98 research outputs found
Enhancement by postfiltering for speech and audio coding in ad-hoc sensor networks
Enhancement algorithms for wireless acoustics sensor networks~(WASNs) are
indispensable with the increasing availability and usage of connected devices
with microphones. Conventional spatial filtering approaches for enhancement in
WASNs approximate quantization noise with an additive Gaussian distribution,
which limits performance due to the non-linear nature of quantization noise at
lower bitrates. In this work, we propose a postfilter for enhancement based on
Bayesian statistics to obtain a multidevice signal estimate, which explicitly
models the quantization noise. Our experiments using PSNR, PESQ and MUSHRA
scores demonstrate that the proposed postfilter can be used to enhance signal
quality in ad-hoc sensor networks
Noise Statistics Update Adaptive Beamfomer With PSD Estimation for Speech Extraction in Noisy Environment
This paper addresses the problem of extracting a desired speech source from a multispeaker environment in the presence of background noise. A new adaptive beamforming structure is proposed for this speech enhancement problem. This structure incorporates power spectral density (PSD) estimation of the speech sources together with a noise statistics update. An inactive-source detector based on minimum statistics is developed to detect the speech presence and to track the noise statistics. Performance of the proposed beamformer is investigated and compared to the minimum variance distortionless response (MVDR) beamformer with or without a postfilter in a real hands-free communication environment. Evaluations show that the proposed beamformer offers good interference and noise suppression levels while maintaining low distortion of the desired source
Joint NN-Supported Multichannel Reduction of Acoustic Echo, Reverberation and Noise
We consider the problem of simultaneous reduction of acoustic echo,
reverberation and noise. In real scenarios, these distortion sources may occur
simultaneously and reducing them implies combining the corresponding
distortion-specific filters. As these filters interact with each other, they
must be jointly optimized. We propose to model the target and residual signals
after linear echo cancellation and dereverberation using a multichannel
Gaussian modeling framework and to jointly represent their spectra by means of
a neural network. We develop an iterative block-coordinate ascent algorithm to
update all the filters. We evaluate our system on real recordings of acoustic
echo, reverberation and noise acquired with a smart speaker in various
situations. The proposed approach outperforms in terms of overall distortion a
cascade of the individual approaches and a joint reduction approach which does
not rely on a spectral model of the target and residual signals
Dual-Channel Speech Enhancement Based on Extended Kalman Filter Relative Transfer Function Estimation
This paper deals with speech enhancement in dual-microphone smartphones using
beamforming along with postfiltering techniques. The performance of these algorithms relies on
a good estimation of the acoustic channel and speech and noise statistics. In this work we present
a speech enhancement system that combines the estimation of the relative transfer function (RTF)
between microphones using an extended Kalman filter framework with a novel speech presence
probability estimator intended to track the noise statisticsâ variability. The available dual-channel
information is exploited to obtain more reliable estimates of clean speech statistics. Noise reduction
is further improved by means of postfiltering techniques that take advantage of the speech presence
estimation. Our proposal is evaluated in different reverberant and noisy environments when the
smartphone is used in both close-talk and far-talk positions. The experimental results show that our
system achieves improvements in terms of noise reduction, low speech distortion and better speech
intelligibility compared to other state-of-the-art approaches.Spanish MINECO/FEDER Project TEC2016-80141-PSpanish
Ministry of Education through the National Program FPU under Grant FPU15/0416
- âŠ