core i3. Experimental results suggest that our proposed solution can extract up to 16 audio sources in real time under a 16-microphone setup. In contrast, under the same setup, the Atom 330 cannot extract any audio sources in real time, while the Core2 Duo and the Core i3 can process in real time only up to 4 and 6 sources respectively. Furthermore, a Virtex4-based BF system consumes more than an order less energy compared to the aforementioned GPP-based approaches.

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