The Experts below are selected from a list of 3072 Experts worldwide ranked by ideXlab platform
Jiao Yishan - One of the best experts on this subject based on the ideXlab platform.
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nmf based speech and music separation in monaural speech recordings with sparseness and Temporal Continuity constraints
International Conference on Model Transformation, 2013Co-Authors: Tu Ming, Xie Xiang, Jiao YishanAbstract:This paper proposes a semi-supervised approach of speech and music separation in monaural speech recordings based on non-negative matrix factorization (NMF). Considering the scenario that the genre of background music is known, music basis vectors are randomly picked from the magnitude of short time fourier transform (STFT) of training music, while speech basis vectors are estimated by executing NMF on the magnitude of STFT of polluted speech signal. Moreover, we apply sparseness and Temporal Continuity constraints to speech and music respectively and evaluate how different constraints can influence the separation performance. The test set contains 10 Mandarin speech utterances from 10 speakers mixed with music in different speech-music ratios (SMR). The baseline is semi-supervised separation system with no constraint. The results reveal that adding Temporal Continuity constraint can improve the separation performance compared with the baseline and separation system with only sparseness constraint.
David C Hogg - One of the best experts on this subject based on the ideXlab platform.
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enhanced tracking and recognition of moving objects by reasoning about spatio Temporal Continuity
Image and Vision Computing, 2008Co-Authors: Brandon Bennett, Anthony G Cohn, Derek R Magee, David C HoggAbstract:A framework for the logical and statistical analysis and annotation of dynamic scenes containing occlusion and other uncertainties is presented. This framework consists of three elements; an object tracker module, an object recognition/classification module and a logical consistency, ambiguity and error reasoning engine. The principle behind the object tracker and object recognition modules is to reduce error by increasing ambiguity (by merging objects in close proximity and presenting multiple hypotheses). The reasoning engine deals with error, ambiguity and occlusion in a unified framework to produce a hypothesis that satisfies fundamental constraints on the spatio-Temporal Continuity of objects. Our algorithm finds a globally consistent model of an extended video sequence that is maximally supported by a voting function based on the output of a statistical classifier. The system results in an annotation that is significantly more accurate than what would be obtained by by-frame evaluation of the classifier output. The framework has been implemented and applied successfully to the analysis of team sports with a single camera.
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using spatio Temporal Continuity constraints to enhance visual tracking of moving objects
European Conference on Artificial Intelligence, 2004Co-Authors: Brandon Bennett, Anthony G Cohn, Derek R Magee, David C HoggAbstract:We present a framework for annotating dynamic scenes involving occlusion and other uncertainties. Our system comprises an object tracker, an object classifier and an algorithm for reasoning about spatio-Temporal Continuity. The principle behind the object tracking and classifier modules is to reduce error by increasing ambiguity (by merging objects in close proximity and presenting multiple hypotheses). The reasoning engine resolves error, ambiguity and occlusion to produce a most likely hypothesis, which is consistent with global spatio-Temporal Continuity constraints. The system results in improved annotation over frame-by-frame methods. It has been implemented and applied to the analysis of a team sports video.
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ECAI - Using spatio-Temporal Continuity constraints to enhance visual tracking of moving objects
2004Co-Authors: Brandon Bennett, Anthony G Cohn, Derek R Magee, David C HoggAbstract:We present a framework for annotating dynamic scenes involving occlusion and other uncertainties. Our system comprises an object tracker, an object classifier and an algorithm for reasoning about spatio-Temporal Continuity. The principle behind the object tracking and classifier modules is to reduce error by increasing ambiguity (by merging objects in close proximity and presenting multiple hypotheses). The reasoning engine resolves error, ambiguity and occlusion to produce a most likely hypothesis, which is consistent with global spatio-Temporal Continuity constraints. The system results in improved annotation over frame-by-frame methods. It has been implemented and applied to the analysis of a team sports video.
Tu Ming - One of the best experts on this subject based on the ideXlab platform.
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nmf based speech and music separation in monaural speech recordings with sparseness and Temporal Continuity constraints
International Conference on Model Transformation, 2013Co-Authors: Tu Ming, Xie Xiang, Jiao YishanAbstract:This paper proposes a semi-supervised approach of speech and music separation in monaural speech recordings based on non-negative matrix factorization (NMF). Considering the scenario that the genre of background music is known, music basis vectors are randomly picked from the magnitude of short time fourier transform (STFT) of training music, while speech basis vectors are estimated by executing NMF on the magnitude of STFT of polluted speech signal. Moreover, we apply sparseness and Temporal Continuity constraints to speech and music respectively and evaluate how different constraints can influence the separation performance. The test set contains 10 Mandarin speech utterances from 10 speakers mixed with music in different speech-music ratios (SMR). The baseline is semi-supervised separation system with no constraint. The results reveal that adding Temporal Continuity constraint can improve the separation performance compared with the baseline and separation system with only sparseness constraint.
Brandon Bennett - One of the best experts on this subject based on the ideXlab platform.
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enhanced tracking and recognition of moving objects by reasoning about spatio Temporal Continuity
Image and Vision Computing, 2008Co-Authors: Brandon Bennett, Anthony G Cohn, Derek R Magee, David C HoggAbstract:A framework for the logical and statistical analysis and annotation of dynamic scenes containing occlusion and other uncertainties is presented. This framework consists of three elements; an object tracker module, an object recognition/classification module and a logical consistency, ambiguity and error reasoning engine. The principle behind the object tracker and object recognition modules is to reduce error by increasing ambiguity (by merging objects in close proximity and presenting multiple hypotheses). The reasoning engine deals with error, ambiguity and occlusion in a unified framework to produce a hypothesis that satisfies fundamental constraints on the spatio-Temporal Continuity of objects. Our algorithm finds a globally consistent model of an extended video sequence that is maximally supported by a voting function based on the output of a statistical classifier. The system results in an annotation that is significantly more accurate than what would be obtained by by-frame evaluation of the classifier output. The framework has been implemented and applied successfully to the analysis of team sports with a single camera.
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using spatio Temporal Continuity constraints to enhance visual tracking of moving objects
European Conference on Artificial Intelligence, 2004Co-Authors: Brandon Bennett, Anthony G Cohn, Derek R Magee, David C HoggAbstract:We present a framework for annotating dynamic scenes involving occlusion and other uncertainties. Our system comprises an object tracker, an object classifier and an algorithm for reasoning about spatio-Temporal Continuity. The principle behind the object tracking and classifier modules is to reduce error by increasing ambiguity (by merging objects in close proximity and presenting multiple hypotheses). The reasoning engine resolves error, ambiguity and occlusion to produce a most likely hypothesis, which is consistent with global spatio-Temporal Continuity constraints. The system results in improved annotation over frame-by-frame methods. It has been implemented and applied to the analysis of a team sports video.
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ECAI - Using spatio-Temporal Continuity constraints to enhance visual tracking of moving objects
2004Co-Authors: Brandon Bennett, Anthony G Cohn, Derek R Magee, David C HoggAbstract:We present a framework for annotating dynamic scenes involving occlusion and other uncertainties. Our system comprises an object tracker, an object classifier and an algorithm for reasoning about spatio-Temporal Continuity. The principle behind the object tracking and classifier modules is to reduce error by increasing ambiguity (by merging objects in close proximity and presenting multiple hypotheses). The reasoning engine resolves error, ambiguity and occlusion to produce a most likely hypothesis, which is consistent with global spatio-Temporal Continuity constraints. The system results in improved annotation over frame-by-frame methods. It has been implemented and applied to the analysis of a team sports video.
Conglin Yuan - One of the best experts on this subject based on the ideXlab platform.
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speech enhancement based on constrained low rank sparse matrix decomposition integrated with Temporal Continuity regularisation
Archives of Acoustics, 2019Co-Authors: Conglin YuanAbstract:Speech enhancement in strong noise condition is a challenging problem. Low-rank and sparse matrix decomposition (LSMD) theory has been applied to speech enhancement recently and good performance was obtained. Existing LSMD algorithms consider each frame as an individual observation. However, real-world speeches usually have a Temporal structure, and their acoustic characteristics vary slowly as a function of time. In this paper, we propose a Temporal Continuity constrained low-rank sparse matrix decomposition (TCCLSMD) based speech enhancement method. In this method, speech separation is formulated as a TCCLSMD problem and Temporal Continuity constraints are imposed in the LSMD process. We develop an alternative optimisation algorithm for noisy spectrogram decomposition. By means of TCCLSMD, the recovery speech spectrogram is more consistent with the structure of the clean speech spectrogram, and it can lead to more stable and reasonable results than the existing LSMD algorithm. Experiments with various types of noises show the proposed algorithm can achieve a better performance than traditional speech enhancement algorithms, in terms of yielding less residual noise and lower speech distortion.
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A Novel Speech Enhancement Method Based on the Constraints of Temporal Continuity
2019 IEEE 3rd Information Technology Networking Electronic and Automation Control Conference (ITNEC), 2019Co-Authors: Conglin Yuan, Bao XiongAbstract:The Temporal Continuity of speech is a vital feature, whose utilizing makes speech enhancement better. By adding the feature in this paper, we present a novel speech enhancement method based on Temporal Continuity constrained low-rank sparse matrix decomposition (TCCLSMD). This approach makes up for the deficiencies of the constrained low-rank and sparse matrix decomposition (CLSMD) by leading into the Temporal Continuity constraints. The proposed approach based on the sparse matrix obtained by singular value decomposition, and the discrete sparse matrix is reduced by adding Temporal Continuity to reduce discrete isolated points, retaining more speech information and reducing the speech distortion. Under various kinds of noise settings, compared with the CLSMD method, the experimental results show that the proposed method reduces speech distortion, makes the residual noise less, and raises speech intelligibility.
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Non-negative Matrix Factorization Speech Enhancement Method Based on Constraints of Temporal Continuity
2019 IEEE 3rd Information Technology Networking Electronic and Automation Control Conference (ITNEC), 2019Co-Authors: Conglin YuanAbstract:The constrained low-rank and sparse matrix decomposition (CLSMD) method ignores the Temporal Continuity between adjacent speech frames in the process of speech enhancement, resulting in a sparse matrix generated by decomposition with isolated discrete points. Therefore, in order to improve the noise suppression ability of the speech system and improve the enhanced speech quality and intelligibility, this paper proposes a speech enhancement method based on Temporal Continuity Constraint for Non-negative Low-rank and Sparse Matrix Decomposition (TCNLSMD). In this method, in addition to adding low -rank and sparse constraints, Temporal Continuity constraints are added. The proposed method based on the sparse matrix obtained by eigenvalue decomposition of non-negative matrices and hard-threshold function estimation, the discrete sparse matrix is reduced by adding Temporal Continuity constraints to reduce discrete isolated points, retaining more speech information and reducing the enhanced speech distortion. The experimental results show that under various types of noise test conditions, compared with the current mainstream speech enhancement methods, especially with NLSMD, the proposed method improve the noise suppression capability, make the residual noise less, and improve the quality of the enhanced speech.
