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CENET: CONTENT-AWARE ENHANCED NETWORK FOR PRACTICAL SCENE PARSING 49

CENET: CONTENT-AWARE ENHANCED NETWORK FOR PRACTICAL SCENE PA...

引用

49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Song, Kai Wang, Zhengtan Dai, Huhe Zheng, Yuan Inner Mongolia Univ Hohhot Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Attention mechanisms are widely adopted in existing scene parsing methods due to their excellent performance, especially spatial self-attention. However, spatial self-attention suffers from high computational complexity, which limits the practical applications of the scene parsing methods on mobile devices with limited resources. In view of this, we propose a simple yet effective spatial attention module, namely Content-Aware Attention Module (CA2 M). CA2M is a lightweight spatial attention module that consists of several convolution and pooling operations, compared to various spatial self-attention modules. Moreover, it is able to adaptively select spatial pixel information which is helpful for scene parsing task. With CA2M, we present a Content-aware Enhanced Network for scene parsing (CENet), where CA2M is introduced into the lateral connections at four different scales, resulting in a semantic alignment at adjacent scales and an effective semantic propagation. To validate the performance of the proposed CA2 M and CENet, we conduct extensive experiments and achieve consistently improved performances on three popular benchmarks. Furthermore, we verify their generalization ability when using different baseline models and backbone networks. Code is available at https://***/ZY-IMU-CV/CENET_SK_2023.

关键词： Spatial attention image semantic segmentation encoder-decoder

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IMPROVING GRAPHEME-TO-PHONEME CONVERSION BY INVESTIGATING COPYING MECHANISM IN RECURRENT ARCHITECTURES

IMPROVING GRAPHEME-TO-PHONEME CONVERSION BY INVESTIGATING CO...

引用

IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)

作者： Niranjan, Abhishek Shaik, M. Ali Basha Samsung Res & Dev Inst Voice Intelligence Bangalore Karnataka India

ISBN: (纸本)9781728103068

Attention driven encoder-decoder architectures have become highly successful in various sequence-to-sequence learning tasks. We propose copy-augmented Bi-directional Long Short-Term Memory based encoder-decoder architecture for the Grapheme-to-Phoneme conversion. In Grapheme-to-Phoneme task, a number of character units in words possess high degree of similarity with some phoneme unit(s). Thus, we make an attempt to capture this characteristic using copy-augmented architecture. Our proposed model automatically learns to generate phoneme sequences during inference by copying source token embeddings to the decoder's output in a controlled manner. To our knowledge, this is the first time the copy-augmentation is being investigated for Grapheme-to-Phoneme conversion task. We validate our experiments over accented and non-accented publicly available CMU-Dict datasets and achieve State-of-The-Art performances in terms of both phoneme and word error rates. Further, we verify the applicability of our proposed approach on Hindi Lexicon and show that our model outperforms all recent State-of-The-Art results.

关键词： Grapheme-to-Phoneme Copy augmentation encoder-decoder attention

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A Causal U-net based Neural Beamforming Network for Real-Time Multi-Channel Speech Enhancement 22

A Causal U-net based Neural Beamforming Network for Real-Tim...

引用

Interspeech Conference

作者： Ren, Xinlei Zhang, Xu Chen, Lianwu Zheng, Xiguang Zhang, Chen Guo, Liang Yu, Bing Kuai Shou Technol Co Beijing Peoples R China

ISBN: (纸本)9781713836902

People are meeting through video conferencing more often. While single channel speech enhancement techniques are useful for the individual participants, the speech quality will be significantly degraded in large meeting rooms where the far-field and reverberate conditions are introduced. Approaches based on microphone array signal processing are proposed to explore the inter-channel correlation among the individual microphone channels. In this work, a new causal U-net based multiple-inmultiple-out structure is proposed for real-time multi-channel speech enhancement. The proposed method incorporates the traditional beamforming structure with the multi-channel causal U-net by explicitly adding a beamforming operation at the end of the neural beamformer. The proposed method has entered the INTERSPEECH Far-field Multi-Channel Speech Enhancement Challenge for Video Conferencing. With 1.97M model parameters and 0.25 real-time factor on Intel Core i7 (2.6GHz) CPU, the proposed method has outperforms the baseline system of this challenge on PESQ, Si-SNR and STOI metrics.

关键词： multi-channel speech enhancement U-NET encoder-decoder deep learning

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SDPredictNet-A Topology based SDN Neural Routing Framework with Traffic Prediction Analysis 11

SDPredictNet-A Topology based SDN Neural Routing Framework w...

引用

IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC)

作者： Sanagavarapu, Sowmya Sridhar, Sashank Anna Univ Dept Comp Sci & Engn Coll Engn Guindy Chennai 600025 Tamil Nadu India

ISBN: (数字)9781665414906

ISBN: (纸本)9781665414906

Software Defined Networking is an intelligent network management approach for monitoring and improving performance such as in cloud computing. These networks follow separation between the Forwarding layer and the Control layer in the system to enable efficient programmability to perform network configurations. The SDN Controller present in the network has complete information about the network schema and its components to update the routing information of the switches present. The dynamic nature of traffic in these networks with multi-layer switches hinder the efficiency of SDN's performance in multi-cloud environments. This paper proposes SDPredictNet, a Recurrent Neural Network framework deployed on the SDN Controller that can predict the traffic in the network and update flow tables of the higher layer switches to perform routing based on the perceived bottlenecks in the network. SDPredictNet uses a Sequence-to-Sequence model that trains on the network data congestion to forecast the traffic in the SDN which is then modelled by an Artificial Neural Network to predict the path of the packets. SDPredictNet has achieved a RMSE score of 0.07 and an accuracy of 99.88% for traffic estimation and subsequent path determination.

关键词： Software Defined Networks Traffic Prediction Multi-layer switches Deep Neural Routing LSTM Sequence to Sequence Modelling encoder-decoder

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A SHALLOW U-NET WITH SPLIT-FUSED ATTENTION MECHANISM FOR RETINAL VESSEL SEGMENTATION 30

A SHALLOW U-NET WITH SPLIT-FUSED ATTENTION MECHANISM FOR RET...

引用

30th IEEE International Conference on Image Processing (ICIP)

作者： Bhati, Amit Jain, Samir Gour, Neha Khanna, Pritee Ojha, Aparajita Werghi, Naoufel PDPM IIITDM Dept Comp Sci & Engn Jabalpur India Khalifa Univ Dept Elect Engn & Comp Sci C2PS Abu Dhabi U Arab Emirates

ISBN: (纸本)9781728198354

Extraction of retinal vascular parts is an important task in retinal disease diagnosis. Precise segmentation of the retinal vascular pattern is challenging due to its complex structure, overlapping with other anatomical structures, and crucial thin vascular structures. In recent years, complex and heavy deep learning networks have been proposed to segment retinal blood vessels accurately. However, these methods fail to detect the thin vascular structure among different patterns of thick vessels. An attention-based novel architecture is proposed to segment the thin vasculature to address this limitation. The proposed model comprises a shallow U-Net based encoder-decoder architecture with split-fuse attention (SFA) block. The proposed SFA block enables the network to identify the placement of pixels for the tree-shaped vessel patterns at their relative position during the reconstruction phase in the decoder. The attention block aggregates low-level and high-level semantic information, improving the vessel segmentation performance. Experimentation performed on publicly available fundus datasets, DRIVE, HRF, CHASE-DB1, and STARE show that the proposed method performs better than the current state-of-the-art methods. The results demonstrate the adaptability of the proposed model for clinical applications due to its low memory footprint and better performance.

关键词： Retinal Vessel Segmentation Shallow U-Net Split Fused Attention Fully Convolution Network encoder-decoder

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Study of facial generation methods after orthodontic treatment 46

Study of facial generation methods after orthodontic treatme...

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46th Annual IEEE-Computer-Society International Computers, Software, and Applications Conference (COMPSAC) - Computers, Software, and Applications in an Uncertain World

作者： Tian, Jia-Liang Zhang, Qin-Yan Li, Hai-Zhen Wang, Qing Lei, Yi Zang, Lin Gao, Xue-Mei Yang, Ji-Jiang Beijing Univ Posts & Telecommun Coll Artificial Intelligence Beijing Peoples R China Peking Univ Dept Orthodont Sch & Hosp Stomatol Beijing Peoples R China Tsinghua Univ Dept Automat Beijing Peoples R China Beijing Univ Technol Fac Informat Technol Sch Software Engn Beijing Peoples R China Pharmacovigilance Res Ctr Informat Technol & Data Xiamen Fujian Peoples R China

ISBN: (纸本)9781665488105

As the medical aesthetic market is growing rapidly in China, orthodontic treatment is becoming very common among the adolescent population. However, there are countless doctor-patient disputes due to treatment results that do not meet patients' expectations, so there is an urgent need for a method to predict treatment results. With the development of artificial intelligence technology, generative adversarial network has provided us with a new way of thinking. The purpose of this paper is to accurately predict the face of patients after orthodontic treatment by using generative adversarial network. Therefore, we designed an evaluation index to reflect the difference between the algorithm predicted image and the patient's real image. After that, we designed a network based on encoder-decoder architecture to transform the vectors in StyleGAN latent space. Finally, we carried out experiments to verify the effectiveness of the evaluation index design and the advantages of the algorithm.

关键词： Face Generation Orthodontic Treatment StyleGAN encoder-decoder

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Exploring Cross-lingual Singing Voice Synthesis Using Speech Data 12

Exploring Cross-lingual Singing Voice Synthesis Using Speech...

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12th International Symposium on Chinese Spoken Language Processing (ISCSLP)

作者： Cao, Yuewen Liu, Songxiang Kang, Shiyin Hu, Na Liu, Peng Liu, Xunying Su, Dan Yu, Dong Meng, Helen Chinese Univ Hong Kong Hong Kong Peoples R China Tencent AI Lab Shenzhen Peoples R China Huya Inc Guangzhou Peoples R China

ISBN: (纸本)9781728169941

State-of-the-art singing voice synthesis (SVS) models can generate natural singing voice of a target speaker, given his/her speaking/singing data in the same language. However, there may be challenging conditions where only speech data in a non-target language of the target speaker is available. In this paper, we present a cross-lingual SVS system that can synthesize an English speaker's singing voice in Mandarin from musical scores with only her speech data in English. The proposed cross-lingual SVS system contains four parts: a BLSTM based duration model, a pitch model, a cross-lingual acoustic model and a neural vocoder. The acoustic model employs encoder-decoder architecture conditioned on pitch, phoneme duration, speaker information and language information. An adversarially-trained speaker classifier is employed to discourage the text encodings from capturing speaker information. Objective evaluation and subjective listening tests demonstrate that the proposed cross-lingual SVS system can generate singing voice with decent naturalness and fair speaker similarity. We also find that adding singing data or multi-speaker monolingual speech data further improves generalization on pronunciation and pitch accuracy.

关键词： singing voice synthesis cross-lingual encoder-decoder adversarial loss

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Joint Maximization decoder with Neural Converters for Fully Neural Network-based Japanese Speech Recognition 20

Joint Maximization Decoder with Neural Converters for Fully ...

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Interspeech Conference

作者： Moriya, Takafumi Wang, Jian Tanaka, Tomohiro Masumura, Ryo Shinohara, Yusuke Yamaguchi, Yoshikazu Aono, Yushi NTT Corp NTT Media Intelligence Labs Tokyo Japan Univ Tokyo Grad Sch Engn Tokyo Japan

We present a novel fully neural network (FNN) -based automatic speech recognition (ASR) system that addresses the out-of-vocabulary (OOV) problem. The most common approach to the OOV problem is leveraging character/sub-word level units as output symbols. Unfortunately, this approach is not suitable for Japanese and Mandarin Chinese since they have many more grapheme sets than English. Our solution is to develop FNN-based ASR that uses a pronunciation-based unit set with dictionaries, i.e., word-to-pronunciation rules. A previous study proposed, for Mandarin Chinese, a greedy cascading decoder (GCD) that uses two neural converters, acoustic-to-pronunciation (A2P) and pronunciation-to-word (P2W) conversion models. However, to generate optimal word sequences, the previous work considered just optimal pronunciation sequences. In this paper, we propose a joint maximization decoder (JMD) that considers the joint probability of pronunciation and word in beam-search decoding. Moreover, we introduce a neural network based joint source channel model for improving A2P conversion performance. Experiments on Japanese ASR tasks demonstrate that JMD achieves better performance than GCD. Furthermore, we show the effectiveness of using just language resources to retrain the P2W conversion model.

关键词： ASR neural network encoder-decoder joint source channel model syllable based acoustic modeling

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End-to-End Multilingual Multi-Speaker Speech Recognition 20

End-to-End Multilingual Multi-Speaker Speech Recognition

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Interspeech Conference

作者： Seki, Hiroshi Hori, Takaaki Watanabe, Shinji Le Roux, Jonathan Hershey, John R. Mitsubishi Elect Res Labs MERL Cambridge MA 02139 USA Toyohashi Univ Technol Toyohashi Aichi Japan Johns Hopkins Univ Baltimore MD 21218 USA

The expressive power of end-to-end automatic speech recognition (ASR) systems enables direct estimation of a character or word label sequence from a sequence of acoustic features. Direct optimization of the whole system is advantageous because it not only eliminates the internal linkage necessary for hybrid systems, but also extends the scope of potential applications by training the model for various objectives. In this paper, we tackle the challenging task of multilingual multi-speaker ASR using such an all-in-one end-to-end system. Several multilingual ASR systems were recently proposed based on a monolithic neural network architecture without language-dependent modules, showing that modeling of multiple languages is well within the capabilities of an end-to-end framework. There has also been growing interest in multi-speaker speech recognition, which enables generation of multiple label sequences from single-channel mixed speech. In particular, a multi-speaker end-to-end ASR system that can directly model one-to-many mappings without additional auxiliary clues was recently proposed. The proposed model, which integrates the capabilities of these two systems, is evaluated using mixtures of two speakers generated by using 10 languages, including code-switching utterances.

关键词： end-to-end ASR multilingual ASR multi-speaker ASR code-switching encoder-decoder CTC

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A COMBINED FEATURE ENCODING NETWORK WITH SEMANTIC ENHANCEMENT FOR IMAGE TAMPERING FORENSICS 18th

A COMBINED FEATURE ENCODING NETWORK WITH SEMANTIC ENHANCEMEN...

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18th IFIP WG 11.9 International Conference on Digital Forensics

作者： Luo, Yuling Liang, Ce Zhang, Shunsheng Qin, Sheng Guangxi Normal Univ Elect Engn Guilin Peoples R China Guangxi Normal Univ Sch Elect Engn Guilin Peoples R China

ISBN: (纸本)9783031100789;9783031100772

Image tampering forensics is performed by analyzing images to locate the tampered regions. However, most image tampering detection methods lack locational accuracy and are effective only for specific types of tampering. To address these problems, this chapter proposes a method that employs an encoder-decoder network structure with combined multiple feature encoding to segment tampered regions of an image from untampered regions. Three features, obtained using constrained convolution, steganalysis rich model filtering and common convolution, are combined. During the encoding stage, ring residual units are used to extract features. The combination of multiple features and the ring residual units makes the proposed method most suitable for image tampering detection. Channel attention with a soft threshold function is used to reinforce semantic information in the decoding stage. Experiments with three image forensic datasets, NIST16, COVERAGE and CASIA, demonstrate that the proposed method exhibits strong performance in terms of the F1 score and localization of tampered regions.

关键词： Image tampering encoder-decoder combined features semantics

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