409 research outputs found
Particle Filter Design Using Importance Sampling for Acoustic Source Localisation and Tracking in Reverberant Environments
Sequential Monte Carlo methods have been recently proposed to deal with the problem of acoustic source localisation and tracking using an array of microphones. Previous implementations make use of the basic bootstrap particle filter, whereas a more general approach involves the concept of importance sampling. In this paper, we develop a new particle filter for acoustic source localisation using importance sampling, and compare its tracking ability with that of a bootstrap algorithm proposed previously in the literature. Experimental results obtained with simulated reverberant samples and real audio recordings demonstrate that the new algorithm is more suitable for practical applications due to its reinitialisation capabilities, despite showing a slightly lower average tracking accuracy. A real-time implementation of the algorithm also shows that the proposed particle filter can reliably track a person talking in real reverberant rooms.This paper was performed while Eric A. Lehmann was working
with National ICT Australia. National ICT Australia
is funded by the Australian Government’s Department of
Communications, Information Technology, and the Arts,
the Australian Research Council, through Backing Australia’s
Ability, and the ICT Centre of Excellence programs
EXPERIMENTAL EVALUATION OF MODIFIED PHASE TRANSFORM FOR SOUND SOURCE DETECTION
The detection of sound sources with microphone arrays can be enhanced through processing individual microphone signals prior to the delay and sum operation. One method in particular, the Phase Transform (PHAT) has demonstrated improvement in sound source location images, especially in reverberant and noisy environments. Recent work proposed a modification to the PHAT transform that allows varying degrees of spectral whitening through a single parameter, andamp;acirc;, which has shown positive improvement in target detection in simulation results. This work focuses on experimental evaluation of the modified SRP-PHAT algorithm. Performance results are computed from actual experimental setup of an 8-element perimeter array with a receiver operating characteristic (ROC) analysis for detecting sound sources. The results verified simulation results of PHAT- andamp;acirc; in improving target detection probabilities. The ROC analysis demonstrated the relationships between various target types (narrowband and broadband), room reverberation levels (high and low) and noise levels (different SNR) with respect to optimal andamp;acirc;. Results from experiment strongly agree with those of simulations on the effect of PHAT in significantly improving detection performance for narrowband and broadband signals especially at low SNR and in the presence of high levels of reverberation
Online Localization and Tracking of Multiple Moving Speakers in Reverberant Environments
We address the problem of online localization and tracking of multiple moving
speakers in reverberant environments. The paper has the following
contributions. We use the direct-path relative transfer function (DP-RTF), an
inter-channel feature that encodes acoustic information robust against
reverberation, and we propose an online algorithm well suited for estimating
DP-RTFs associated with moving audio sources. Another crucial ingredient of the
proposed method is its ability to properly assign DP-RTFs to audio-source
directions. Towards this goal, we adopt a maximum-likelihood formulation and we
propose to use an exponentiated gradient (EG) to efficiently update
source-direction estimates starting from their currently available values. The
problem of multiple speaker tracking is computationally intractable because the
number of possible associations between observed source directions and physical
speakers grows exponentially with time. We adopt a Bayesian framework and we
propose a variational approximation of the posterior filtering distribution
associated with multiple speaker tracking, as well as an efficient variational
expectation-maximization (VEM) solver. The proposed online localization and
tracking method is thoroughly evaluated using two datasets that contain
recordings performed in real environments.Comment: IEEE Journal of Selected Topics in Signal Processing, 201
Efficient Interferer Cancelation based on Geometrical Information of the Reverberant Environment
reserved7ISSN (online) 2219-5491
5 pagg totaliPagani, P.; Riva, D.; Antonacci, F.; Prandi, G.; Tagliasacchi, M.; Sarti, A.; Tubaro, S.Pagani, P.; Riva, Davide; Antonacci, Fabio; Prandi, Giorgio; Tagliasacchi, Marco; Sarti, Augusto; Tubaro, Stefan
Spatial dissection of a soundfield using spherical harmonic decomposition
A real-world soundfield is often contributed by multiple desired and undesired sound sources. The performance of many acoustic systems such as automatic speech recognition, audio surveillance, and teleconference relies on its ability to extract the desired sound components in such a mixed environment. The existing solutions to the above problem are constrained by various fundamental limitations and require to enforce different priors depending on the acoustic condition such as reverberation and spatial distribution of sound sources. With the growing emphasis and integration of audio applications in diverse technologies such as smart home and virtual reality appliances, it is imperative to advance the source separation technology in order to overcome the limitations of the traditional approaches.
To that end, we exploit the harmonic decomposition model to dissect a mixed soundfield into its underlying desired and undesired components based on source and signal characteristics. By analysing the spatial projection of a soundfield, we achieve multiple outcomes such as (i) soundfield separation with respect to distinct source regions, (ii) source separation in a mixed soundfield using modal coherence model, and (iii) direction of arrival (DOA) estimation of multiple overlapping sound sources through pattern recognition of the modal coherence of a soundfield.
We first employ an array of higher order microphones for soundfield separation in order to reduce hardware requirement and implementation complexity. Subsequently, we develop novel mathematical models for modal coherence of noisy and reverberant soundfields that facilitate convenient ways for estimating DOA and power spectral densities leading to robust source separation algorithms. The modal domain approach to the soundfield/source separation allows us to circumvent several practical limitations of the existing techniques and enhance the performance and robustness of the system. The proposed methods are presented with several practical applications and performance evaluations using simulated and real-life dataset
A multimodal approach to blind source separation of moving sources
A novel multimodal approach is proposed to solve the
problem of blind source separation (BSS) of moving sources. The
challenge of BSS for moving sources is that the mixing filters are
time varying; thus, the unmixing filters should also be time varying,
which are difficult to calculate in real time. In the proposed approach,
the visual modality is utilized to facilitate the separation for
both stationary and moving sources. The movement of the sources
is detected by a 3-D tracker based on video cameras. Positions
and velocities of the sources are obtained from the 3-D tracker
based on a Markov Chain Monte Carlo particle filter (MCMC-PF),
which results in high sampling efficiency. The full BSS solution
is formed by integrating a frequency domain blind source separation
algorithm and beamforming: if the sources are identified
as stationary for a certain minimum period, a frequency domain
BSS algorithm is implemented with an initialization derived from
the positions of the source signals. Once the sources are moving, a
beamforming algorithm which requires no prior statistical knowledge
is used to perform real time speech enhancement and provide
separation of the sources. Experimental results confirm that
by utilizing the visual modality, the proposed algorithm not only
improves the performance of the BSS algorithm and mitigates the
permutation problem for stationary sources, but also provides a
good BSS performance for moving sources in a low reverberant
environment
Video-aided model-based source separation in real reverberant rooms
Source separation algorithms that utilize only audio
data can perform poorly if multiple sources or reverberation
are present. In this paper we therefore propose a video-aided
model-based source separation algorithm for a two-channel
reverberant recording in which the sources are assumed static.
By exploiting cues from video, we first localize individual speech
sources in the enclosure and then estimate their directions.
The interaural spatial cues, the interaural phase difference and
the interaural level difference, as well as the mixing vectors
are probabilistically modeled. The models make use of the
source direction information and are evaluated at discrete timefrequency
points. The model parameters are refined with the wellknown
expectation-maximization (EM) algorithm. The algorithm
outputs time-frequency masks that are used to reconstruct the
individual sources. Simulation results show that by utilizing the
visual modality the proposed algorithm can produce better timefrequency
masks thereby giving improved source estimates. We
provide experimental results to test the proposed algorithm in
different scenarios and provide comparisons with both other
audio-only and audio-visual algorithms and achieve improved
performance both on synthetic and real data. We also include
dereverberation based pre-processing in our algorithm in order
to suppress the late reverberant components from the observed
stereo mixture and further enhance the overall output of the algorithm.
This advantage makes our algorithm a suitable candidate
for use in under-determined highly reverberant settings where
the performance of other audio-only and audio-visual methods
is limited
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