127 research outputs found
Multimodal methods for blind source separation of audio sources
The enhancement of the performance of frequency domain convolutive
blind source separation (FDCBSS) techniques when applied to the
problem of separating audio sources recorded in a room environment
is the focus of this thesis. This challenging application is termed the
cocktail party problem and the ultimate aim would be to build a machine
which matches the ability of a human being to solve this task.
Human beings exploit both their eyes and their ears in solving this task
and hence they adopt a multimodal approach, i.e. they exploit both
audio and video modalities. New multimodal methods for blind source
separation of audio sources are therefore proposed in this work as a
step towards realizing such a machine.
The geometry of the room environment is initially exploited to improve
the separation performance of a FDCBSS algorithm. The positions
of the human speakers are monitored by video cameras and this
information is incorporated within the FDCBSS algorithm in the form
of constraints added to the underlying cross-power spectral density
matrix-based cost function which measures separation performance. [Continues.
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
Convolutive Blind Source Separation Methods
In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be organized, and we present published results from those algorithms that have been applied to real-world audio separation tasks
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
Use of Bimodal Coherence to Resolve Spectral Indeterminacy in Convolutive BSS
Recent studies show that visual information contained in visual speech can be helpful for the performance enhancement of audio-only blind source separation (BSS) algorithms. Such information is exploited through the statistical characterisation of the coherence between the audio and visual speech using, e.g. a Gaussian mixture model (GMM). In this paper, we present two new contributions. An adapted expectation maximization (AEM) algorithm is proposed in the training process to model the audio-visual coherence upon the extracted features. The coherence is exploited to solve the permutation problem in the frequency domain using a new sorting scheme. We test our algorithm on the XM2VTS multimodal database. The experimental results show that our proposed algorithm outperforms traditional audio-only BSS
Real Time Blind Source Separation in Reverberant Environments
An online convolutive blind source separation solution has been developed for use in reverberant environments with stationary sources. Results are presented for simulation and real world data. The system achieves a separation SINR of 16.8 dB when operating on a two source mixture, with a total acoustic delay was 270 ms. This is on par with, and in many respects outperforms various published algorithms [1],[2]. A number of instantaneous blind source separation algorithms have been developed, including a block wise and recursive ICA algorithm, and a clustering based algorithm, able to obtain up to 110 dB SIR performance. The system has been realised in both Matlab and C, and is modular, allowing for easy update of the ICA algorithm that is the core of the unmixing process
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