2015 IEEE International Conference on Digital Signal Processing (DSP)最新文献_第5页

Human-centered favorite music estimation: EEG-based extraction of audio features reflecting individual preference 以人为中心的最喜欢的音乐估计:基于脑电图的提取反映个人偏好的音频特征

2015 IEEE International Conference on Digital Signal Processing (DSP) Pub Date : 2015-07-21 DOI: 10.1109/ICDSP.2015.7251990

Ryosuke Sawata, Takahiro Ogawa, M. Haseyama

{"title":"Human-centered favorite music estimation: EEG-based extraction of audio features reflecting individual preference","authors":"Ryosuke Sawata, Takahiro Ogawa, M. Haseyama","doi":"10.1109/ICDSP.2015.7251990","DOIUrl":"https://doi.org/10.1109/ICDSP.2015.7251990","url":null,"abstract":"This paper presents a human-centered method for favorite music estimation using EEG-based audio features. In order to estimate user's favorite musical pieces, our method utilizes his/her EEG signals for calculating new audio features suitable for representing the user's music preference. Specifically, projection, which transforms original audio features into the features reflecting the preference, is calculated by applying kernel Canonical Correlation Analysis (CCA) to the audio features and the EEG features which are extracted from the user's EEG signals during listening to favorite musical pieces. By using the obtained projection, the new EEG-based audio features can be derived since this projection provides the best correlation between the user's EEG signals and their corresponding audio signals. Thus, successful estimation of user's favorite musical pieces via a Support Vector Machine (SVM) classifier using the new audio features becomes feasible. Since our method does not need acquisition of EEG signals for obtaining new audio features from new musical pieces after calculating the projection, this indicates the high practicability of our method. Experimental results show that our method outperforms methods using original audio features or EEG features.","PeriodicalId":216293,"journal":{"name":"2015 IEEE International Conference on Digital Signal Processing (DSP)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116185670","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: 12

2-D DOA estimation with virtual sensors 基于虚拟传感器的二维方位估计

2015 IEEE International Conference on Digital Signal Processing (DSP) Pub Date : 2015-07-21 DOI: 10.1109/ICDSP.2015.7252018

Yongzhi Liu, Zhiping Lin

引用次数: 0

A new framework based on sparse representation applied to monitor video 一种基于稀疏表示的新框架应用于视频监控

2015 IEEE International Conference on Digital Signal Processing (DSP) Pub Date : 2015-07-21 DOI: 10.1109/ICDSP.2015.7252039

Qianru Jiang, Zeru Lu, Sheng Li, Gang Li, Huang Bai, Tao Hong

引用次数: 0

Root cause localization on power networks 电力网络的根本原因定位

2015 IEEE International Conference on Digital Signal Processing (DSP) Pub Date : 2015-07-21 DOI: 10.1109/ICDSP.2015.7251975

Zhen Chen, Kai Zhu, Lei Ying

引用次数: 0

An image super resolution reconstruction algorithm based on Undecimated Morphological Wavelet 一种基于未消差形态小波的图像超分辨率重建算法

2015 IEEE International Conference on Digital Signal Processing (DSP) Pub Date : 2015-07-21 DOI: 10.1109/ICDSP.2015.7251944

Wei Liu, Yongsheng Liang, Zeng-Bin Chen, Jihong Zhang

引用次数: 2

Streaming media transmission system based on crowd participatory network oriented by remote medical 面向远程医疗的基于人群参与式网络的流媒体传输系统

2015 IEEE International Conference on Digital Signal Processing (DSP) Pub Date : 2015-07-21 DOI: 10.1109/ICDSP.2015.7251332

Lei Tan, Huiting Fan, Yawei Jia, Weikang Rui, Jing Guo, Qing Wang

引用次数: 2

An efficient FIR filtering technique for processing non-uniformly sampled signal 一种处理非均匀采样信号的高效FIR滤波技术

2015 IEEE International Conference on Digital Signal Processing (DSP) Pub Date : 2015-07-21 DOI: 10.1109/ICDSP.2015.7251855

W. Ye, Y. Yu

引用次数: 6

Low-power and high-performance design of OpenGL ES 2.0 graphics processing unit for mobile applications 面向移动应用的OpenGL ES 2.0图形处理单元的低功耗高性能设计

2015 IEEE International Conference on Digital Signal Processing (DSP) Pub Date : 2015-07-21 DOI: 10.1109/ICDSP.2015.7251840

Shen-Fu Hsiao, Shun-Jie Li, K. Tsao

{"title":"Low-power and high-performance design of OpenGL ES 2.0 graphics processing unit for mobile applications","authors":"Shen-Fu Hsiao, Shun-Jie Li, K. Tsao","doi":"10.1109/ICDSP.2015.7251840","DOIUrl":"https://doi.org/10.1109/ICDSP.2015.7251840","url":null,"abstract":"Low-Power High-Speed graphics processing unit (GPU) is one of the key hardware components in modern mobile devices such as smart phones. This paper presents several methods to improve the execution performance and reduce the power of a previously designed programmable GPU for OpenGL ES 2.0 specification. The GPU has a vectored and single-instruction-multiple-data (SIMD) architecture that can execute a maximum of four concurrent scalar operations, and supports the functions of swizzle and write mask. Besides, a special function unit (SFU) is included in the GPU to compute complicate arithmetic functions such as reciprocal square-root, logarithm and exponential. As per performance improvements, hazard control and data forwarding mechanism are added in the GPU architecture to reduce the stalls caused by data dependency. Furthermore, the execution pipeline is re-designed so that the number of execution pipeline stages is reduced with more balanced delay in each stage. For power reduction, this paper presents several methods that disenable unused execution and/or memory units. The memory units are also partitioned into several banks so that only a small portion of memory is active during memory access. According to the experimental results, the new GPU designs have significant improvement in performance and power compared with the prior design.","PeriodicalId":216293,"journal":{"name":"2015 IEEE International Conference on Digital Signal Processing (DSP)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126759282","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: 8

FPGA implementation of Shaped Offset QPSK modulator 形状偏置QPSK调制器的FPGA实现

2015 IEEE International Conference on Digital Signal Processing (DSP) Pub Date : 2015-07-21 DOI: 10.1109/ICDSP.2015.7251984

D. Rieth, C. Heller, G. Ascheid

引用次数: 9

Enabling R-peak detection in wearable ECG: Combining matched filtering and Hilbert transform 实现可穿戴心电r峰检测:结合匹配滤波和希尔伯特变换

2015 IEEE International Conference on Digital Signal Processing (DSP) Pub Date : 2015-07-21 DOI: 10.1109/ICDSP.2015.7251845

Theerasak Chanwimalueang, W. V. Rosenberg, D. Mandic

{"title":"Enabling R-peak detection in wearable ECG: Combining matched filtering and Hilbert transform","authors":"Theerasak Chanwimalueang, W. V. Rosenberg, D. Mandic","doi":"10.1109/ICDSP.2015.7251845","DOIUrl":"https://doi.org/10.1109/ICDSP.2015.7251845","url":null,"abstract":"Precise detection of R-peaks is a prerequisite in real-world ECG applications - this is particularly critical for wearable ECG where sensors are typically low resolution and embedded. Such recorded ECG data are typically contaminated by noise, motion artefacts, unbalanced skin-electrode impedance and other physiological signals. These affect the quality of R-peak detection and can consequently lead to failure in the evaluation of physiological functions or a misinterpretation of the state of the body, such as in monitoring stress. While numerous methods for R-peak detection are available for stationary and comparably noise-free ECG, robust DSP software for wearable devices is still emerging. To this end, a new approach which combines matched filtering and Hilbert transform is proposed. The RR-intervals and cross-correlation are used in conjunction to not only automatically locate the R-peaks but also to display the candidate ambiguous peaks via an interactive graphical user interface. The performance of the proposed approach is compared to the well-known Pan-Tompkins algorithm and is evaluated for two types of ECG databases: standard stationary data and low-SNR ECG data obtained from wearable ECG. The proposed method results in a distinctly higher positive predictivity and leads to more satisfying overall outcomes, especially for the critical call of low-SNR data.","PeriodicalId":216293,"journal":{"name":"2015 IEEE International Conference on Digital Signal Processing (DSP)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122953858","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: 41