2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)最新文献_第9页

Noise Prior Knowledge Learning for Speech Enhancement via Gated Convolutional Generative Adversarial Network 基于门控卷积生成对抗网络的语音增强噪声先验知识学习

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023216

Cunhang Fan, B. Liu, J. Tao, Jiangyan Yi, Zhengqi Wen, Ye Bai

{"title":"Noise Prior Knowledge Learning for Speech Enhancement via Gated Convolutional Generative Adversarial Network","authors":"Cunhang Fan, B. Liu, J. Tao, Jiangyan Yi, Zhengqi Wen, Ye Bai","doi":"10.1109/APSIPAASC47483.2019.9023216","DOIUrl":"https://doi.org/10.1109/APSIPAASC47483.2019.9023216","url":null,"abstract":"Speech enhancement generative adversarial network (SEGAN) is an end-to-end deep learning architecture, which only uses the clean speech as the training targets. However, when the signal-to-noise ratio (SNR) is very low, predicting clean speech signals could be very difficult as the speech is dominated by the noise. In order to address this problem, in this paper, we propose a gated convolutional neural network (CNN) SEGAN (GSEGAN) with noise prior knowledge learning to address this problem. The proposed model not only estimates the clean speech, but also learns the noise prior knowledge to assist the speech enhancement. In addition, gated CNN has an excellent potential for capturing long-term temporal dependencies than regular CNN. Motivated by this, we use a gated CNN architecture to acquire more detailed information at waveform level instead of regular CNN. We evaluate the proposed method GSEGAN on Voice Bank corpus. Experimental results show that the proposed method GSEGAN outperforms the SEGAN baseline, with a relative improvement of 0.7%, 28.2% and 43.9% for perceptual evaluation of speech quality (PESQ), overall Signal-to-Noise Ratio (SNRovl) and Segmental Signal-to-Noise Ratio (SNRseg), respectively.","PeriodicalId":145222,"journal":{"name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128364744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Subjective Feedback-based Neural Network Pruning for Speech Enhancement 基于主观反馈的语音增强神经网络剪枝

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023330

Fuqiang Ye, Yu Tsao, Fei Chen

{"title":"Subjective Feedback-based Neural Network Pruning for Speech Enhancement","authors":"Fuqiang Ye, Yu Tsao, Fei Chen","doi":"10.1109/APSIPAASC47483.2019.9023330","DOIUrl":"https://doi.org/10.1109/APSIPAASC47483.2019.9023330","url":null,"abstract":"Speech enhancement based on neural networks provides performance superior to that of conventional algorithms. However, the network may suffer owing to redundant parameters, which demands large unnecessary computation and power consumption. This work aimed to prune the large network by removing extra neurons and connections while maintaining speech enhancement performance. Iterative network pruning combined with network retraining was employed to compress the network based on the weight magnitude of neurons and connections. This pruning method was evaluated using a deep denoising autoencoder neural network, which was trained to enhance speech perception under nonstationary noise interference. Word correct rate was utilized as the subjective intelligibility feedback to evaluate the understanding of noisy speech enhanced by the sparse network. Results showed that the iterative pruning method combined with retraining could reduce 50% of the parameters without significantly affecting the speech enhancement performance, which was superior to the two baseline conditions of direct network pruning with network retraining and iterative network pruning without network retraining. Finally, an optimized network pruning method was proposed to implement the iterative network pruning and retraining in a greedy repetition manner, yielding a maximum pruning ratio of 80%.","PeriodicalId":145222,"journal":{"name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129756791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Urgent Voicemail Detection Focused on Long-term Temporal Variation 基于长期时间变化的紧急语音邮件检测

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023034

Hosana Kamiyama, Atsushi Ando, Ryo Masumura, Satoshi Kobashikawa, Y. Aono

{"title":"Urgent Voicemail Detection Focused on Long-term Temporal Variation","authors":"Hosana Kamiyama, Atsushi Ando, Ryo Masumura, Satoshi Kobashikawa, Y. Aono","doi":"10.1109/APSIPAASC47483.2019.9023034","DOIUrl":"https://doi.org/10.1109/APSIPAASC47483.2019.9023034","url":null,"abstract":"This paper proposes a effective urgent speech detection for voicemails focused on speech rhythm. Previous techniques use short-term features with millisecond scale (such as fundamental frequency, loudness and spectral features), and conventional techniques for urgent speech detection use also features obtained from entire speech (such as average speech rate). However, the features obtained from entire speech are too over-smoothed to explain the difference between urgent and nonurgent speech. We found that there was a difference between urgent and non-urgent speech in temporal variability related to speech rhythm. To handle the temporal variability of speech rhythm, the proposal extracts long-term temporal features. The long-term temporal features are envelope modulation spectrum and temporal statistics of Mel-frequency cepstrum coefficient with 1 sec scale. To use both features with different time scales, the proposed method integrates the long-term temporal features and the short-term features on neural networks. Our proposal yields better accuracy than the conventional methods (which uses e features obtained from entire speech); it achieves a 50.0% reduction in the error rate.","PeriodicalId":145222,"journal":{"name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129927899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DNN-based Voice Conversion with Auxiliary Phonemic Information to Improve Intelligibility of Glossectomy Patients' Speech 基于dnn的辅助音位信息语音转换提高舌切除术患者言语清晰度

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023168

Hiroki Murakami, Sunao Hara, M. Abe

{"title":"DNN-based Voice Conversion with Auxiliary Phonemic Information to Improve Intelligibility of Glossectomy Patients' Speech","authors":"Hiroki Murakami, Sunao Hara, M. Abe","doi":"10.1109/APSIPAASC47483.2019.9023168","DOIUrl":"https://doi.org/10.1109/APSIPAASC47483.2019.9023168","url":null,"abstract":"In this paper, we propose using phonemic information in addition to acoustic features to improve the intelligibility of speech uttered by patients with articulation disorders caused by a wide glossectomy. Our previous studies showed that voice conversion algorithm improves the quality of glossectomy patients' speech. However, losses in acoustic features of glossectomy patients' speech are so large that the quality of the reconstructed speech is low. To solve this problem, we explored potentials of several additional information to improve speech intelligibility. One of the candidates is phonemic information, more specifically Phoneme Labels as Auxiliary input (PLA). To combine both acoustic features and PLA, we employed a DNN-based algorithm. PLA is represented by a kind of one-of-k vector, i.e., PLA has a weight value (<1.0) that gradually changes in time axis, whereas one-of-k has a binary value (0 or 1). The results showed that the proposed algorithm reduced the mel-frequency cepstral distortion for all phonemes, and almost always improved intelligibility. Notably, the intelligibility was largely improved in phonemes /s/ and /z/, mainly because the tongue is used to sustain constriction to produces these phonemes. This indicates that PLA works well to compensate the lack of a tongue.","PeriodicalId":145222,"journal":{"name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129923561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Digital implementation of Hilbert Transform in the LCT domain associated with FIR filter 与FIR滤波器相关联的LCT域希尔伯特变换的数字实现

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023095

B. Deng, Qingshun Huang, Lin Zhang

引用次数: 0

An Alternative Solution to the Dynamically Regularized RLS Algorithm 动态正则化RLS算法的另一种解决方案

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023073

Feiran Yang, Jun Yang, F. Albu

引用次数: 1

Acoustic-Domain Self-Interference Cancellation for Full-Duplex Underwater Acoustic Communication Systems 全双工水声通信系统的声域自干扰消除

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023019

Yanyan Wang, Yingsong Li, Lu Shen, Y. Zakharov

引用次数: 1

A Survey on Applications of Deep Reinforcement Learning in Resource Management for 5G Heterogeneous Networks 深度强化学习在5G异构网络资源管理中的应用综述

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023331

Ying Loong Lee, Donghong Qin

{"title":"A Survey on Applications of Deep Reinforcement Learning in Resource Management for 5G Heterogeneous Networks","authors":"Ying Loong Lee, Donghong Qin","doi":"10.1109/APSIPAASC47483.2019.9023331","DOIUrl":"https://doi.org/10.1109/APSIPAASC47483.2019.9023331","url":null,"abstract":"Heterogeneous networks (HetNets) have been regarded as the key technology for fifth generation (5G) communications to support the explosive growth of mobile traffics. By deploying small-cells within the macrocells, the HetNets can boost the network capacity and support more users especially in the hotspot and indoor areas. Nonetheless, resource management for such networks becomes more complex compared to conventional cellular networks due to the interference arise between small-cells and macrocells, which thus making quality of service provisioning more challenging. Recent advances in deep reinforcement learning (DRL) have inspired its applications in resource management for 5G HetNets. In this paper, a survey on the applications of DRL in resource management for 5G HetNets is conducted. In particular, we review the DRL-based resource management schemes for 5G HetNets in various domains including energy harvesting, network slicing, cognitive HetNets, coordinated multipoint transmission, and big data. An insightful comparative summary and analysis on the surveyed studies is provided to shed some light on the shortcomings and research gaps in the current advances in DRL-based resource management for 5G HetNets. Last but not least, several open issues and future directions are presented.","PeriodicalId":145222,"journal":{"name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127710348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Multi-Task Based Mispronunciation Detection of Children Speech Using Multi-Lingual Information 基于多语言信息的多任务儿童语音错误检测

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023351

Linxuan Wei, Wenwei Dong, Binghuai Lin, Jinsong Zhang

{"title":"Multi-Task Based Mispronunciation Detection of Children Speech Using Multi-Lingual Information","authors":"Linxuan Wei, Wenwei Dong, Binghuai Lin, Jinsong Zhang","doi":"10.1109/APSIPAASC47483.2019.9023351","DOIUrl":"https://doi.org/10.1109/APSIPAASC47483.2019.9023351","url":null,"abstract":"In developing a Computer-Aided Pronunciation Training (CAPT) system for Chinese ESL (English as a Second Language) children, we suffered from insufficient task-specific data. To address this issue, we propose to utilize first language (L1) and second language (L2) knowledge from both adult and children data through multitask-based transfer learning according to Speech Learning Model (SLM). Experimental set-up includes the TDNN acoustic modelling using the following training data: 70 hours of English speech by American Children (AC), 100 hours by American Adults (AA), 5 hours of Chinese speech by Chinese Children (CC), and 89 hours by Chinese Adults (CA). Testing data includes 2 hours of ESL speech by Chinese children. Experimental results showed that the inclusion of AA data brought about 13% relative Detection Error Rate (DER) reduction compared to AC only. Further inclusion of CC and CA data through L1 transfer learning brought about a total of 21% relative improvement in DER. These results suggested the proposed method is effective in mitigating insufficient data problem.","PeriodicalId":145222,"journal":{"name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127070179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Pain versus Affect? An Investigation in the Relationship between Observed Emotional States and Self-Reported Pain 疼痛vs情感?观察情绪状态与自述疼痛关系的研究

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023134

F. Tsai, Yi-Ming Weng, C. Ng, Chi-Chun Lee

{"title":"Pain versus Affect? An Investigation in the Relationship between Observed Emotional States and Self-Reported Pain","authors":"F. Tsai, Yi-Ming Weng, C. Ng, Chi-Chun Lee","doi":"10.1109/APSIPAASC47483.2019.9023134","DOIUrl":"https://doi.org/10.1109/APSIPAASC47483.2019.9023134","url":null,"abstract":"Painis an internal sensation intricately intertwined with individual affect states resulting in a varied expressive behaviors multimodally. Past research have indicated that emotion is an important factor in shaping one's painful experiences and behavioral expressions. In this work, we present a study into understanding the relationship between individual emotional states and self-reported pain-levels. The analyses show that there is a significant correlation between observed valence state of an individual and his/her own self-reported pain-levels. Furthermore, we propose an emotion-enriched multitask network (EEMN) to improve self-reported pain-level recognition by leveraging the rated emotional states using multimodal expressions computed from face and speech. Our framework achieves accuracy of 70.1% and 52.1% in binary and ternary classes classification. The method improves a relative of 6.6% and 13% over previous work on the same dataset. Further, our analyses not only show that an individual's valence state is negatively correlated to the pain-level reported, but also reveal that asking observers to rate valence attribute could be related more to the self-reported pain than to rate directly on the pain intensity itself.","PeriodicalId":145222,"journal":{"name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"192 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127124488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2