576 research outputs found
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
Realistic multi-microphone data simulation for distant speech recognition
The availability of realistic simulated corpora is of key importance for the
future progress of distant speech recognition technology. The reliability,
flexibility and low computational cost of a data simulation process may
ultimately allow researchers to train, tune and test different techniques in a
variety of acoustic scenarios, avoiding the laborious effort of directly
recording real data from the targeted environment.
In the last decade, several simulated corpora have been released to the
research community, including the data-sets distributed in the context of
projects and international challenges, such as CHiME and REVERB. These efforts
were extremely useful to derive baselines and common evaluation frameworks for
comparison purposes. At the same time, in many cases they highlighted the need
of a better coherence between real and simulated conditions.
In this paper, we examine this issue and we describe our approach to the
generation of realistic corpora in a domestic context. Experimental validation,
conducted in a multi-microphone scenario, shows that a comparable performance
trend can be observed with both real and simulated data across different
recognition frameworks, acoustic models, as well as multi-microphone processing
techniques.Comment: Proc. of Interspeech 201
Exploiting CNNs for Improving Acoustic Source Localization in Noisy and Reverberant Conditions
This paper discusses the application of convolutional neural networks (CNNs) to minimum variance distortionless response localization schemes. We investigate the direction of arrival estimation problems in noisy and reverberant conditions using a uniform linear array (ULA). CNNs are used to process the multichannel data from the ULA and to improve the data fusion scheme, which is performed in the steered response power computation. CNNs improve the incoherent frequency fusion of the narrowband response power by weighting the components, reducing the deleterious effects of those components affected by artifacts due to noise and reverberation. The use of CNNs avoids the necessity of previously encoding the multichannel data into selected acoustic cues with the advantage to exploit its ability in recognizing geometrical pattern similarity. Experiments with both simulated and real acoustic data demonstrate the superior localization performance of the proposed SRP beamformer with respect to other state-of-the-art techniques
GWA: A Large High-Quality Acoustic Dataset for Audio Processing
We present the Geometric-Wave Acoustic (GWA) dataset, a large-scale audio
dataset of over 2 million synthetic room impulse responses (IRs) and their
corresponding detailed geometric and simulation configurations. Our dataset
samples acoustic environments from over 6.8K high-quality diverse and
professionally designed houses represented as semantically labeled 3D meshes.
We also present a novel real-world acoustic materials assignment scheme based
on semantic matching that uses a sentence transformer model. We compute
high-quality impulse responses corresponding to accurate low-frequency and
high-frequency wave effects by automatically calibrating geometric acoustic
ray-tracing with a finite-difference time-domain wave solver. We demonstrate
the higher accuracy of our IRs by comparing with recorded IRs from complex
real-world environments. The code and the full dataset will be released at the
time of publication. Moreover, we highlight the benefits of GWA on audio deep
learning tasks such as automated speech recognition, speech enhancement, and
speech separation. We observe significant improvement over prior synthetic IR
datasets in all tasks due to using our dataset.Comment: Project webpage https://gamma.umd.edu/pro/sound/gw
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