2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)最新文献_第2页

A real-time example-based single-image super-resolution algorithm via cross-scale high-frequency components self-learning 基于跨尺度高频分量自学习的实时样本单图像超分辨算法

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2016-03-20 DOI: 10.1109/ICASSP.2016.7471962

Chang Su, Li Tao

引用次数: 0

Pseudo-coherence-based MVDR beamformer for speech enhancement with ad hoc microphone arrays 基于伪相干的MVDR波束形成器，用于自组织麦克风阵列语音增强

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2015-04-19 DOI: 10.1109/ICASSP.2015.7178453

Vincent Mohammad Tavakoliy, Jesper Rindom Jenseny, Mads Graesboll Christenseny, Jacob Benestyz

引用次数: 13

An outreach after-school program to introduce high-school students to electrical engineering 一个向高中生介绍电气工程的课外拓展项目

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2015-04-19 DOI: 10.1109/ICASSP.2015.7179031

Monica F. Bugalloy, Angela M. Kellyz

引用次数: 5

The convergence guarantees of a non-convex approach for sparse recovery using regularized least squares 正则最小二乘稀疏恢复非凸方法的收敛性保证

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2014-07-14 DOI: 10.1109/ICASSP.2014.6854221

Laming Chen, Yuantao Gu

引用次数: 16

Pitch adaptive training for hmm-based singing voice synthesis 基于hmm的歌唱声音合成的音调自适应训练

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2014-05-04 DOI: 10.1109/ICASSP.2014.6854062

K. Shirota, Kazuhiro Nakamura, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, K. Tokuda

{"title":"Pitch adaptive training for hmm-based singing voice synthesis","authors":"K. Shirota, Kazuhiro Nakamura, Kei Hashimoto, Keiichiro Oura, Yoshihiko Nankaku, K. Tokuda","doi":"10.1109/ICASSP.2014.6854062","DOIUrl":"https://doi.org/10.1109/ICASSP.2014.6854062","url":null,"abstract":"A statistical parametric approach to singing voice synthesis based on hidden Markov Models (HMMs) has been growing in popularity over the last few years. The spectrum, excitation, vibrato, and duration of singing voices in this approach are simultaneously modeled with context-dependent HMMs and waveforms are generated from the HMMs themselves. HMM-based singing voice synthesis systems are heavily based on the training data in performance because these systems are “corpus-based.” Therefore, HMMs corresponding to contextual factors that hardly ever appear in the training data cannot be well-trained. Pitch should especially be correctly covered since generated F0 trajectories have a great impact on the subjective quality of synthesized singing voices. We applied the method of “speaker adaptive training” (SAT) to “pitch adaptive training,” which is discussed in this paper. This technique made it possible to normalize pitch based on musical notes in the training process. The experimental results demonstrated that the proposed technique could alleviate the data sparseness problem.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"31 1","pages":"5377-5380"},"PeriodicalIF":0.0,"publicationDate":"2014-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86653818","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: 21

Auditory attention based mobile audio quality assessment 基于听觉注意的移动音频质量评估

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2014-01-01 DOI: 10.1109/ICASSP.2014.6853825

Yuhong Yang, Hongjiang Yu, R. Hu, Li Gao, Song Wang, Qing Zhai, Songbo Xie

引用次数: 0

Large deviation delay analysis of queue-aware multi-user MIMO systems with two timescale mobile-driven feedback 具有双时间尺度移动驱动反馈的队列感知多用户MIMO系统的大偏差延迟分析

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2013-05-26 DOI: 10.1109/ICASSP.2013.6638620

Junting Chen, V. Lau

{"title":"Large deviation delay analysis of queue-aware multi-user MIMO systems with two timescale mobile-driven feedback","authors":"Junting Chen, V. Lau","doi":"10.1109/ICASSP.2013.6638620","DOIUrl":"https://doi.org/10.1109/ICASSP.2013.6638620","url":null,"abstract":"Multi-user multi-input-multi-output (MU-MIMO) systems usually require users to feedback the channel state information (CSI) for scheduling. Most of the existing literature on the reduced feedback user scheduling focused on the throughput performance and the queueing delay was usually ignored. As the delay is important for real-time applications, it is desirable to have a low feedback queue-aware user scheduling algorithm for MU-MIMO systems. This paper proposes a two timescale queue-aware user scheduling algorithm, which consists of a queue-aware mobile-driven feedback filtering stage and a SINR-based user scheduling stage. The feedback policy is obtained by solving a queue-weighted optimization problem. In addition, we evaluate the associated queueing delay performance by using the large deviation analysis. The large deviation decay rate for the proposed algorithm is shown to be much larger than the CSI-only scheduling algorithm. Numerical results demonstrate the large performance gain of the proposed algorithm over the CSI-only algorithm, while the proposed one requires only a small amount of feedback.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"89 1","pages":"5036-5040"},"PeriodicalIF":0.0,"publicationDate":"2013-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84232648","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: 3

A diagonalized newton algorithm for non-negative sparse coding 非负稀疏编码的对角化牛顿算法

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2013-05-26 DOI: 10.1109/ICASSP.2013.6639080

H. V. hamme

引用次数: 1

Lossy compression of sparse histogram image 稀疏直方图图像的有损压缩

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2012-08-31 DOI: 10.1109/ICASSP.2012.6288143

M. Iwahashi, H. Kobayashi, H. Kiya

引用次数: 33

Effect of anti-aliasing filtering on the quality of speech from an HMM-based synthesizer 抗混叠滤波对基于hmm合成器语音质量的影响

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) Pub Date : 2012-08-31 DOI: 10.1109/ICASSP.2012.6288924

Y. Shiga

{"title":"Effect of anti-aliasing filtering on the quality of speech from an HMM-based synthesizer","authors":"Y. Shiga","doi":"10.1109/ICASSP.2012.6288924","DOIUrl":"https://doi.org/10.1109/ICASSP.2012.6288924","url":null,"abstract":"This paper investigates how the quality of speech produced through statistical parametric synthesis is affected by anti-aliasing filtering, i.e., low-pass filtering that is applied prior to (down-) sampling prerecorded speech at a desired rate. It has empirically been known that the frequency response of such anti-aliasing filters influences the quality of speech synthesized to a considerable degree. For the purpose of understanding such influence more clearly, in this paper we examine the spectral aspects of speech involved in the processes of HMM training and synthesis. We then propose a technique of feature extraction that can avoid producing the roll-off feature of the frequency response near the Nyquist frequency, which is found to be the major cause of speech quality degradation resulting from anti-aliasing filtering. In the technique, the spectrum is first computed from speech at a sampling rate higher than the desired rate, then it is truncated so that its frequency range above the target Nyquist frequency is discarded, and finally the truncated spectrum is converted directly into the cepstrum. Listening test results show that the proposed technique enables training HMMs efficiently with a limited number of model parameters and effectively with less artifacts in the speech synthesized at a desired sampling rate.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"93 20 1","pages":"4525-4528"},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83488176","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