2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)最新文献_第8页

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

Unsupervised Pronunciation Fluency Scoring by infoGan 由infoGan提供的无监督发音流利度评分

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023010

Wenwei Dong, Yanlu Xie, Binghuai Lin

引用次数: 1

Delving into the Methods of Coverless Image Steganography 无覆盖图像隐写方法研究

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023053

Koi Yee Ng, Simying Ong, Koksheik Wong

引用次数: 2

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

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

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

A morpheme sequence and convolutional neural network based Kazakh text classification 基于语素序列和卷积神经网络的哈萨克语文本分类

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023280

Sardar Parhat, Gao Ting, Mijit Ablimit, A. Hamdulla

引用次数: 1

Robust Demixing Filter Update Algorithm Based on Microphone-wise Coordinate Descent for Independent Deeply Learned Matrix Analysis 基于麦克风坐标下降的独立深度学习矩阵分析鲁棒去混滤波器更新算法

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023032

Naoki Makishima, Norihiro Takamune, H. Saruwatari, Daichi Kitamura, Yu Takahashi, Kazunobu Kondo

{"title":"Robust Demixing Filter Update Algorithm Based on Microphone-wise Coordinate Descent for Independent Deeply Learned Matrix Analysis","authors":"Naoki Makishima, Norihiro Takamune, H. Saruwatari, Daichi Kitamura, Yu Takahashi, Kazunobu Kondo","doi":"10.1109/APSIPAASC47483.2019.9023032","DOIUrl":"https://doi.org/10.1109/APSIPAASC47483.2019.9023032","url":null,"abstract":"In this paper, we propose a robust demixing filter update algorithm for audio source separation, which is the task of recovering source signals from multichannel mixtures observed in a microphone array. Recently, independent deeply learned matrix analysis (IDLMA) has been proposed as a state-of-the-art separation method. IDLMA utilizes the deep neural network (DNN) inference of source models and the blind estimation of demixing filters based on sources' independence. In conventional IDLMA, iterative projection (IP) is exploited to estimate the demixing filters. Although IP is a fast algorithm, when a specific source model is not accurate owing to an unfavorable SNR condition, the subsequent update of filters will fail. This is because IP updates the demixing filters in a sourcewise manner, where only one source model is used for each update. In this paper, we derive a new microphone-wise update algorithm that exploits all information of the source models simultaneously for each update. The microphone-wise update problem cannot be solved by IP, but instead, a new type of vectorwise coordinate descent algorithm is introduced into the proposed algorithm to realize convergence-guaranteed parameter estimation. Experimental results show that the proposed update algorithm achieves better separation performance than IP.","PeriodicalId":145222,"journal":{"name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"56 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":"116041385","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

A Hue Correction Scheme Based on Constant-Hue Plane for Color Image Enhancement 一种基于恒色相平面的彩色图像增强色相校正方案

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023061

Yuma Kinoshita, Kouki Seo, H. Kiya

{"title":"A Hue Correction Scheme Based on Constant-Hue Plane for Color Image Enhancement","authors":"Yuma Kinoshita, Kouki Seo, H. Kiya","doi":"10.1109/APSIPAASC47483.2019.9023061","DOIUrl":"https://doi.org/10.1109/APSIPAASC47483.2019.9023061","url":null,"abstract":"In this paper, we propose a novel hue correction scheme based on constant-hue plane in the RGB color space for color image enhancement. A number of hue-preserving image enhancement methods have already been proposed. Although these methods can preserve hue, these methods cannot be applied to the state-of-the-art enhancement methods such as deep-learning based ones. We therefore generalize a hue-preserving method based on the constant-hue plane in this paper. This generalization derives our novel hue correction scheme. In the proposed scheme, any existing image enhancement method including deep-learning based ones can be used to enhance images. The hue distortion due to the enhancement is then removed by replacing the maximally saturated colors of an enhanced image with those of the corresponding input one. Experimental results show that the proposed scheme is effective to suppress the hue distortion due to two color enhancement methods including a deep-learning based one. Furthermore, objective quality evaluations demonstrate that the proposed scheme can maintain the performance of image enhancement methods.","PeriodicalId":145222,"journal":{"name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"79 3 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":"116466270","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: 1