Publications
443 results found
Hogg A, Evers C, Naylor P, 2019, Multiple hypothesis tracking for overlapping speaker segmentation, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), Publisher: IEEE
Speaker segmentation is an essential part of any diarization system.Applications of diarization include tasks such as speaker indexing, improving automatic speech recognition (ASR) performance and making single speaker-based algorithms available for use in multi-speaker environments.This paper proposes a multiple hypothesis tracking (MHT) method that exploits the harmonic structure associated with the pitch in voiced speech in order to segment the onsets and end-points of speech from multiple, overlapping speakers. The proposed method is evaluated against a segmentation system from the literature that uses a spectral representation and is based on employing bidirectional long short term memory networks (BLSTM). The proposed method is shown to achieve comparable performance for segmenting overlapping speakers only using the pitch harmonic information in the MHT framework.
Antonello N, De Sena E, Moonen M, et al., 2019, Joint Acoustic Localization and Dereverberation Through Plane Wave Decomposition and Sparse Regularization, IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, Vol: 27, Pages: 1893-1905, ISSN: 2329-9290
Hafezi S, Moore AH, Naylor PA, 2019, Spatial consistency for multiple source direction-of-arrival estimation and source counting., Journal of the Acoustical Society of America, Vol: 146, Pages: 4592-4603, ISSN: 0001-4966
A conventional approach to wideband multi-source (MS) direction-of-arrival (DOA) estimation is to perform single source (SS) DOA estimation in time-frequency (TF) bins for which a SS assumption is valid. The typical SS-validity confidence metrics analyse the validity of the SS assumption over a fixed-size TF region local to the TF bin. The performance of such methods degrades as the number of simultaneously active sources increases due to the associated decrease in the size of the TF regions where the SS assumption is valid. A SS-validity confidence metric is proposed that exploits a dynamic MS assumption over relatively larger TF regions. The proposed metric first clusters the initial DOA estimates (one per TF bin) and then uses the members' spatial consistency as well as its cluster's spread to weight each TF bin. Distance-based and density-based clustering are employed as two alternative approaches for clustering DOAs. A noise-robust density-based clustering is also used in an evolutionary framework to propose a method for source counting and source direction estimation. The evaluation results based on simulations and also with real recordings show that the proposed weighting strategy significantly improves the accuracy of source counting and MS DOA estimation compared to the state-of-the-art.
Sharma D, Hogg A, Wang Y, et al., 2019, Non-Intrusive POLQA estimation of speech quality using recurrent neural networks, European Signal Processing Conference (EUSIPCO), Publisher: IEEE
Estimating the quality of speech without the use of a clean reference signal is a challenging problem, in part due to the time and expense required to collect sufficient training data for modern machine learning algorithms. We present a novel, non-intrusive estimator that exploits recurrent neural network architectures to predict the intrusive POLQA score of a speech signal in a short time context. The predictor is based on a novel compressed representation of modulation domain features, used in conjunction with static MFCC features. We show that the proposed method can reliably predict POLQA with a 300 ms context, achieving a mean absolute error of 0.21 on unseen data.The proposed method is trained using English speech and is shown to generalize well across unseen languages. The neural network also jointly estimates the mean voice activity detection(VAD) with an F1 accuracy score of 0.9, removing the need for an external VAD.
Harrison JR, Akers RJ, Allan SY, et al., 2019, Overview of new MAST physics in anticipation of first results from MAST Upgrade, NUCLEAR FUSION, Vol: 59, ISSN: 0029-5515
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Neo V, Evers C, Naylor P, 2019, Speech enhancement using polynomial eigenvalue decomposition, IEEE Workshop on Applications of Signal Processing to Audio and Acoustics, Publisher: IEEE
Speech enhancement is important for applications such as telecommunications, hearing aids, automatic speech recognition and voice-controlled system. The enhancement algorithms aim to reduce interfering noise while minimizing any speech distortion. In this work for speech enhancement, we propose to use polynomial matrices in order to exploit the spatial, spectral as well as temporal correlations between the speech signals received by the microphone array. Polynomial matrices provide the necessary mathematical framework in order to exploit constructively​ the spatial correlations within and between sensor pairs, as well as the spectral-temporal correlations of broadband signals, such as speech. Specifically, the polynomial eigenvalue decomposition (PEVD) decorrelates simultaneously in space, time and frequency. We then propose a PEVD-based speech enhancement algorithm. Simulations and informal listening examples have shown that our method achieves noise reduction without introducing artefacts into the enhanced signal for white, babble and factory noise conditions between -10 dB to 30 dB SNR.
Neo V, Naylor PA, 2019, Second order sequential best rotation algorithm with householder reduction for polynomial matrix eigenvalue decomposition, IEEE International Conference on Acoustics, Speech and Signal Processing, Publisher: IEEE, Pages: 8043-8047, ISSN: 0736-7791
The Second-order Sequential Best Rotation (SBR2) algorithm, usedfor Eigenvalue Decomposition (EVD) on para-Hermitian polynomialmatrices typically encountered in wideband signal processingapplications like multichannel Wiener filtering and channel coding,involves a series of delay and rotation operations to achieve diagonalisation.In this paper, we proposed the use of Householder transformationsto reduce polynomial matrices to tridiagonal form beforezeroing the dominant element with rotation. Similar to performingHouseholder reduction on conventional matrices, our methodenables SBR2 to converge in fewer iterations with smaller orderof polynomial matrix factors because more off-diagonal Frobeniusnorm(F-norm) could be transferred to the main diagonal at everyiteration. A reduction in the number of iterations by 12.35% and0.1% improvement in reconstruction error is achievable.
Moore AH, de Haan JM, Pedersen MS, et al., 2019, Personalized signal-independent beamforming for binaural hearing aids, Journal of the Acoustical Society of America, Vol: 145, Pages: 2971-2981, ISSN: 0001-4966
The effect of personalized microphone array calibration on the performance of hearing aid beamformers under noisy reverberant conditions is studied. The study makes use of a new, publicly available, database containing acoustic transfer function measurements from 29 loudspeakers arranged on a sphere to a pair of behind-the-ear hearing aids in a listening room when worn by 27 males, 14 females, and 4 mannequins. Bilateral and binaural beamformers are designed using each participant's hearing aid head-related impulse responses (HAHRIRs). The performance of these personalized beamformers is compared to that of mismatched beamformers, where the HAHRIR used for the design does not belong to the individual for whom performance is measured. The case where the mismatched HAHRIR is that of a mannequin is of particular interest since it represents current practice in commercially available hearing aids. The benefit of personalized beamforming is assessed using an intrusive binaural speech intelligibility metric and in a matrix speech intelligibility test. For binaural beamforming, both measures demonstrate a statistically signficant (p < 0.05) benefit of personalization. The benefit varies substantially between individuals with some predicted to benefit by as much as 1.5 dB.
Hogg A, Naylor P, Evers C, 2019, Speaker change detection using fundamental frequency with application to multi-talker segmentation, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE
This paper shows that time varying pitch properties can be used advantageously within the segmentation step of a multi-talker diarization system. First a study is conducted to verify that changes in pitch are strong indicators of changes in the speaker. It is then highlighted that an individual’s pitch is smoothly varying and, therefore, can be predicted by means of a Kalman filter. Subsequently it is shown that if the pitch is not predictable then this is most likely due to a change in the speaker. Finally, a novel system is proposed that uses this approach of pitch prediction for speaker change detection. This system is then evaluated against a commonly used MFCC segmentation system. The proposed system is shown to increase the speaker change detection rate from 43.3% to 70.5% on meetings in the AMI corpus. Therefore, there are two equally weighted contributions in this paper: 1. We address the question of whether a change in pitch is a reliable estimator of a speaker change in multi-talk meeting audio. 2. We develop a method to extract such speaker changes and test them on a widely available meeting corpus.
Moore A, Xue W, Naylor P, et al., 2019, Noise covariance matrix estimation for rotating microphone arrays, IEEE/ACM Transactions on Audio, Speech and Language Processing, Vol: 27, Pages: 519-530, ISSN: 2329-9290
The noise covariance matrix computed between the signals from a microphone array is used in the design of spatial filters and beamformers with applications in noise suppression and dereverberation. This paper specifically addresses the problem of estimating the covariance matrix associated with a noise field when the array is rotating during desired source activity, as is common in head-mounted arrays. We propose a parametric model that leads to an analytical expression for the microphone signal covariance as a function of the array orientation and array manifold. An algorithm for estimating the model parameters during noise-only segments is proposed and the performance shown to be improved, rather than degraded, by array rotation. The stored model parameters can then be used to update the covariance matrix to account for the effects of any array rotation that occurs when the desired source is active. The proposed method is evaluated in terms of the Frobenius norm of the error in the estimated covariance matrix and of the noise reduction performance of a minimum variance distortionless response beamformer. In simulation experiments the proposed method achieves 18 dB lower error in the estimated noise covariance matrix than a conventional recursive averaging approach and results in noise reduction which is within 0.05 dB of an oracle beamformer using the ground truth noise covariance matrix.
Gannot S, Naylor PA, 2019, Highlights from the Audio and Acoustic Signal Processing Technical Committee [In the Spotlight], IEEE Signal Processing Magazine, Vol: 36, ISSN: 1053-5888
© 1991-2012 IEEE. The IEEE Audio and Acoustic Signal Processing Technical Committee (AASP TC) is one of 13 TCs in the IEEE Signal Processing Society. Its mission is to support, nourish, and lead scientific and technological development in all areas of AASP. These areas are currently seeing increased levels of interest and significant growth, providing a fertile ground for a broad range of specific and interdisciplinary research and development. Ranging from array processing for microphones and loudspeakers to music genre classification, from psychoacoustics to machine learning (ML), from consumer electronics devices to blue-sky research, this scope encompasses countless technical challenges and many hot topics. The TC has roughly 30 elected volunteer members drawn equally from leading academic and industrial organizations around the world, unified by the common aim of offering their expertise in the service of the scientific community.
Moore A, de Haan JM, Pedersen MS, et al., 2019, Personalized {HRTF}s for hearing aids, ELOBES2019
Brookes D, Lightburn L, Moore A, et al., 2019, Mask-assisted speech enhancement for binaural hearing aids, ELOBES2019
Xue W, Moore AH, Brookes M, et al., 2018, Modulation-domain parametric multichannel kalman filtering for speech enhancement, European Signal Processing Conference (EUSIPCO), Publisher: IEEE, Pages: 2509-2513, ISSN: 2076-1465
The goal of speech enhancement is to reduce the noise signal while keeping the speech signal undistorted. Recently we developed the multichannel Kalman filtering (MKF) for speech enhancement, in which the temporal evolution of the speech signal and the spatial correlation between multichannel observations are jointly exploited to estimate the clean signal. In this paper, we extend the previous work to derive a parametric MKF (PMKF), which incorporates a controlling factor to achieve the trade-off between the speech distortion and noise reduction. The controlling factor weights between the speech distortion and noise reduction related terms in the cost function of PMKF, and based on the minimum mean squared error (MMSE) criterion, the optimal PMKF gain is derived. We analyse the performance of the proposed PMKF and show the differences with the speech distortion weighted multichannel Wiener filter (SDW-MWF). We conduct experiments in different noisy conditions to evaluate the impact of the controlling factor on the noise reduction performance, and the results demonstrate the effectiveness of the proposed method.
Moore AH, Lightburn L, Xue W, et al., 2018, Binaural mask-informed speech enhancement for hearing aids with head tracking, International Workshop on Acoustic Signal Enhancement (IWAENC 2018), Publisher: IEEE, Pages: 461-465
An end-to-end speech enhancement system for hearing aids is pro-posed which seeks to improve the intelligibility of binaural speechin noise during head movement. The system uses a reference beam-former whose look direction is informed by knowledge of the headorientation and the a priori known direction of the desired source.From this a time-frequency mask is estimated using a deep neuralnetwork. The binaural signals are obtained using bilateral beam-formers followed by a classical minimum mean square error speechenhancer, modified to use the estimated mask as a speech presenceprobability prior. In simulated experiments, the improvement in abinaural intelligibility metric (DBSTOI) given by the proposed sys-tem relative to beamforming alone corresponds to an SNR improve-ment of 4 to 6 dB. Results also demonstrate the individual contribu-tions of incorporating the mask and the head orientation-aware beamsteering to the proposed system.
Sharma D, Nour-Eldin A, Harding P, et al., 2018, ROBUST FEATURE EXTRACTION FROM AD-HOC MICROPHONES FOR MEETING DIARIZATION, 16th International Workshop on Acoustic Signal Enhancement (IWAENC), Publisher: IEEE, Pages: 296-300, ISSN: 2639-4316
Evers C, Loellmann H, Mellmann H, et al., 2018, LOCATA challenge - evaluation tasks and measures, International Workshop on Acoustic Signal Enhancement (IWAENC 2018), Publisher: IEEE
Sound source localization and tracking algorithms provide estimatesof the positional information about active sound sources in acous-tic environments. Despite substantial advances and significant in-terest in the research community, a comprehensive benchmarkingcampaign of the various approaches using a common database ofaudio recordings has, to date, not been performed. The aim of theIEEE-AASP Challenge on sound source localization and tracking(LOCATA) is to objectively benchmark state-of-the-art localizationand tracking algorithms using an open-access data corpus of record-ings for scenarios typically encountered in audio and acoustic signalprocessing applications. The challenge tasks range from the local-ization of a single source with a static microphone array to trackingof multiple moving sources with a moving microphone array. Thispaper provides an overview of the challenge tasks, describes the per-formance measures used for evaluation of the LOCATA Challenge,and presents baseline results for the development dataset.
Moore AH, Xue W, Naylor PA, et al., 2018, Estimation of the Noise Covariance Matrix for Rotating Sensor Arrays, 52nd Asilomar Conference on Signals, Systems, and Computers, Publisher: IEEE, Pages: 1936-1941, ISSN: 1058-6393
Xue W, Moore A, Brookes DM, et al., 2018, Modulation-domain multichannel Kalman filtering for speech enhancement, IEEE/ACM Transactions on Audio, Speech and Language Processing, Vol: 26, Pages: 1833-1847, ISSN: 2329-9290
Compared with single-channel speech enhancement methods, multichannel methods can utilize spatial information to design optimal filters. Although some filters adaptively consider second-order signal statistics, the temporal evolution of the speech spectrum is usually neglected. By using linear prediction (LP) to model the inter-frame temporal evolution of speech, single-channel Kalman filtering (KF) based methods have been developed for speech enhancement. In this paper, we derive a multichannel KF (MKF) that jointly uses both interchannel spatial correlation and interframe temporal correlation for speech enhancement. We perform LP in the modulation domain, and by incorporating the spatial information, derive an optimal MKF gain in the short-time Fourier transform domain. We show that the proposed MKF reduces to the conventional multichannel Wiener filter if the LP information is discarded. Furthermore, we show that, under an appropriate assumption, the MKF is equivalent to a concatenation of the minimum variance distortion response beamformer and a single-channel modulation-domain KF and therefore present an alternative implementation of the MKF. Experiments conducted on a public head-related impulse response database demonstrate the effectiveness of the proposed method.
Yiallourides C, Moore AH, Auvinet E, et al., 2018, Acoustic Analysis and Assessment of the Knee in Osteoarthritis During Walking, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 281-285
We examine the relation between the sounds emitted by the knee joint during walking and its condition, with particular focus on osteoarthritis, and investigate their potential for noninvasive detection of knee pathology. We present a comparative analysis of several features and evaluate their discriminant power for the task of normal-abnormal signal classification. We statistically evaluate the feature distributions using the two-sample Kolmogorov-Smirnov test and the Bhattacharyya distance. We propose the use of 11 statistics to describe the distributions and test with several classifiers. In our experiments with 249 normal and 297 abnormal acoustic signals from 40 knees, a Support Vector Machine with linear kernel gave the best results with an error rate of 13.9%.
Xue W, Moore A, Brookes DM, et al., 2018, Multichannel kalman filtering for speech ehnancement, IEEE Intl Conf on Acoustics, Speech and Signal Processing, Publisher: IEEE, ISSN: 2379-190X
The use of spatial information in multichannel speech enhancement methods is well established but information associated with the temporal evolution of speech is less commonly exploited. Speech signals can be modelled using an autoregressive process in the time-frequency modulation domain, and Kalman filtering based speech enhancement algorithms have been developed for single-channel processing. In this paper, a multichannel Kalman filter (MKF) for speech enhancement is derived that jointly considers the multichannel spatial information and the temporal correlations of speech. We model the temporal evolution of speech in the modulation domain and, by incorporating the spatial information, an optimal MKF gain is derived in the short-time Fourier transform domain. We also show that the proposed MKF becomes a conventional multichannel Wiener filter if the temporal information is discarded. Experiments using the signals generated from a public head-related impulse response database demonstrate the effectiveness of the proposed method in comparison to other techniques.
Moore AH, Naylor P, Brookes DM, 2018, Room identification using frequency dependence of spectral decay statistics, IEEE Int. Conf. on Acoustics, Speech and Signal Processing, Publisher: Institute of Electrical and Electronics Engineers Inc., Pages: 6902-6906, ISSN: 0736-7791
A method for room identification is proposed based on the reverberation properties of multichannel speech recordings. The approach exploits the dependence of spectral decay statistics on the reverberation time of a room. The average negative-side variance within 1/3-octave bands is proposed as the identifying feature and shown to be effective in a classification experiment. However, negative-side variance is also dependent on the direct-to-reverberant energy ratio. The resulting sensitivity to different spatial configurations of source and microphones within a room are mitigated using a novel reverberation enhancement algorithm. A classification experiment using speech convolved with measured impulse responses and contaminated with environmental noise demonstrates the effectiveness of the proposed method, achieving 79% correct identification in the most demanding condition compared to 40% using unenhanced signals.
Antonello N, De Sena E, Moonen M, et al., 2018, JOINT SOURCE LOCALIZATION AND DEREVERBERATION BY SOUND FIELD INTERPOLATION USING SPARSE REGULARIZATION, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 6892-6896
In this paper, source localization and dereverberation are formulated jointly as an inverse problem. The inverse problem consists in the interpolation of the sound field measured by a set of microphones by matching the recorded sound pressure with that of a particular acoustic model. This model is based on a collection of equivalent sources creating either spherical or plane waves. In order to achieve meaningful results, spatial, spatio-temporal and spatio-spectral sparsity can be promoted in the signals originating from the equivalent sources. The inverse problem consists of a large-scale optimization problem that is solved using a first order matrix-free optimization algorithm. It is shown that once the equivalent source signals capable of effectively interpolating the sound field are obtained, they can be readily used to localize a speech sound source in terms of Direction of Arrival (DOA) and to perform dereverberation in a highly reverberant environment.
Evers C, Naylor PA, 2018, Acoustic SLAM, IEEE/ACM Transactions on Audio, Speech and Language Processing, Vol: 26, Pages: 1484-1498, ISSN: 2329-9290
An algorithm is presented that enables devices equipped with microphones, such as robots, to move within their environment in order to explore, adapt to and interact with sound sources of interest. Acoustic scene mapping creates a 3D representation of the positional information of sound sources across time and space. In practice, positional source information is only provided by Direction-of-Arrival (DoA) estimates of the source directions; the source-sensor range is typically difficult to obtain. DoA estimates are also adversely affected by reverberation, noise, and interference, leading to errors in source location estimation and consequent false DoA estimates. Moroever, many acoustic sources, such as human talkers, are not continuously active, such that periods of inactivity lead to missing DoA estimates. Withal, the DoA estimates are specified relative to the observer's sensor location and orientation. Accurate positional information about the observer therefore is crucial. This paper proposes Acoustic Simultaneous Localization and Mapping (aSLAM), which uses acoustic signals to simultaneously map the 3D positions of multiple sound sources whilst passively localizing the observer within the scene map. The performance of aSLAM is analyzed and evaluated using a series of realistic simulations. Results are presented to show the impact of the observer motion and sound source localization accuracy.
Evers C, Habets EAP, Gannot S, et al., 2018, DoA reliability for distributed acoustic tracking, IEEE Signal Processing Letters, Vol: 25, Pages: 1320-1324, ISSN: 1070-9908
Distributed acoustic tracking estimates the trajectories of source positions using an acoustic sensor network. As it is often difficult to estimate the source-sensor range from individual nodes, the source positions have to be inferred from Direction of Arrival (DoA) estimates. Due to reverberation and noise, the sound field becomes increasingly diffuse with increasing source-sensor distance, leading to decreased DoA estimation accuracy. To distinguish between accurate and uncertain DoA estimates, this paper proposes to incorporate the Coherent-to-Diffuse Ratio as a measure of DoA reliability for single-source tracking. It is shown that the source positions therefore can be probabilistically triangulated by exploiting the spatial diversity of all nodes.
Dawson PJ, De Sena E, Naylor PA, 2018, An acoustic image-source characterisation of surface profiles, 2018 26th European Signal Processing Conference (EUSIPCO), Publisher: IEEE, Pages: 2130-2134, ISSN: 2076-1465
The image-source method models the specular reflection from a plane by means of a secondary source positioned at the source's reflected image. The method has been widely used in acoustics to model the reverberant field of rectangular rooms, but can also be used for general-shaped rooms and non-flat reflectors. This paper explores the relationship between the physical properties of a non-flat reflector and the statistical properties of the associated cloud of image-sources. It is shown here that the standard deviation of the image-sources is strongly correlated with the ratio between depth and width of the reflector's spatial features.
Löllmann HW, Evers C, Schmidt A, et al., 2018, The LOCATA challenge data corpus for acoustic source localization and tracking, IEEE Sensor Array and Multichannel Signal Processing Workshop 2018, Publisher: IEEE, ISSN: 2151-870X
Algorithms for acoustic source localization andtracking are essential for a wide range of applications suchas personal assistants, smart homes, tele-conferencing systems,hearing aids, or autonomous systems. Numerous algorithms havebeen proposed for this purpose which, however, are not evaluatedand compared against each other by using a common database sofar. The IEEE-AASP Challenge on sound source localization andtracking (LOCATA) provides a novel, comprehensive data corpusfor the objective benchmarking of state-of-the-art algorithmson sound source localization and tracking. The data corpuscomprises six tasks ranging from the localization of a singlestatic sound source with a static microphone array to the trackingof multiple moving speakers with a moving microphone array.It contains real-world multichannel audio recordings, obtainedby hearing aids, microphones integrated in a robot head, aplanar and a spherical microphone array in an enclosed acousticenvironment as well as positional information about the involvedarrays and sound sources represented by moving human talkersor static loudspeakers.
Hafezi S, Moore AH, Naylor PA, 2018, ROBUST SOURCE COUNTING AND ACOUSTIC DOA ESTIMATION USING DENSITY-BASED CLUSTERING, 10th IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM), Publisher: IEEE, Pages: 395-399, ISSN: 1551-2282
Evers C, Naylor PA, 2018, Optimized self-localization for SLAM in dynamic scenes using probability hypothesis density filters, IEEE Transactions on Signal Processing, Vol: 66, Pages: 863-878, ISSN: 1053-587X
In many applications, sensors that map the positions of objects in unknown environments are installed on dynamic platforms. As measurements are relative to the observer's sensors, scene mapping requires accurate knowledge of the observer state. However, in practice, observer reports are subject to positioning errors. Simultaneous localization and mapping addresses the joint estimation problem of observer localization and scene mapping. State-of-the-art approaches typically use visual or optical sensors and therefore rely on static beacons in the environment to anchor the observer estimate. However, many applications involving sensors that are not conventionally used for Simultaneous Localization and Mapping (SLAM) are affected by highly dynamic scenes, such that the static world assumption is invalid. This paper proposes a novel approach for dynamic scenes, called GEneralized Motion (GEM) SLAM. Based on probability hypothesis density filters, the proposed approach probabilistically anchors the observer state by fusing observer information inferred from the scene with reports of the observer motion. This paper derives the general, theoretical framework for GEM-SLAM, and shows that it generalizes existing Probability Hypothesis Density (PHD)-based SLAM algorithms. Simulations for a model-specific realization using range-bearing sensors and multiple moving objects highlight that GEM-SLAM achieves significant improvements over three benchmark algorithms.
Galand M, 2018, Foreword, Publisher: REVUE BELGE PHILOLOGIE HISTOIRE, Pages: 5-+, ISSN: 0035-0818
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