Liu Q, Wang W (2013) Show-through removal for scanned images using non-linear NMF with adaptive smoothing, 2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings pp. 650-654
Scans of double-sided documents often suffer from show-through distortions, where contents of the reverse side (verso) may appear in the front-side page (recto). Several algorithms employed for show-through removal from the scanned images, are based on linear mixing models, including blind source separation (BSS), non-negative matrix factorization (NMF), and adaptive filtering. However, a recent study shows that a non-linear model may provide better performance for resolving the overlapping front-reverse contents, especially in grayscale scans. In this paper, we propose a new non-linear NMF algorithm based on projected gradient adaptation. An adaptive filtering process is also incorporated to further eliminate the blurring effect caused by non-perfect calibration of the scans. Our numerical tests show that the proposed algorithm offers better results than the baseline methods. © 2013 IEEE.
Liu Q, Wang W (2011) Blind source separation and visual voice activity detection for target speech extraction, Proceedings of 2011 3rd International Conference on Awareness Science and Technology, iCAST 2011 pp. 457-460
Despite being studied extensively, the performance of blind source separation (BSS) is still limited especially for the sensor data collected in adverse environments. Recent studies show that such an issue can be mitigated by incorporating multimodal information into the BSS process. In this paper, we propose a method for the enhancement of the target speech separated by a BSS algorithm from sound mixtures, using visual voice activity detection (VAD) and spectral subtraction. First, a classifier for visual VAD is formed in the off-line training stage, using labelled features extracted from the visual stimuli. Then we use this visual VAD classifier to detect the voice activity of the target speech. Finally we apply a multi-band spectral subtraction algorithm to enhance the BSS-separated speech signal based on the detected voice activity. We have tested our algorithm on the mixtures generated artificially by the mixing filters with different reverberation times, and the results show that our algorithm improves the quality of the separated target signal. © 2011 IEEE.
Representing a complex acoustic scene with audio objects is desirable but challenging in object-based spatial audio production and reproduction, especially when concurrent sound signals are present in the scene. Source separation (SS) provides a potentially useful and enabling tool for audio object extraction. These extracted objects are often remixed to reconstruct a sound field in the reproduction stage. A suitable SS method is expected to produce audio objects that ultimately deliver high quality audio after remix. The performance of these SS algorithms therefore needs to be evaluated in this context. Existing metrics for SS performance evaluation, however, do not take into account the essential sound field reconstruction process. To address this problem, here we propose a new SS evaluation method which employs a remixing strategy similar to the panning law, and provides a framework to incorporate the conventional SS metrics. We have tested our proposed method on real-room recordings processed with four SS methods, including two state-of-the art blind source separation (BSS) methods and two classic beamforming algorithms. The evaluation results based on three conventional SS metrics are analysed.
We present a novel method for extracting target speech from auditory mixtures using bimodal coherence, which is statistically characterised by a Gaussian mixture modal (GMM) in the offline
training process, using the robust features obtained from the audio-visual speech. We then adjust the ICA-separated spectral components using the bimodal coherence in the time-frequency
domain, to mitigate the scale ambiguities in different frequency bins. We tested our algorithm on the XM2VTS database, and the results show the performance improvement with our proposed algorithm in terms of SIR measurements.
Information from video has been used recently to address the issue of scaling
ambiguity in convolutive blind source separation (BSS) in the frequency domain,
based on statistical modeling of the audio-visual coherence with Gaussian
mixture models (GMMs) in the feature space. However, outliers in the feature
space may greatly degrade the system performance in both training and
separation stages. In this paper, a new feature selection scheme is proposed to
discard non-stationary features, which improves the robustness of the coherence
model and reduces its computational complexity. The scaling parameters obtained
by coherence maximization and non-linear interpolation from the selected
features are applied to the separated frequency components to mitigate the
scaling ambiguity. A multimodal database composed of different combinations of
vowels and consonants was used to test our algorithm. Experimental results show
the performance improvement with our proposed algorithm.
Liu Qingju, deCampos T, Wang Wenwu, Jackson Philip, Hilton Adrian (2016) Person tracking using audio and depth cues,International Conference on Computer Vision (ICCV) Workshop on 3D Reconstruction and Understanding with Video and Sound pp. 709-717
In this paper, a novel probabilistic Bayesian tracking scheme is proposed and applied to bimodal measurements consisting of tracking results from the depth sensor and audio recordings collected using binaural microphones. We use random finite sets to cope with varying number of tracking targets. A measurement-driven birth process is integrated to quickly localize any emerging person. A new bimodal fusion method that prioritizes the most confident modality is employed. The approach was tested on real room recordings and experimental results show that the proposed combination of audio and depth outperforms individual modalities, particularly when there are multiple people talking simultaneously and when occlusions are frequent.
Probabilistic models of binaural cues, such as the interaural phase difference (IPD) and the interaural level difference (ILD), can be used to obtain the audio mask in the time-frequency (TF) domain, for source separation of binaural mixtures. Those models are, however, often degraded by acoustic noise. In contrast, the video stream contains relevant information about the synchronous audio stream that is not affected by acoustic noise. In this paper, we present a novel method for modeling the audio-visual (AV) coherence based on dictionary learning. A visual mask is constructed from the video signal based on the learnt AV dictionary, and incorporated with the audio mask to obtain a noise-robust audio-visual mask, which is then applied to the binaural signal for source separation. We tested our algorithm on the XM2VTS database, and observed considerable performance improvement for noise corrupted signals.
Recent studies show that facial 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 characterization of the coherence between the audio and visual speech using, e.g., a Gaussian mixture model (GMM). In this paper, we present three contributions. With the synchronized features, we propose an adapted expectation maximization (AEM) algorithm to model the audiovisual coherence in the off-line training process. To improve the accuracy of this coherence model, we use a frame selection scheme to discard nonstationary features. Then with the coherence maximization technique, we develop a new sorting method to solve the permutation problem in the frequency domain. We test our algorithm on a multimodal speech database composed of different combinations of vowels and consonants. The experimental results show that our proposed algorithm outperforms traditional audio-only BSS, which confirms the benefit of using visual speech to assist in separation of the audio. © 2011 Elsevier B.V. All rights reserved.
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.
Spontaneous speech in videos capturing the speaker's mouth provides bimodal information.
Exploiting the relationship between the audio and visual streams, we propose a new visual
voice activity detection (VAD) algorithm, to overcome the vulnerability of conventional
audio VAD techniques in the presence of background interference. First, a novel lip
extraction algorithm combining rotational templates and prior shape constraints with
active contours is introduced. The visual features are then obtained from the extracted
lip region. Second, with the audio voice activity vector used in training, adaboosting is
applied to the visual features, to generate a strong final voice activity classifier by
boosting a set of weak classifiers. We have tested our lip extraction algorithm on the
XM2VTS database (with higher resolution) and some video clips from YouTube (with lower
resolution). The visual VAD was shown to offer low error rates.
The work on 3D human pose estimation has seen a significant amount of progress in recent years, particularly due to the widespread availability of commodity depth sensors. However, most pose estimation methods follow a tracking-as-detection approach which does not explicitly handle occlusions, thus introducing outliers and identity association issues when multiple targets are involved. To address these issues, we propose a new method based on Probability Hypothesis Density (PHD) filter. In this method, the PHD filter with a novel clutter intensity model is used to remove outliers in the 3D head detection results, followed by an identity association scheme with occlusion detection for the targets. Experimental results show that our proposed method greatly mitigates the outliers, and correctly associates identities to individual detections with low computational cost.
Liu Q, Wang W, Jackson PJB, Barnard M, Kittler J, Chambers J (2013) Source separation of convolutive and noisy mixtures using audio-visual dictionary learning and probabilistic time-frequency masking, IEEE Transactions on Signal Processing 61 (22) 99 pp. 5520-5535
In existing audio-visual blind source separation (AV-BSS) algorithms, the AV coherence is usually established through statistical modelling, using e.g. Gaussian mixture models (GMMs). These methods often operate in a lowdimensional feature space, rendering an effective global representation of the data. The local information, which is important in capturing the temporal structure of the data, however, has not been explicitly exploited. In this paper, we propose a new method for capturing such local information, based on audio-visual dictionary learning (AVDL). We address several challenges associated with AVDL, including cross-modality differences in size, dimension and sampling rate, as well as the issues of scalability and computational complexity. Following a commonly employed bootstrap coding-learning process, we have developed a new AVDL algorithm which features, a bimodality balanced and scalable matching criterion, a size and dimension adaptive dictionary, a fast search index for efficient coding, and cross-modality diverse sparsity. We also show how the proposed AVDL can be incorporated into a BSS algorithm. As an example, we consider binaural mixtures, mimicking aspects of human binaural hearing, and derive a new noise-robust AV-BSS algorithm by combining the proposed AVDL algorithm with Mandel?s BSS method, which is a state-of-the-art audio-domain method using time-frequency masking. We have systematically evaluated the proposed AVDL and AV-BSS algorithms, and show their advantages over the corresponding baseline methods, using both synthetic data and visual speech data from the multimodal LILiR Twotalk corpus.
Alinaghi A, Jackson PJB, Liu Q, Wang W (2014) Joint Mixing Vector and Binaural Model Based Stereo Source Separation, IEEE Transactions on Audio, Speech, & Language Processing 22 9 pp. 1434-1448
In this paper the mixing vector (MV) in the statistical mixing model is compared to the binaural cues represented by interaural level and phase differences (ILD and IPD). It is shown that the MV distributions are quite distinct while binaural models overlap when the sources are close to each other. On the other hand, the binaural cues are more robust to high reverberation than MV models. According to this complementary behavior we introduce a new robust algorithm for stereo speech separation which considers both additive and convolutive noise signals to model the MV and binaural cues in parallel and estimate probabilistic time-frequency masks. The contribution of each cue to the final decision is also adjusted by weighting the log-likelihoods of the cues empirically. Furthermore, the permutation problem of the frequency domain blind source separation (BSS) is addressed by initializing the MVs based on binaural cues. Experiments are performed systematically on determined and underdetermined speech mixtures in five rooms with various acoustic properties including anechoic, highly reverberant, and spatially-diffuse noise conditions. The results in terms of signal-to-distortion-ratio (SDR) confirm the benefits of integrating the MV and binaural cues, as compared with two state-of-the-art baseline algorithms which only use MV or the binaural cues.
© 2014 IEEE.The visual modality, deemed to be complementary to the audio modality, has recently been exploited to improve the performance of blind source separation (BSS) of speech mixtures, especially in adverse environments where the performance of audio-domain methods deteriorates steadily. In this paper, we present an enhancement method to audio-domain BSS with the integration of voice activity information, obtained via a visual voice activity detection (VAD) algorithm. Mimicking aspects of human hearing, binaural speech mixtures are considered in our two-stage system. Firstly, in the off-line training stage, a speaker-independent voice activity detector is formed using the visual stimuli via the adaboosting algorithm. In the on-line separation stage, interaural phase difference (IPD) and interaural level difference (ILD) cues are statistically analyzed to assign probabilistically each time-frequency (TF) point of the audio mixtures to the source signals. Next, the detected voice activity cues (found via the visual VAD) are integrated to reduce the interference residual. Detection of the interference residual takes place gradually, with two layers of boundaries in the correlation and energy ratio map. We have tested our algorithm on speech mixtures generated using room impulse responses at different reverberation times and noise levels. Simulation results show performance improvement of the proposed method for target speech extraction in noisy and reverberant environments, in terms of signal-to-interference ratio (SIR) and perceptual evaluation of speech quality (PESQ).
State-of-the-art binaural objective intelligibility measures (OIMs) require individual source signals for making intelligibility predictions, limiting their usability in real-time online operations. This limitation may be addressed by a blind source separation (BSS) process, which is able to extract the underlying sources from a mixture. In this study, a speech source is presented with either a stationary noise masker or a fluctuating noise masker whose azimuth varies in a horizontal plane, at two speech-to-noise ratios (SNRs). Three binaural OIMs are used to predict speech intelligibility from the signals separated by a BSS algorithm. The model predictions are compared with listeners' word identification rate in a perceptual listening experiment. The results suggest that with SNR compensation to the BSS-separated speech signal, the OIMs can maintain their predictive power for individual maskers compared to their performance measured from the direct signals. It also reveals that the errors in SNR between the estimated signals are not the only factors that decrease the predictive accuracy of the OIMs with the separated signals. Artefacts or distortions on the estimated signals caused by the BSS algorithm may also be concerns.
A novel multimodal (audio-visual) approach to the problem of blind source separation (BSS) is evaluated in room environments. The main challenges of BSS in realistic environments are: 1) sources are moving in complex motions and 2) the room impulse responses are long. For moving sources the unmixing filters to separate the audio signals are difficult to calculate from only statistical information available from a limited number of audio samples. For physically stationary sources measured in rooms with long impulse responses, the performance of audio only BSS methods is limited. Therefore, visual modality is utilized to facilitate the separation. The movement of the sources is detected with a 3-D tracker based on a Markov Chain Monte Carlo particle filter (MCMC-PF), and the direction of arrival information of the sources to the microphone array is estimated. A robust least squares frequency invariant data independent (RLSFIDI) beamformer is implemented to perform real time speech enhancement. The uncertainties in source localization and direction of arrival information are also controlled by using a convex optimization approach in the beamformer design. A 16 element circular array configuration is used. Simulation studies based on objective and subjective measures confirm the advantage of beamforming based processing over conventional BSS methods. © 2011 EURASIP.
Deep neural networks (DNN) have recently been
shown to give state-of-the-art performance in monaural speech
enhancement. However in the DNN training process, the perceptual
difference between different components of the DNN
output is not fully exploited, where equal importance is often
assumed. To address this limitation, we have proposed a new
perceptually-weighted objective function within a feedforward
DNN framework, aiming to minimize the perceptual difference
between the enhanced speech and the target speech. A perceptual
weight is integrated into the proposed objective function, and
has been tested on two types of output features: spectra and
ideal ratio masks. Objective evaluations for both speech quality
and speech intelligibility have been performed. Integration of our
perceptual weight shows consistent improvement on several noise
levels and a variety of different noise types.
Binaural features of interaural level difference and
interaural phase difference have proved to be very effective
in training deep neural networks (DNNs), to generate timefrequency
masks for target speech extraction in speech-speech
mixtures. However, effectiveness of binaural features is reduced
in more common speech-noise scenarios, since the noise may
over-shadow the speech in adverse conditions. In addition, the
reverberation also decreases the sparsity of binaural features and
therefore adds difficulties to the separation task. To address the
above limitations, we highlight the spectral difference between
speech and noise spectra and incorporate the log-power spectra
features to extend the DNN input. Tested on two different
reverberant rooms at different signal to noise ratios (SNR), our
proposed method shows advantages over the baseline method
using only binaural features in terms of signal to distortion ratio
(SDR) and Short-Time Perceptual Intelligibility (STOI).
In object-based spatial audio system, positions of the
audio objects (e.g. speakers/talkers or voices) presented in the
sound scene are required as important metadata attributes for
object acquisition and reproduction. Binaural microphones are
often used as a physical device to mimic human hearing and to
monitor and analyse the scene, including localisation and tracking
of multiple speakers. The binaural audio tracker, however, is
usually prone to the errors caused by room reverberation and
background noise. To address this limitation, we present a
multimodal tracking method by fusing the binaural audio with
depth information (from a depth sensor, e.g., Kinect). More
specifically, the PHD filtering framework is first applied to the
depth stream, and a novel clutter intensity model is proposed
to improve the robustness of the PHD filter when an object
is occluded either by other objects or due to the limited field
of view of the depth sensor. To compensate mis-detections in
the depth stream, a novel gap filling technique is presented to
map audio azimuths obtained from the binaural audio tracker to
3D positions, using speaker-dependent spatial constraints learned
from the depth stream. With our proposed method, both the
errors in the binaural tracker and the mis-detections in the depth
tracker can be significantly reduced. Real-room recordings are
used to show the improved performance of the proposed method
in removing outliers and reducing mis-detections.
A non-intrusive method is introduced to predict binaural speech intelligibility in noise directly from signals captured using a pair of microphones. The approach combines signal processing techniques in blind source separation and localisation, with an intrusive objective intelligibility measure (OIM). Therefore, unlike classic intrusive OIMs, this method does not require a clean reference speech signal and knowing the location of the sources to operate. The proposed approach is able to estimate intelligibility in stationary and fluctuating noises, when the noise masker is presented as a point or diffused source, and is spatially separated from the target speech source on a horizontal plane. The performance of the proposed method was evaluated in two rooms. When predicting subjective intelligibility measured as word recognition rate, this method showed reasonable predictive accuracy with correlation coefficients above 0.82, which is comparable to that of a reference intrusive OIM in most of the conditions. The proposed approach offers a solution for fast binaural intelligibility prediction, and therefore has practical potential to be deployed in situations where on-site speech intelligibility is a concern.
Coleman Philip, Franck A, Francombe Jon, Liu Qingju, de Campos Teofilo, Hughes R, Menzies D, Simon Galvez, M, Tang Y, Woodcock J, Jackson Philip, Melchior F, Pike C, Fazi F, Cox T, Hilton Adrian (2018) An Audio-Visual System for Object-Based Audio:
From Recording to Listening,IEEE Transactions on Multimedia 20 (8) pp. 1919-1931
Object-based audio is an emerging representation
for audio content, where content is represented in a reproductionformat-
agnostic way and thus produced once for consumption on
many different kinds of devices. This affords new opportunities
for immersive, personalized, and interactive listening experiences.
This article introduces an end-to-end object-based spatial audio
pipeline, from sound recording to listening. A high-level
system architecture is proposed, which includes novel audiovisual
interfaces to support object-based capture and listenertracked
rendering, and incorporates a proposed component for
objectification, i.e., recording content directly into an object-based
form. Text-based and extensible metadata enable communication
between the system components. An open architecture for object
rendering is also proposed.
The system?s capabilities are evaluated in two parts. First,
listener-tracked reproduction of metadata automatically estimated
from two moving talkers is evaluated using an objective
binaural localization model. Second, object-based scene capture
with audio extracted using blind source separation (to remix
between two talkers) and beamforming (to remix a recording of
a jazz group), is evaluated with perceptually-motivated objective
and subjective experiments. These experiments demonstrate that
the novel components of the system add capabilities beyond
the state of the art. Finally, we discuss challenges and future
perspectives for object-based audio workflows.
Coleman Philip, Franck Andreas, Francombe Jon, Liu Qingju, de Campos Teofilo, Hughes Richard, Menzies Dylan, Simo?n Ga?lvez Marcos, Tang Yan, Woodcock James, Melchior Frank, Pike Chris, Fazi Filippo, Cox Trevor, Hilton Adrian, Jackson Philip (2018) S3A Audio-Visual System for Object-Based Audio,
University of Surrey
Coleman Philip, Liu Qingju, Francombe Jon, Jackson Philip (2018) Perceptual evaluation of blind source separation in object-based audio production,Latent Variable Analysis and Signal Separation - 14th International Conference, LVA/ICA 2018, Guildford, UK, July 2?5, 2018, Proceedings pp. 558-567
Object-based audio has the potential to enable multime-
dia content to be tailored to individual listeners and their reproduc-
tion equipment. In general, object-based production assumes that the
objects|the assets comprising the scene|are free of noise and inter-
ference. However, there are many applications in which signal separa-
tion could be useful to an object-based audio work
ow, e.g., extracting
individual objects from channel-based recordings or legacy content, or
recording a sound scene with a single microphone array. This paper de-
scribes the application and evaluation of blind source separation (BSS)
for sound recording in a hybrid channel-based and object-based workflow, in which BSS-estimated objects are mixed with the original stereo
recording. A subjective experiment was conducted using simultaneously
spoken speech recorded with omnidirectional microphones in a rever-
berant room. Listeners mixed a BSS-extracted speech object into the
scene to make the quieter talker clearer, while retaining acceptable au-
dio quality, compared to the raw stereo recording. Objective evaluations
show that the relative short-term objective intelligibility and speech qual-
ity scores increase using BSS. Further objective evaluations are used to
discuss the in
uence of the BSS method on the remixing scenario; the
scenario shown by human listeners to be useful in object-based audio is
shown to be a worse-case scenario.
In this paper, we propose an iterative deep neural network
(DNN)-based binaural source separation scheme, for recovering
two concurrent speech signals in a room environment.
Besides the commonly-used spectral features, the DNN also
takes non-linearly wrapped binaural spatial features as input,
which are refined iteratively using parameters estimated from
the DNN output via a feedback loop. Different DNN structures
have been tested, including a classic multilayer perception
regression architecture as well as a new hybrid network
with both convolutional and densely-connected layers. Objective
evaluations in terms of PESQ and STOI showed consistent
improvement over baseline methods using traditional
binaural features, especially when the hybrid DNN architecture
was employed. In addition, our proposed scheme is robust
to mismatches between the training and testing data.
Woodcock James, Franombe Jon, Franck Andreas, Coleman Philip, Hughes Richard, Kim Hansung, Liu Qingju, Menzies Dylan, Simón Gálvez Marcos F, Tang Yan, Brookes Tim, Davies William J, Fazenda Bruno M, Mason Russell, Cox Trevor J, Fazi Filippo Maria, Jackson Philip, Pike Chris, Hilton Adrian (2018) A Framework for Intelligent Metadata Adaptation in Object-Based Audio,AES E-Library pp. P11-3
Audio Engineering Society
Object-based audio can be used to customize, personalize, and optimize audio reproduction depending on the speci?c listening scenario. To investigate and exploit the bene?ts of object-based audio, a framework for intelligent metadata adaptation was developed. The framework uses detailed semantic metadata that describes the audio objects, the loudspeakers, and the room. It features an extensible software tool for real-time metadata adaptation that can incorporate knowledge derived from perceptual tests and/or feedback from perceptual meters to drive adaptation and facilitate optimal rendering. One use case for the system is demonstrated through a rule-set (derived from perceptual tests with experienced mix engineers) for automatic adaptation of object levels and positions when rendering 3D content to two- and ?ve-channel systems.
In this paper, we compare different deep neural
networks (DNN) in extracting speech signals from competing
speakers in room environments, including the conventional fullyconnected
multilayer perception (MLP) network, convolutional
neural network (CNN), recurrent neural network (RNN), and
the recently proposed capsule network (CapsNet). Each DNN
takes input of both spectral features and converted spatial
features that are robust to position mismatch, and outputs the
separation mask for target source estimation. In addition, a
psychacoustically-motivated objective function is integrated in
each DNN, which explores perceptual importance of each TF
unit in the training process. Objective evaluations are performed
on the separated sounds using the converged models, in terms
of PESQ, SDR as well as STOI. Overall, all the implemented
DNNs have greatly improved the quality and speech intelligibility
of the embedded target source as compared to the original
recordings. In particular, bidirectional RNN, either along the
temporal direction or along the frequency bins, outperforms the
other DNN structures with consistent improvement.
The intelligibility of speech in noise can be improved by
modifying the speech. But with object-based audio, there
is the possibility of altering the background sound while
leaving the speech unaltered. This may prove a less intrusive approach, affording good speech intelligibility without overly compromising the perceived sound quality. In this study, the technique of spectral weighting was applied to the background. The frequency-dependent weightings for adaptation were learnt by maximising a weighted combination of two perceptual objective metrics for speech intelligibility and audio quality. The balance between the two objective metrics was determined by the perceptual relationship between intelligibility and quality. A neural network was trained to provide a fast solution for real-time processing. Tested in a variety of background sounds and speech-to-background ratios (SBRs), the proposed method led to a large intelligibility
gain over the unprocessed baseline. Compared to an approach using constant weightings, the proposed method was able to dynamically preserve the overall audio quality better with respect to SBR changes.