641 research outputs found
Implementation and evaluation of a low complexity microphone array for speaker recognition
Includes bibliographical references (leaves 83-86).This thesis discusses the application of a microphone array employing a noise canceling beamforming technique for improving the robustness of speaker recognition systems in a diffuse noise field
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
A Low-Cost Robust Distributed Linearly Constrained Beamformer for Wireless Acoustic Sensor Networks with Arbitrary Topology
We propose a new robust distributed linearly constrained beamformer which
utilizes a set of linear equality constraints to reduce the cross power
spectral density matrix to a block-diagonal form. The proposed beamformer has a
convenient objective function for use in arbitrary distributed network
topologies while having identical performance to a centralized implementation.
Moreover, the new optimization problem is robust to relative acoustic transfer
function (RATF) estimation errors and to target activity detection (TAD)
errors. Two variants of the proposed beamformer are presented and evaluated in
the context of multi-microphone speech enhancement in a wireless acoustic
sensor network, and are compared with other state-of-the-art distributed
beamformers in terms of communication costs and robustness to RATF estimation
errors and TAD errors
Reconstructing the Dynamic Directivity of Unconstrained Speech
This article presents a method for estimating and reconstructing the spatial
energy distribution pattern of natural speech, which is crucial for achieving
realistic vocal presence in virtual communication settings. The method
comprises two stages. First, recordings of speech captured by a real, static
microphone array are used to create an egocentric virtual array that tracks the
movement of the speaker over time. This virtual array is used to measure and
encode the high-resolution directivity pattern of the speech signal as it
evolves dynamically with natural speech and movement. In the second stage, the
encoded directivity representation is utilized to train a machine learning
model that can estimate the full, dynamic directivity pattern given a limited
set of speech signals, such as those recorded using the microphones on a
head-mounted display. Our results show that neural networks can accurately
estimate the full directivity pattern of natural, unconstrained speech from
limited information. The proposed method for estimating and reconstructing the
spatial energy distribution pattern of natural speech, along with the
evaluation of various machine learning models and training paradigms, provides
an important contribution to the development of realistic vocal presence in
virtual communication settings.Comment: In proceedings of I3DA 2023 - The 2023 International Conference on
Immersive and 3D Audio. DOI coming soo
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