43 research outputs found
Frequency-Sliding Generalized Cross-Correlation: A Sub-band Time Delay Estimation Approach
The generalized cross correlation (GCC) is regarded as the most popular
approach for estimating the time difference of arrival (TDOA) between the
signals received at two sensors. Time delay estimates are obtained by
maximizing the GCC output, where the direct-path delay is usually observed as a
prominent peak. Moreover, GCCs play also an important role in steered response
power (SRP) localization algorithms, where the SRP functional can be written as
an accumulation of the GCCs computed from multiple sensor pairs. Unfortunately,
the accuracy of TDOA estimates is affected by multiple factors, including
noise, reverberation and signal bandwidth. In this paper, a sub-band approach
for time delay estimation aimed at improving the performance of the
conventional GCC is presented. The proposed method is based on the extraction
of multiple GCCs corresponding to different frequency bands of the cross-power
spectrum phase in a sliding-window fashion. The major contributions of this
paper include: 1) a sub-band GCC representation of the cross-power spectrum
phase that, despite having a reduced temporal resolution, provides a more
suitable representation for estimating the true TDOA; 2) such matrix
representation is shown to be rank one in the ideal noiseless case, a property
that is exploited in more adverse scenarios to obtain a more robust and
accurate GCC; 3) we propose a set of low-rank approximation alternatives for
processing the sub-band GCC matrix, leading to better TDOA estimates and source
localization performance. An extensive set of experiments is presented to
demonstrate the validity of the proposed approach.Comment: Article accepted in IEEE/ACM Transactions on Audio, Speech, and
Language Processin
Compensating first reflections in non-anechoic head-related transfer function measurements
[EN] Personalized Head-Related Transfer Functions (HRTFs) are needed as part of the binaural sound individ- ualization process in order to provide a high-quality immersive experience for a specific user. Signal processing methods for performing HRTF measurements in non-anechoic conditions are of high interest to avoid the complex and inconvenient access to anechoic facilities. Non-anechoic HRTF measurements capture the effect of room reflections, which should be correctly identified and eliminated to obtain HRTFs estimates comparable to ones acquired in an anechoic setup. This paper proposes a sub-band frequency-dependent processing method for reflection suppression in non-anechoic HRTF signals. Array processing techniques based on Plane Wave Decomposition (PWD) are adopted as an essential part of the solution for low frequency ranges, whereas the higher frequencies are easily handled by means of time-crop windowing methods. The formulation of the model, extraction of parameters and evaluation of the method are described in detail. In addition, a validation case study is presented showing the suppression of reflections from an HRTF measured in a real system. The results confirm that the method allows to obtain processed HRTFs comparable to those acquired in anechoic conditions.This work has received funding from the Spanish Ministry of Science, Innovation and Universities, through projects RTI2018097045-B-C21 and RTI2018-097045-B-C22, and Generalitat Valenciana under the AICO/2020/154 project grant.López Monfort, JJ.; Gutierrez-Parera, P.; Cobos, M. (2022). Compensating first reflections in non-anechoic head-related transfer function measurements. Applied Acoustics. 188:1-13. https://doi.org/10.1016/j.apacoust.2021.10852311318
Design and Implementation of Acoustic Source Localization on a Low-Cost IoT Edge Platform
The implementation of algorithms for acoustic source localization on edge platforms for the Internet of Things (IoT) is gaining momentum. Applications based on acoustic monitoring can greatly benefit from efficient implementations of such algorithms, enabling novel services for smart homes and buildings or ambient-assisted living. In this context, this brief proposes extreme low-cost sound source localization system composed of two microphones and the low power microcontroller module ESP32. A Direction-Of-Arrival (DOA) algorithm has been implemented taking into account the specific features of this board, showing excellent performance despite the memory constraints imposed by the platform. We have also adapted off-the-shelf lowcost microphone boards to the input requirements of the ESP32 Analog-to-Digital Converter. The processing has been optimized by leveraging in parallel both cores of the microcontroller to capture and process the audio in real time. Our experiments expose that we can perform real-time localization, with a processing time below 3.3 ms.This work was supported in part by the Spanish Government under Grant TIN2017-82972-R, Grant ESP2015-68245-C4-1-P, and Grant RTI2018-097045-B-C21, and in part by the Valencian Regional Government under Grant PROMETEO/2019/109
Simultaneous ranging and self-positioning in unsynchronized wireless acoustic sensor networks
Automatic ranging and self-positioning is a very
desirable property in wireless acoustic sensor networks (WASNs)
where nodes have at least one microphone and one loudspeaker.
However, due to environmental noise, interference and multipath
effects, audio-based ranging is a challenging task. This paper
presents a fast ranging and positioning strategy that makes use
of the correlation properties of pseudo-noise (PN) sequences for
estimating simultaneously relative time-of-arrivals (TOAs) from
multiple acoustic nodes. To this end, a proper test signal design
adapted to the acoustic node transducers is proposed. In addition,
a novel self-interference reduction method and a peak matching
algorithm are introduced, allowing for increased accuracy in
indoor environments. Synchronization issues are removed by
following a BeepBeep strategy, providing range estimates that
are converted to absolute node positions by means of multidimensional
scaling (MDS). The proposed approach is evaluated both
with simulated and real experiments under different acoustical
conditions. The results using a real network of smartphones and
laptops confirm the validity of the proposed approach, reaching
an average ranging accuracy below 1 centimeter.This work was supported by the Spanish Ministry of Economy and Competitiveness under Grant TIN2015-70202-P, TEC2012-37945-C02-02 and FEDER funds
Performance analysis of a millimeter wave MIMO channel estimation method in an embedded multi-core processor
The emerging Multi-Processor System-on-Chip (MPSoC) technology, which combines heterogeneous computing with the high performance of field programmable gate arrays (FPGA), is a promising platform for a large number of applications, including wireless communications and vehicular technology. In this specific application context, when multiple-input multiple-output (MIMO) scenarios are considered, the system usually has to manage a large number of communication links among sensors and antennas involving different vehicles and users. Millimeter wave (mmWave) communications are one of the key technology enablers toward achieving high data rates in beyond 5G systems (B5G). Communication at these frequency bands usually involves the use of large antenna arrays, often requiring high computational resources. One of the candidate platforms able to manage a huge number of communications is the Xilinx Zynq UltraScale+ EG Heterogeneous MPSoC, which is composed of a dual-core Cortex-R5, a quad-core ARM Cortex-A53, a graphics processing unit (GPU) and a high-end FPGA. This work analyzes the computational performance that requires a recent mmWave MIMO channel estimation algorithm in a platform of this kind. As a first approach, we will focus our work on the performance that can be achieved via the quad-core ARM Cortex-A53. To this end, we will use the libraries for numerical algebra (BLAS and LAPACK). The results show that our reference implementation is able to manage a large MIMO communication system with 256 antennas without exhausting platform resources.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Thanks to Grant PID2020-113785RB-100 funded by MCIN/AEI/1013039/ 501100011033 and the Ramón y Cajal Grant RYC-2017-22101. The work has been also supported by the Spanish Ministry of Science and Innovation under Grants RTI2018-097045-B-C21, PID2019-106455GB-C21 and PID2020-113656RB-C21, as well as the Regional Government of Madrid throughout the projects MIMACUHSPACE-CM-UC3M (2022/00024/001) and PEJD-2019-PRE/TIC-16327