Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081334
Yulong Chen, S. Boussakta, C. Tsimenidis, J. Chambers, Shi Jin
Massive multiple-input multiple-output (MIMO) is an emerging technology for future wireless networks, scaling up conventional MIMO to an unprecedented number of antennas at base stations. Such a large antenna array has the potential to make the system achieve high channel capacity and spectral efficiency, but it also leads to high cost in terms of hardware complexity. In this paper, we consider a finite dimensional channel model in which finite distinct directions are applied with M angular bins. In massive multi-user MIMO systems, a hybrid precoding method is proposed to reduce the required number of radio frequency (RF) chains at the base station, employing a single antenna per mobile station. The proposed precoder is partitioned into a high-dimensional RF precoder and a low-dimensional baseband precoder. The RF precoder is designed to obtain power gain with phase-only control and the baseband precoder is designed to facilitate multi-stream processing. For realistic scenarios, we consider the situation where the RF phase control is quantized up to B bits of precision. Furthermore, an upper bound on spectral efficiency is derived with the proposed precoding scheme. The simulation results show that hybrid precoding achieves desirable performance in terms of spectral efficiency, which approaches the performance of zero-forcing precoding.
{"title":"Low complexity hybrid precoding in finite dimensional channel for massive MIMO systems","authors":"Yulong Chen, S. Boussakta, C. Tsimenidis, J. Chambers, Shi Jin","doi":"10.23919/EUSIPCO.2017.8081334","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081334","url":null,"abstract":"Massive multiple-input multiple-output (MIMO) is an emerging technology for future wireless networks, scaling up conventional MIMO to an unprecedented number of antennas at base stations. Such a large antenna array has the potential to make the system achieve high channel capacity and spectral efficiency, but it also leads to high cost in terms of hardware complexity. In this paper, we consider a finite dimensional channel model in which finite distinct directions are applied with M angular bins. In massive multi-user MIMO systems, a hybrid precoding method is proposed to reduce the required number of radio frequency (RF) chains at the base station, employing a single antenna per mobile station. The proposed precoder is partitioned into a high-dimensional RF precoder and a low-dimensional baseband precoder. The RF precoder is designed to obtain power gain with phase-only control and the baseband precoder is designed to facilitate multi-stream processing. For realistic scenarios, we consider the situation where the RF phase control is quantized up to B bits of precision. Furthermore, an upper bound on spectral efficiency is derived with the proposed precoding scheme. The simulation results show that hybrid precoding achieves desirable performance in terms of spectral efficiency, which approaches the performance of zero-forcing precoding.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130113186","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}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081442
Cristian Rusu, J. Thompson
In this paper we analyze the use of tight frames for the problem of localizing a source from noisy time-difference of arrival measurements. Based on the Fisher information matrix, we show that positioning the sensor network according to a tight frame that also obeys some internal symmetries provides the best average localization accuracy. We connect our result to previous approaches from the literature and show experimentally that near optimal accuracy can also be provided by random tight frames. We also make the assumption that the sensors are not fixed but placed on mobile units and we study the problem of bringing them to a tight configuration with the minimum energy consumption. Although our results hold for any dimension, for simplicity of exposition, the numerical experiments depicted are in the two dimensional case.
{"title":"On the use of tight frames for optimal sensor placement in time-difference of arrival localization","authors":"Cristian Rusu, J. Thompson","doi":"10.23919/EUSIPCO.2017.8081442","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081442","url":null,"abstract":"In this paper we analyze the use of tight frames for the problem of localizing a source from noisy time-difference of arrival measurements. Based on the Fisher information matrix, we show that positioning the sensor network according to a tight frame that also obeys some internal symmetries provides the best average localization accuracy. We connect our result to previous approaches from the literature and show experimentally that near optimal accuracy can also be provided by random tight frames. We also make the assumption that the sensors are not fixed but placed on mobile units and we study the problem of bringing them to a tight configuration with the minimum energy consumption. Although our results hold for any dimension, for simplicity of exposition, the numerical experiments depicted are in the two dimensional case.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131468511","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}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081561
Faton Maliqi, Francesca Bassi, P. Duhamel, Ilir Limani
This paper considers the analysis of communication protocols in wireless networks implementing both cooperation and Hybrid Automatic Repeat reQuest (HARQ) for Type I decoder and Type II decoder with Chase Combining. Using an example of a three-node network, we show that the communication protocol can be modeled using Finite State Markov Chains. This model efficiently predicts the performance of the system. However, the complexity depends on the number of states, which increases very fast as the protocol gets more sophisticated. We then derive a simplified model using state aggregation, and obtain a compact description which can be used to predict the performance with a reduced complexity. Moreover, we show that the simplified model describes a probabilistic communication protocol on the same network. Monte Carlo simulations show that the theoretical predictions match the simulated performance.
{"title":"Simplified analysis of HARQ cooperative networks using finite-state Markov chains","authors":"Faton Maliqi, Francesca Bassi, P. Duhamel, Ilir Limani","doi":"10.23919/EUSIPCO.2017.8081561","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081561","url":null,"abstract":"This paper considers the analysis of communication protocols in wireless networks implementing both cooperation and Hybrid Automatic Repeat reQuest (HARQ) for Type I decoder and Type II decoder with Chase Combining. Using an example of a three-node network, we show that the communication protocol can be modeled using Finite State Markov Chains. This model efficiently predicts the performance of the system. However, the complexity depends on the number of states, which increases very fast as the protocol gets more sophisticated. We then derive a simplified model using state aggregation, and obtain a compact description which can be used to predict the performance with a reduced complexity. Moreover, we show that the simplified model describes a probabilistic communication protocol on the same network. Monte Carlo simulations show that the theoretical predictions match the simulated performance.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131758600","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}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081237
A. A. Kumar, M. Chandra, P. Balamuralidhar
In this paper, joint frequency and 2-D direction of arrival (DOA) estimation at sub-Nyquist sampling rates of a multi-band signal (MBS) comprising of P disjoint narrowband signals is considered. Beginning with a standard uniform rectangular array (URA) consisting of M = Mx × My sensors, this paper proposes a simpler modification by adding a N — 1 delay channel network to only one of the sensor. A larger array is then formed by combining the sub-Nyquist sampled outputs of URA and the delay channel network, referred to as the difference space-time (DST) array. Towards estimating the joint frequency and 2-D DOA on this DST array, a new method utilizing the 3-D spatial smoothing for rank enhancement and a subspace algorithm based on ESPRIT is presented. Furthermore, it is shown that an ADC sampling frequency of fs ≥ B suffices, where B is the bandwidth of the narrow-band signal. With the proposed approach, it is shown that O(MN/4) frequencies and their 2-D DOAs can be estimated even when all frequencies alias to the same frequency due to sub-Nyquist sampling. Appropriate simulation results are also presented to corroborate these findings.
{"title":"Joint frequency and 2-D DOA recovery with sub-Nyquist difference space-time array","authors":"A. A. Kumar, M. Chandra, P. Balamuralidhar","doi":"10.23919/EUSIPCO.2017.8081237","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081237","url":null,"abstract":"In this paper, joint frequency and 2-D direction of arrival (DOA) estimation at sub-Nyquist sampling rates of a multi-band signal (MBS) comprising of P disjoint narrowband signals is considered. Beginning with a standard uniform rectangular array (URA) consisting of M = Mx × My sensors, this paper proposes a simpler modification by adding a N — 1 delay channel network to only one of the sensor. A larger array is then formed by combining the sub-Nyquist sampled outputs of URA and the delay channel network, referred to as the difference space-time (DST) array. Towards estimating the joint frequency and 2-D DOA on this DST array, a new method utilizing the 3-D spatial smoothing for rank enhancement and a subspace algorithm based on ESPRIT is presented. Furthermore, it is shown that an ADC sampling frequency of fs ≥ B suffices, where B is the bandwidth of the narrow-band signal. With the proposed approach, it is shown that O(MN/4) frequencies and their 2-D DOAs can be estimated even when all frequencies alias to the same frequency due to sub-Nyquist sampling. Appropriate simulation results are also presented to corroborate these findings.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129382201","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}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081179
Rainer Strobel, Andreas Barthelme, W. Utschick
Hybrid copper/fiber networks bridge the gap between the fiber link at the distribution point and the customer by using copper wires over the last meters. The G.fast technology has been designed to be used in such a fiber to the distribution point (FTTdp) network. Crosstalk management using MIMO precoding is a key to the required performance of FTTdp. With higher frequencies used on copper wires, nonlinear precoding schemes such as Tomlinson Harashima precoding are discussed as an alternative to linear precoding. This paper focuses on the advantages and losses of Tomlinson Harashima precoding used for coded transmission on twisted pair cable bundles. A performance loss model for the Modulo loss in coded transmission is presented. Interoperability between linear and nonlinear precoding is discussed.
{"title":"Implementation aspects of nonlinear precoding for G.fast — coding and legacy receivers","authors":"Rainer Strobel, Andreas Barthelme, W. Utschick","doi":"10.23919/EUSIPCO.2017.8081179","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081179","url":null,"abstract":"Hybrid copper/fiber networks bridge the gap between the fiber link at the distribution point and the customer by using copper wires over the last meters. The G.fast technology has been designed to be used in such a fiber to the distribution point (FTTdp) network. Crosstalk management using MIMO precoding is a key to the required performance of FTTdp. With higher frequencies used on copper wires, nonlinear precoding schemes such as Tomlinson Harashima precoding are discussed as an alternative to linear precoding. This paper focuses on the advantages and losses of Tomlinson Harashima precoding used for coded transmission on twisted pair cable bundles. A performance loss model for the Modulo loss in coded transmission is presented. Interoperability between linear and nonlinear precoding is discussed.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125238935","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}
Pub Date : 2017-08-01DOI: 10.23919/eusipco.2017.8081392
Daichi Kitamura, Nobutaka Ono, H. Saruwatari
In this paper, we address the blind source separation (BSS) problem and analyze the optimal window length in the short-time Fourier transform (STFT) for independent low-rank matrix analysis (ILRMA). ILRMA is a state-of-the-art BSS technique that utilizes the statistical independence between low-rank matrix spectrogram models, which are estimated by nonnegative matrix factorization. In conventional frequency-domain BSS, the modeling error of a mixing system increases when the window length is too short, and the accuracy of statistical estimation decreases when the window length is too long. Therefore, the optimal window length is determined by both the reverberation time and the number of time frames. However, unlike classical BSS methods such as ICA and IVA, ILRMA enables the full modeling of spectrograms, which may improve the robustness to a decrease in the number of frames in a longer-window case. To confirm this hypothesis, the optimal window length for ILRMA is experimentally investigated, and the difference between the performances of ILRMA and conventional BSS is discussed.
{"title":"Experimental analysis of optimal window length for independent low-rank matrix analysis","authors":"Daichi Kitamura, Nobutaka Ono, H. Saruwatari","doi":"10.23919/eusipco.2017.8081392","DOIUrl":"https://doi.org/10.23919/eusipco.2017.8081392","url":null,"abstract":"In this paper, we address the blind source separation (BSS) problem and analyze the optimal window length in the short-time Fourier transform (STFT) for independent low-rank matrix analysis (ILRMA). ILRMA is a state-of-the-art BSS technique that utilizes the statistical independence between low-rank matrix spectrogram models, which are estimated by nonnegative matrix factorization. In conventional frequency-domain BSS, the modeling error of a mixing system increases when the window length is too short, and the accuracy of statistical estimation decreases when the window length is too long. Therefore, the optimal window length is determined by both the reverberation time and the number of time frames. However, unlike classical BSS methods such as ICA and IVA, ILRMA enables the full modeling of spectrograms, which may improve the robustness to a decrease in the number of frames in a longer-window case. To confirm this hypothesis, the optimal window length for ILRMA is experimentally investigated, and the difference between the performances of ILRMA and conventional BSS is discussed.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133061460","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}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081677
G. Campobello, Antonino Segreto, Sarah Zanafi, Salvatore Serrano
In this paper we propose a new lossless compression algorithm suitable for Internet of Things (IoT). The proposed algorithm, named RAKE, is based only on elementary counting operations and has low memory requirements, and therefore it can be easily implemented in low-cost and low-speed micro-controllers as those used in IoT devices. Despite its simplicity, simulation results show that, in the case of sparse sequences, the proposed algorithm outperforms well-known lossless compression algorithms such as rar, gzip and bzip2. Moreover, in the case of real-world data, RAKE achieves higher compression ratios as even compared to IoT-specific lossless compression algorithms.
{"title":"RAKE: A simple and efficient lossless compression algorithm for the Internet of Things","authors":"G. Campobello, Antonino Segreto, Sarah Zanafi, Salvatore Serrano","doi":"10.23919/EUSIPCO.2017.8081677","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081677","url":null,"abstract":"In this paper we propose a new lossless compression algorithm suitable for Internet of Things (IoT). The proposed algorithm, named RAKE, is based only on elementary counting operations and has low memory requirements, and therefore it can be easily implemented in low-cost and low-speed micro-controllers as those used in IoT devices. Despite its simplicity, simulation results show that, in the case of sparse sequences, the proposed algorithm outperforms well-known lossless compression algorithms such as rar, gzip and bzip2. Moreover, in the case of real-world data, RAKE achieves higher compression ratios as even compared to IoT-specific lossless compression algorithms.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133502084","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}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081288
J. H. D. M. Goulart, P. Comon
Low-rank tensor approximation algorithms are building blocks in tensor methods for signal processing. In particular, approximations of low multilinear rank (mrank) are of central importance in tensor subspace analysis. This paper proposes a novel non-iterative algorithm for computing a low-mrank approximation, termed sequential low-rank approximation and projection (SeLRAP). Our algorithm generalizes sequential rank-one approximation and projection (SeROAP), which aims at the rank-one case. For third-order mrank-(1,R,R) approximations, SeLRAP's outputs are always at least as accurate as those of previously proposed methods. Our simulation results suggest that this is actually the case for the overwhelmingly majority of random third- and fourth-order tensors and several different mranks. Though the accuracy improvement is often small, we show it can make a large difference when repeatedly computing approximations, as happens, e.g., in an iterative hard thresholding algorithm for tensor completion.
{"title":"A novel non-iterative algorithm for low-multilinear-rank tensor approximation","authors":"J. H. D. M. Goulart, P. Comon","doi":"10.23919/EUSIPCO.2017.8081288","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081288","url":null,"abstract":"Low-rank tensor approximation algorithms are building blocks in tensor methods for signal processing. In particular, approximations of low multilinear rank (mrank) are of central importance in tensor subspace analysis. This paper proposes a novel non-iterative algorithm for computing a low-mrank approximation, termed sequential low-rank approximation and projection (SeLRAP). Our algorithm generalizes sequential rank-one approximation and projection (SeROAP), which aims at the rank-one case. For third-order mrank-(1,R,R) approximations, SeLRAP's outputs are always at least as accurate as those of previously proposed methods. Our simulation results suggest that this is actually the case for the overwhelmingly majority of random third- and fourth-order tensors and several different mranks. Though the accuracy improvement is often small, we show it can make a large difference when repeatedly computing approximations, as happens, e.g., in an iterative hard thresholding algorithm for tensor completion.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132149686","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}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081684
Fatemeh Afkhaminia, M. Azghani
Estimating the direction of arrival (DOA) in sensor arrays is a crucial task in array signal processing systems. This task becomes more difficult when the sensors have gain/phase uncertainty. We have addressed this issue by modeling the problem as a combination of two sparse components, the DOA vector and the gain/phase uncertainty vector. Therefore, a sparse decomposition technique is suggested to jointly recover the DOAs and the sensors with gain/phase uncertainty. The simulation results confirm that the suggested method offers very good performance in different scenarios and is superior to its counterparts.
{"title":"Sparsity-based direction of arrival estimation in the presence of gain/phase uncertainty","authors":"Fatemeh Afkhaminia, M. Azghani","doi":"10.23919/EUSIPCO.2017.8081684","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081684","url":null,"abstract":"Estimating the direction of arrival (DOA) in sensor arrays is a crucial task in array signal processing systems. This task becomes more difficult when the sensors have gain/phase uncertainty. We have addressed this issue by modeling the problem as a combination of two sparse components, the DOA vector and the gain/phase uncertainty vector. Therefore, a sparse decomposition technique is suggested to jointly recover the DOAs and the sensors with gain/phase uncertainty. The simulation results confirm that the suggested method offers very good performance in different scenarios and is superior to its counterparts.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132768083","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}
Pub Date : 2017-08-01DOI: 10.23919/EUSIPCO.2017.8081526
Zhi Zhu, Ryota Miyauchi, Yukiko Araki, M. Unoki
Cochlear implant (CI) listeners were found to have great difficulty with vocal emotion recognition because of the limited spectral cues provided by CI devices. Previous studies have shown that the modulation spectral features of temporal envelopes may be important cues for vocal emotion recognition of noise-vocoded speech (NVS) as simulated CIs. In this paper, the feasibility of vocal emotion conversion on a modulation spectrogram for simulated CIs for correctly recognizing vocal emotion is confirmed. A method based on a linear prediction scheme is proposed to modify the modulation spectrogram and its features of neutral speech to match that of emotional speech. The logic of this approach is that if vocal emotion perception of NVS is based on the modulation spectral features, NVS with similar modulation spectral features of emotional speech will be recognized as the same emotion. As a result, it was found that the modulation spectrogram of neutral speech can be successfully converted to that of emotional speech. The results of the evaluation experiment showed the feasibility of vocal emotion conversion on the modulation spectrogram for simulated CIs. The vocal emotion enhancement on the modulation spectrogram was also further discussed.
{"title":"Feasibility of vocal emotion conversion on modulation spectrogram for simulated cochlear implants","authors":"Zhi Zhu, Ryota Miyauchi, Yukiko Araki, M. Unoki","doi":"10.23919/EUSIPCO.2017.8081526","DOIUrl":"https://doi.org/10.23919/EUSIPCO.2017.8081526","url":null,"abstract":"Cochlear implant (CI) listeners were found to have great difficulty with vocal emotion recognition because of the limited spectral cues provided by CI devices. Previous studies have shown that the modulation spectral features of temporal envelopes may be important cues for vocal emotion recognition of noise-vocoded speech (NVS) as simulated CIs. In this paper, the feasibility of vocal emotion conversion on a modulation spectrogram for simulated CIs for correctly recognizing vocal emotion is confirmed. A method based on a linear prediction scheme is proposed to modify the modulation spectrogram and its features of neutral speech to match that of emotional speech. The logic of this approach is that if vocal emotion perception of NVS is based on the modulation spectral features, NVS with similar modulation spectral features of emotional speech will be recognized as the same emotion. As a result, it was found that the modulation spectrogram of neutral speech can be successfully converted to that of emotional speech. The results of the evaluation experiment showed the feasibility of vocal emotion conversion on the modulation spectrogram for simulated CIs. The vocal emotion enhancement on the modulation spectrogram was also further discussed.","PeriodicalId":346811,"journal":{"name":"2017 25th European Signal Processing Conference (EUSIPCO)","volume":"24 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133208151","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}
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