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检索条件"主题词=predictive coding"

共 1801 条记录，以下是291-300 订阅

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Contrastive predictive coding Supported Factorized Variational Autoencoder For Unsupervised Learning Of Disentangled Speech Representations

Contrastive Predictive Coding Supported Factorized Variation...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Janek Ebbers Michael Kuhlmann Tobias Cord-Landwehr Reinhold Haeb-Umbach Paderborn University Paderborn Germany

In this work we address disentanglement of style and content in speech signals. We propose a fully convolutional variational autoencoder employing two encoders: a content encoder and a style encoder. To foster disentanglement, we propose adversarial contrastive predictive coding. This new disentanglement method does neither need parallel data nor any supervision. We show that the proposed technique is capable of separating speaker and content traits into the two different representations and show competitive speaker-content disentanglement performance compared to other unsupervised approaches. We further demonstrate an increased robustness of the content representation against a train-test mismatch compared to spectral features, when used for phone recognition.

关键词： Training Convolutional codes Speech coding Convolution Conferences predictive coding Robustness

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A New Secure Steganographic Method Based on predictive coding and Quantization Index Modulation

A New Secure Steganographic Method Based on Predictive Codin...

引用

International Conference of Soft Computing and Pattern Recognition (SOCPAR)

作者： Hamid Izadinia Fereshteh Sadeghi Mohammad Rahmati Computer Engineering and Information Technology Department Amirkabir University of Technology슠 Tehran Iran

Steganography is a branch in the information hiding research area which aims to conceal data transmission between two parties. In this paper a new method based on predictive coding is proposed which employs Quantization Index Modulation (QIM) for quantizing error values and embedding data simultaneously. Furthermore, a correction mechanism is proposed to preserve the histogram of the cover image and make it resistant against histogram-based attacks. To evaluate the performance of the proposed method, several experiments on gray-level images are carried out and compared with two prominent methods called Jsteg and Steganography Based on predictive coding (SBPC). The experimental results show that the proposed method achieves an efficient trade-off among imperceptibility, hiding capacity, compression ratio and robustness against malicious attacks.

关键词： Steganography predictive coding Quantization Modulation coding Design optimization Printing Integer linear programming Constraint optimization Testing Containers

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Analysing the Masked predictive coding Training Criterion for Pre-Training a Speech Representation Model

Analysing the Masked Predictive Coding Training Criterion fo...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hemant Yadav Sunayana Sitaram Rajiv Ratn Shah IIIT Delhi India Microsoft Research India

Recent developments in pre-trained speech representation utilizing self-supervised learning (SSL) have yielded exceptional results on a variety of downstream tasks. One such technique, known as masked predictive coding (MPC), has been employed by some of the most high-performing models. In this study, we investigate the impact of MPC loss on the type of information learnt at various layers in the HuBERT model, using nine probing tasks. Our findings indicate that the amount of content information learned at various layers of the HuBERT model has a positive correlation to the MPC loss. Additionally, it is also observed that any speaker-related information learned at intermediate layers of the model, is an indirect consequence of the learning process, and therefore cannot be controlled using the MPC loss. These findings may serve as inspiration for further research in the speech community, specifically in the development of new pre-training tasks or the exploration of new pre-training criterion’s that directly preserves both speaker and content information at various layers of a learnt model.

关键词： Training Correlation Speech coding Process control Self-supervised learning predictive models predictive coding

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GRASSMANNIAN predictive coding FOR LIMITED FEEDBACK MULTIUSER MIMO SYSTEMS

GRASSMANNIAN PREDICTIVE CODING FOR LIMITED FEEDBACK MULTIUSE...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Takao Inoue Robert W. Heath National Instruments Austin TX USA University of Texas Austin Austin TX USA

Grassmannian beamforming is an efficient way to quantize channel state information in multiple-input multiple-output wireless systems. Unfortunately, multiuser systems require larger codebooks since the quantization error creates residual interference that limits the sum rate performance. To reduce the feedback requirements in multiuser systems, we propose Grassmannian predictive coding to exploit temporal channel correlation. The proposed algorithm exploits the differential geometric structure of the Grassmann manifold. The difference between points, prediction, and quantization are defined using the tangent space of the Grassmann manifold. We show that with practical feedback rates, a significant sum rate improvement can be obtained as a function of the channel correlation.

关键词： Manifolds MIMO Array signal processing Prediction algorithms Correlation Quantization predictive coding

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Audio-Visual predictive coding for Self-Supervised Visual Representation Learning

Audio-Visual Predictive Coding for Self-Supervised Visual Re...

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International Conference on Pattern Recognition

作者： Mani Kumar Tellamekala Michel Valstar Michael Pound Timo Giesbrecht Computer Vision Laboratory School of Computer Science University of Nottingham UK Unilever R&D Port Sunlight UK

Self-supervised learning has emerged as a candidate approach to learn semantic visual features from unlabeled video data. In self-supervised learning, intrinsic correspondences between data points are used to define a proxy task that forces the model to learn semantic representations. Most existing proxy tasks applied to video data exploit only either intra-modal (e.g. temporal) or cross-modal (e.g. audio-visual) correspondences separately. In theory, jointly learning both these correspondences may result in richer visual features; but, as we show in this work, doing so is non-trivial in practice. To address this problem, we introduce `Audio-Visual Permutative predictive coding' (AV-PPC), a multi-task learning framework designed to fully leverage the temporal and cross-modal correspondences as natural supervision signals. In AV-PPC, the model is trained to simultaneously learn multiple intra- and cross-modal predictive coding sub-tasks. By using visual speech recognition (lip-reading) as the downstream evaluation task, we show that our proposed proxy task can learn higher quality visual features than existing proxy tasks. We also show that AV-PPC visual features are highly data-efficient. Without further finetuning, AV-PPC visual encoder achieves 80.30% spoken word classification rate on the LRW dataset, performing on par with directly supervised visual encoders that are learned from large amounts of labeled data.

关键词： Visualization Correlation Semantics Speech recognition predictive coding Streaming media predictive models

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A predictive coding Approach on Microvessel Identification via Single-Opponent Signals

A Predictive Coding Approach on Microvessel Identification v...

引用

International Congress on Image and Signal Processing

作者： Quan Wen Juan Chen Wenhao Liu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science and Technology Hangzhou Normal University Hangzhou China

ISBN: (纸本)9781479927654

In this paper, we propose a predictive coding approach to identify microvessel subimages using Single-Opponent (SO) signal. First, we simulate the signal from SO neurons in the positive red and negative cyan channel to segment microvessel regions. Second, the SO signal is convolved with a Laplacian of Gaussian (LoG) filter and then processed in a predictive coding and biased competition (PC/BC) neural network. The texture patterns from SO signal is learnt and represented by the PC/BC model. Finally, we employ Support Vector Machine (SVM) with Spatial Pyramid Matching (SPM) scheme to classify the microvessel subimages on the learnt texture patterns. We have carried out extensive experiments on the identification of microvessel subimages. The proposed method is about 10% superior in Fl-measure compared with direct classification on subimages in RGB channel.

关键词： predictive coding single-opponent microvessel identification neural network spatial pyramid matching

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Bi-APC: Bidirectional Autoregressive predictive coding for Unsupervised Pre-Training and its Application to Children's ASR

Bi-APC: Bidirectional Autoregressive Predictive Coding for U...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Ruchao Fan Amber Afshan Abeer Alwan University of California Los Angeles USA

ISBN: (纸本)9781728176055;9781728176062

We present a bidirectional unsupervised model pre-training (UPT) method and apply it to children's automatic speech recognition (ASR). An obstacle to improving child ASR is the scarcity of child speech databases. A common approach to alleviate this problem is model pre-training using data from adult speech. Pre-training can be done using supervised (SPT) or unsupervised methods, depending on the availability of annotations. Typically, SPT performs better. In this paper, we focus on UPT to address the situations when pre-training data are unlabeled. Autoregressive predictive coding (APC), a UPT method, predicts frames from only one direction, limiting its use to uni-directional pre-training. Conventional bidirectional UPT methods, however, predict only a small portion of frames. To extend the benefits of APC to bi-directional pre-training, Bi-APC is proposed. We then use adaptation techniques to transfer knowledge learned from adult speech (using the Librispeech corpus) to child speech (OGI Kids corpus). LSTM-based hybrid systems are investigated. For the uni-LSTM structure, APC obtains similar WER improvements to SPT over the baseline. When applied to BLSTM, however, APC is not as competitive as SPT, but our proposed Bi-APC has comparable improvements to SPT.

关键词： Limiting Databases Conferences Bidirectional control predictive coding predictive models Data models

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Challenges and Recent Advances in Distributed predictive coding

Challenges and Recent Advances in Distributed Predictive Cod...

引用

IEEE (first ITW conference of the year) Information Theory Workshop (ITW)

作者： Ankur Saxena Kenneth Rose Department of Electrical and Computer Engineering University of California Santa Barbara CA USA

Distributed coding of correlated sources with memory poses a number of considerable challenges that threaten practical applications, particularly (but not only) in the context of sensor networks. This problem is strongly motivated by the obvious observation that most common sources exhibit temporal correlations that may be at least as important as spatial or inter-source correlations. This paper presents an analysis of the underlying tradeoffs, paradigms for coding systems, and approaches for distributed predictive coder design optimization. Motivated by practical limitations on both complexity and delay (especially for dense sensor networks) the focus here is on predictive coding. From the source coding perspective, the most basic tradeoff (and difficulty) is due to conflicts that arise between distributed coding and prediction, wherein 'standard' distributed quantization of the prediction errors, if coupled with imposition of zero decoder drift, would drastically compromise the predictor performance and hence the ability to exploit temporal correlations. Another challenge arises from instabilities in the design of closed loop predictors, whose impact has been observed in the past, but is greatly exacerbated in the case of distributed coding. The main contribution focuses on the tradeoffs encountered within a more general paradigm where decoder drift is allowable or unavoidable, and must be effectively accounted for and controlled. We briefly review our earlier results on which we build to derive an overall design optimization method that avoids the pitfalls of naive distributed predictive quantization and produces an optimized low complexity and low delay coding system.

关键词： predictive coding Design optimization Source coding Quantization Decoding Temperature sensors Lakes Application software Delay systems Sensor fusion

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Empirical Evaluation of Ensemble and Single Model Approaches for Multilingual predictive coding

Empirical Evaluation of Ensemble and Single Model Approaches...

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IEEE International Conference on Big Data

作者： Jingchao Yang Adam Dabrowski Robert Neary Nathaniel Huber-Fliflet Fusheng Wei Qiang Mao Data & Technology Ankura Consulting Group LLC Washington D.C. USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Corporate litigation costs associated with manually reviewing documents in legal matters continue to rise, driven by the ever-expanding volumes of data generated by businesses. To address this challenge, legal teams have increasingly turned to text classification techniques, commonly referred to in the legal community as predictive modeling, predictive coding, or "Technology Assisted Review (TAR)", to identify relevant documents more efficiently during the discovery phase of legal matters. By streamlining the classification process, predictive coding has proven instrumental in minimizing both the time and cost associated with legal discovery. As more corporations expand their operations across global regions, legal matters increasingly involve multilingual datasets, introducing significant complexities for the application of predictive coding. Traditional predictive models exhibit bias towards the dominant language within the model’s training data. The prevalence of multiple languages within the same document further complicates the challenge. This can lead to inefficiencies, elevated costs, and risks of inaccurate classifications of nondominant language *** research empirically examines two methods for applying predictive coding to multilingual datasets: a single multilingual model approach and a language-specific modeling approach. This group of collaborators posits that language-specific models will outperform the single multilanguage model approach in evaluating document relevance. The research used a multilingual dataset from a recent confidential real-world legal matter containing predominantly English and Chinese document text Performance was assessed using precision and recall measures. Our results show that the ensemble approach – employing language-specific models for classifying documents in their respective languages and employing the mixed language model across all remaining documents—outperforms a single multilanguage model. The outcomes of

关键词： Accuracy Costs Law Reviews Text categorization Training data predictive models predictive coding Multilingual Ensemble learning

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Layered predictive coding of Time-Consistent Dynamic 3D Meshes using a Non-Linear Predictor

Layered Predictive Coding of Time-Consistent Dynamic 3D Mesh...

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IEEE International Conference on Image Processing

作者： Nikolce Stefanoski Patrick Klie Xiaoliang Liu Jorn Ostermann Institut für Informationsverarbeitung Leibniz Universität Hannover Hanover Germany

We present a layered predictive compression approach for time-consistent dynamic 3D meshes. The algorithm decomposes each frame of a dynamic 3D mesh in layers employing patch-based mesh simplification techniques. This layered decomposition is consistent in time. Following the predictive coding paradigm, local temporal and spatial dependencies between layers and frames are exploited for compression. Prediction is performed vertex-wise from coarse to fine layers exploiting local linear and non-linear dependencies between vertex locations for compression. It is shown that a non-linear predictive exploitation of the proposed layered configuration of vertices can improve the compression performance upon other state-of-the-art approaches by more than 15% in domains relevant for applications.

关键词： predictive coding Decoding Principal component analysis Wavelet transforms Coherence Entropy Vectors Animation Scalability Navigation

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