Direction of arrival (DOA) estimation for multi-channel speech enhancement is a challenging problem. In this context, this paper proposes a new method for joint DOA estimation using a low complexity convolutional neural network (CNN) architecture. The spherical harmonic (SH) coefficients of the received speech signal are obtained from the spherical harmonics decomposition (SHD). The magnitude and phase features are extracted from these SH coefficients and combined as a single feature for training the CNN. A single CNN model is trained using these combined features in contrast to two CNN models used in earlier work. Both azimuth and elevation are then obtained for estimation of DOA from this single CNN. Extensive simulations are also conducted for the performance evaluation of the proposed low complexity CNN model. It is observed that the proposed CNN model provides robust DOA estimates at the various signal to noise ratios (SNR) and reverberation times with reduced computational complexity. Performance evaluated in terms of the gross error (GE) and run-time complexity also provides interesting results motivating the use of the proposed model in practical applications.
{"title":"Joint DOA Estimation in Spherical Harmonics Domain using Low Complexity CNN","authors":"Priyadarshini Dwivedi, Raj Prakash Gohil, Gyanajyoti Routray, Vishnuvardhan Varanasi, R. Hegde","doi":"10.1109/SPCOM55316.2022.9840853","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840853","url":null,"abstract":"Direction of arrival (DOA) estimation for multi-channel speech enhancement is a challenging problem. In this context, this paper proposes a new method for joint DOA estimation using a low complexity convolutional neural network (CNN) architecture. The spherical harmonic (SH) coefficients of the received speech signal are obtained from the spherical harmonics decomposition (SHD). The magnitude and phase features are extracted from these SH coefficients and combined as a single feature for training the CNN. A single CNN model is trained using these combined features in contrast to two CNN models used in earlier work. Both azimuth and elevation are then obtained for estimation of DOA from this single CNN. Extensive simulations are also conducted for the performance evaluation of the proposed low complexity CNN model. It is observed that the proposed CNN model provides robust DOA estimates at the various signal to noise ratios (SNR) and reverberation times with reduced computational complexity. Performance evaluated in terms of the gross error (GE) and run-time complexity also provides interesting results motivating the use of the proposed model in practical applications.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116808919","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840787
Murali Krishna Pavuluri, Prem Singh, A. Jagannatham, V. Gadre
In this paper, a novel millimeter wave (mmWave) multiple-input multiple-output (MIMO) system based on coded filter bank multicarrier (FBMC) waveform is proposed. The coded FBMC modifies FBMC waveform such that it mitigates intrinsic interference by spreading the symbols in time. Next, a semi-blind channel estimation scheme is proposed for the mmWave MIMO coded FBMC system. The proposed semiblind estimation exploits the data symbols along with pilots to substantially enhance the mean square error (MSE) performance. Therefore, the proposed work is superior to the existing works in terms of accuracy of channel estimation. The simulation results indicate improved MSE performance of the proposed semi-blind scheme in comparison to the existing conventional training based channel estimation techniques.
{"title":"Semi-Blind Technique for Frequency Selective Channel Estimation in Millimeter-Wave MIMO Coded FBMC System","authors":"Murali Krishna Pavuluri, Prem Singh, A. Jagannatham, V. Gadre","doi":"10.1109/SPCOM55316.2022.9840787","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840787","url":null,"abstract":"In this paper, a novel millimeter wave (mmWave) multiple-input multiple-output (MIMO) system based on coded filter bank multicarrier (FBMC) waveform is proposed. The coded FBMC modifies FBMC waveform such that it mitigates intrinsic interference by spreading the symbols in time. Next, a semi-blind channel estimation scheme is proposed for the mmWave MIMO coded FBMC system. The proposed semiblind estimation exploits the data symbols along with pilots to substantially enhance the mean square error (MSE) performance. Therefore, the proposed work is superior to the existing works in terms of accuracy of channel estimation. The simulation results indicate improved MSE performance of the proposed semi-blind scheme in comparison to the existing conventional training based channel estimation techniques.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"184 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114402216","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840825
P. Das, Pradyumna Hegade
This paper investigates the physical layer secrecy performance for an underlay wiretap spectrum sharing network with optimal relay and antenna selection (ORAS). It jointly selects a transmit antenna at a secondary source, a receive antenna at its destination, and a relay between them to maximize the secrecy rate at the destination under a passive eavesdropping scenario. We derive novel, exact expressions for secrecy outage probability (SOP) and secrecy throughput (ST) in a single integral-form. We also provide their accurate approximations in closed-form. Considering three distinct scenarios depending on whether the main link is stronger than the wiretap link and the nature of the primary interference constraint, we obtain different useful system design insights. Under a proportional interference constraint, secrecy diversity gain is achieved only when the main link is stronger than the wiretap link, otherwise the diversity gain is lost and leads to an SOP floor. Under a fixed interference constraint, the SOP floor is further increased. Through numerical results, we show the impact of different signal combining strategies by the eavesdropper on the SOP and ST. We also illustrate the efficacy of the ORAS scheme as compared to other relay and antenna selection schemes.
{"title":"Secrecy Performance with Optimal Relay and Antenna Selection in Spectrum-Sharing Networks","authors":"P. Das, Pradyumna Hegade","doi":"10.1109/SPCOM55316.2022.9840825","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840825","url":null,"abstract":"This paper investigates the physical layer secrecy performance for an underlay wiretap spectrum sharing network with optimal relay and antenna selection (ORAS). It jointly selects a transmit antenna at a secondary source, a receive antenna at its destination, and a relay between them to maximize the secrecy rate at the destination under a passive eavesdropping scenario. We derive novel, exact expressions for secrecy outage probability (SOP) and secrecy throughput (ST) in a single integral-form. We also provide their accurate approximations in closed-form. Considering three distinct scenarios depending on whether the main link is stronger than the wiretap link and the nature of the primary interference constraint, we obtain different useful system design insights. Under a proportional interference constraint, secrecy diversity gain is achieved only when the main link is stronger than the wiretap link, otherwise the diversity gain is lost and leads to an SOP floor. Under a fixed interference constraint, the SOP floor is further increased. Through numerical results, we show the impact of different signal combining strategies by the eavesdropper on the SOP and ST. We also illustrate the efficacy of the ORAS scheme as compared to other relay and antenna selection schemes.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114709225","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840763
Shreya S. Chaturvedi, Hardik B. Sailor, H. Patil
Automatic Speech Recognition (ASR) is a fast-growing field, where reliable systems are made for high resource languages and for adult’s speech. However, performance of such ASR system is inefficient for children speech, due to numerous acoustic variability in children speech and scarcity of resources. In this paper, we propose to use the unlabeled data extensively to develop ASR system for low resourced children speech. State-of-the-art wav2vec 2.0 is the baseline ASR technique used here. The baseline’s performance is further enhanced with the intuition of Noisy Student Teacher (NST) learning. The proposed technique is not only limited to introducing the use of soft labels (i.e., word-level transcription) of unlabeled data, but also adapts the learning of teacher model or preceding student model, which results in reduction of the redundant training significantly. To that effect, a detailed analysis is reported in this paper, as there is a difference in teacher and student learning. In ASR experiments, character-level tokenization was used and hence, Connectionist Temporal Classification (CTC) loss was used for fine-tuning. Due to computational limitations, experiments are performed with approximately 12 hours of training, and 5 hours of development and test data was used from standard My Science Tutor (MyST) corpus. The baseline wav2vec 2.0 achieves 34% WER, while relatively 10% of performance was improved using the proposed approach. Further, the analysis of performance loss and effect of language model is discussed in details.
{"title":"Noisy Student Teacher Training with Self Supervised Learning for Children ASR","authors":"Shreya S. Chaturvedi, Hardik B. Sailor, H. Patil","doi":"10.1109/SPCOM55316.2022.9840763","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840763","url":null,"abstract":"Automatic Speech Recognition (ASR) is a fast-growing field, where reliable systems are made for high resource languages and for adult’s speech. However, performance of such ASR system is inefficient for children speech, due to numerous acoustic variability in children speech and scarcity of resources. In this paper, we propose to use the unlabeled data extensively to develop ASR system for low resourced children speech. State-of-the-art wav2vec 2.0 is the baseline ASR technique used here. The baseline’s performance is further enhanced with the intuition of Noisy Student Teacher (NST) learning. The proposed technique is not only limited to introducing the use of soft labels (i.e., word-level transcription) of unlabeled data, but also adapts the learning of teacher model or preceding student model, which results in reduction of the redundant training significantly. To that effect, a detailed analysis is reported in this paper, as there is a difference in teacher and student learning. In ASR experiments, character-level tokenization was used and hence, Connectionist Temporal Classification (CTC) loss was used for fine-tuning. Due to computational limitations, experiments are performed with approximately 12 hours of training, and 5 hours of development and test data was used from standard My Science Tutor (MyST) corpus. The baseline wav2vec 2.0 achieves 34% WER, while relatively 10% of performance was improved using the proposed approach. Further, the analysis of performance loss and effect of language model is discussed in details.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127832116","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840826
S. Mukhopadhyay
In this paper we consider the problem of exact recovery of a fixed sparse vector from sequentially arriving measurements. We assume that the measurements are generated by a linear model with time varying matrices and both the measurement vector as well as the matrix at each time are made available. However, we assume that the underlying unknown sparse vector is fixed during the time of interest. We prove that if the measurement matrices are i.i.d. subGaussian, the iterates produced by the popular iterative hard thresholding (IHT) algorithm can converge to the exact sparse vector with high probability if a certain function of the sample complexities of the time varying measurements, which we call effective sample complexity satisfies certain lower bound dependent on K,N, the sparsity and the length of the unknown vector, respectively. Interestingly, this bound reveals that the probability that the estimation error at the end of some instant is small enough, is hardly affected even if very small number measurements are used at sporadically chosen time instances. We also corroborate this theoretical result with numerical experiments which demonstrate that the conventional IHT can enjoy greater probability of recovery by occasionally using far lesser number of measurements than that required for successful recovery with offline IHT with fixed measurement matrix.
{"title":"On the Effective Sample Complexity for Exact Sparse Recovery from Sequential Linear Measurements","authors":"S. Mukhopadhyay","doi":"10.1109/SPCOM55316.2022.9840826","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840826","url":null,"abstract":"In this paper we consider the problem of exact recovery of a fixed sparse vector from sequentially arriving measurements. We assume that the measurements are generated by a linear model with time varying matrices and both the measurement vector as well as the matrix at each time are made available. However, we assume that the underlying unknown sparse vector is fixed during the time of interest. We prove that if the measurement matrices are i.i.d. subGaussian, the iterates produced by the popular iterative hard thresholding (IHT) algorithm can converge to the exact sparse vector with high probability if a certain function of the sample complexities of the time varying measurements, which we call effective sample complexity satisfies certain lower bound dependent on K,N, the sparsity and the length of the unknown vector, respectively. Interestingly, this bound reveals that the probability that the estimation error at the end of some instant is small enough, is hardly affected even if very small number measurements are used at sporadically chosen time instances. We also corroborate this theoretical result with numerical experiments which demonstrate that the conventional IHT can enjoy greater probability of recovery by occasionally using far lesser number of measurements than that required for successful recovery with offline IHT with fixed measurement matrix.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125979931","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840847
Nayan Anand Vats, Purva Barche, Mirishkar Sai Ganesh, A. Vuppala
Alzheimer’s Dementia is a progressive neurological disorder characterized by cognitive impairment. It affects memory, thinking skills, language, and the ability to perform simple tasks. Detection of Alzheimer’s Dementia from the speech is considered a primitive task, as most speech cues are preserved in it. Studies in the literature focused mainly on the lexical features and few acoustic features for detecting Alzheimer’s disease. The present work explores the single frequency filtering cepstral coefficients (SFCC) for the automatic detection of Alzheimer’s disease. In contrast to STFTs, the proposed feature has better temporal and spectral resolution and captures the transient part more appropriately. This offers a very compact and efficient way to derive the formant structure in the speech signal. The experiments were conducted on the ADReSSo dataset, using the support vector machine classifier. The classification performance was compared with several baseline features like Mel-frequency cepstral coefficients (MFCC), perceptual linear prediction (PLP), linear prediction cepstral coefficient (LPCC), Mel frequency cepstral coefficients of LP-residual (MFCC-WR), ZFF signal (MFCC-ZF) and eGeMAPS (openSMILE). The experiments conducted on Alzheimer’s Dementia classification task show that the proposed feature performs better than conventional MFCCs. Among all the features, SFCC offers the best classification accuracy of 65.1% and 60.6% for dementia detection on cross-validation and test data, respectively. The combination of baseline features with SFCC features further improved the performance.
{"title":"Exploring High Spectro-Temporal Resolution for Alzheimer’s Dementia Detection","authors":"Nayan Anand Vats, Purva Barche, Mirishkar Sai Ganesh, A. Vuppala","doi":"10.1109/SPCOM55316.2022.9840847","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840847","url":null,"abstract":"Alzheimer’s Dementia is a progressive neurological disorder characterized by cognitive impairment. It affects memory, thinking skills, language, and the ability to perform simple tasks. Detection of Alzheimer’s Dementia from the speech is considered a primitive task, as most speech cues are preserved in it. Studies in the literature focused mainly on the lexical features and few acoustic features for detecting Alzheimer’s disease. The present work explores the single frequency filtering cepstral coefficients (SFCC) for the automatic detection of Alzheimer’s disease. In contrast to STFTs, the proposed feature has better temporal and spectral resolution and captures the transient part more appropriately. This offers a very compact and efficient way to derive the formant structure in the speech signal. The experiments were conducted on the ADReSSo dataset, using the support vector machine classifier. The classification performance was compared with several baseline features like Mel-frequency cepstral coefficients (MFCC), perceptual linear prediction (PLP), linear prediction cepstral coefficient (LPCC), Mel frequency cepstral coefficients of LP-residual (MFCC-WR), ZFF signal (MFCC-ZF) and eGeMAPS (openSMILE). The experiments conducted on Alzheimer’s Dementia classification task show that the proposed feature performs better than conventional MFCCs. Among all the features, SFCC offers the best classification accuracy of 65.1% and 60.6% for dementia detection on cross-validation and test data, respectively. The combination of baseline features with SFCC features further improved the performance.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122720837","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840815
Ashwini H. Raghavendra, Anagha K. Kowshik, Sanjeev Gurugopinath, S. Muhaidat, C. Tellambura
We propose a novel, low complexity energy-based maximum likelihood (EML) detector for a generalized space shift keying (GSSK)-enabled multiple-input multiple-output (MIMO) ambient backscatter communication (ABC) system. The proposed scheme exploits the multiple antenna structure of the system to achieve a lower error rate performance than the conventional single-antenna ABC systems. The proposed EML GSSK detector does not require the perfect knowledge of the ambient source signal. To gain insights into the performance of the proposed scheme, we derive the exact pairwise error probability (PEP) of the EML detector, and further obtain an upper bound on the probability of error. We also derive a simple asymptotic PEP expression, as the number of antennas of the reader becomes large. We validate our analysis through Monte Carlo simulations, and show that the performance loss due to the approximations employed in our analysis is small. The performance of EML detector is also compared with the conventional ML detector and the loss in performance is studied.
{"title":"Energy-Based Maximum Likelihood Detector for GSSK in MIMO-ABC Systems","authors":"Ashwini H. Raghavendra, Anagha K. Kowshik, Sanjeev Gurugopinath, S. Muhaidat, C. Tellambura","doi":"10.1109/SPCOM55316.2022.9840815","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840815","url":null,"abstract":"We propose a novel, low complexity energy-based maximum likelihood (EML) detector for a generalized space shift keying (GSSK)-enabled multiple-input multiple-output (MIMO) ambient backscatter communication (ABC) system. The proposed scheme exploits the multiple antenna structure of the system to achieve a lower error rate performance than the conventional single-antenna ABC systems. The proposed EML GSSK detector does not require the perfect knowledge of the ambient source signal. To gain insights into the performance of the proposed scheme, we derive the exact pairwise error probability (PEP) of the EML detector, and further obtain an upper bound on the probability of error. We also derive a simple asymptotic PEP expression, as the number of antennas of the reader becomes large. We validate our analysis through Monte Carlo simulations, and show that the performance loss due to the approximations employed in our analysis is small. The performance of EML detector is also compared with the conventional ML detector and the loss in performance is studied.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133577572","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}
In this paper, a wideband band pass filter (BPF) with two notches is presented using a substrate integrated waveguide (SIW). The BPF consists of U-shaped slots at the top of SIW with an integrated digital capacitor (IDC). The Wideband BPF has a bandwidth of 9.25 GHz from 5.86 GHz to 15.1 GHz. In the pass band, small U-shape slots are inserted inside the bigger U-shape slots and with the help of lumped capacitors with values of 0.2 pF and 0.4 pF notches are created at 8 GHz and 10 GHz. The proposed BPF with two notch has the insertion loss of 0.5 dB, and a return loss of 10 dB in the pass band, bandwidth of 9.14 GHz from 6.46 GHz to 15.6 GHz.
{"title":"A Wideband Bandpass Filter using U-shaped slots on SIW with two Notches at 8 GHz and 10 GHz","authors":"Shameer Keelaillam, Saragadam Siva Teja, Tadikonda Jayanth, Ramapuram Ajay Reddy, Kanumetta Bhargava Ramanujam, L. Kumar","doi":"10.1109/SPCOM55316.2022.9840774","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840774","url":null,"abstract":"In this paper, a wideband band pass filter (BPF) with two notches is presented using a substrate integrated waveguide (SIW). The BPF consists of U-shaped slots at the top of SIW with an integrated digital capacitor (IDC). The Wideband BPF has a bandwidth of 9.25 GHz from 5.86 GHz to 15.1 GHz. In the pass band, small U-shape slots are inserted inside the bigger U-shape slots and with the help of lumped capacitors with values of 0.2 pF and 0.4 pF notches are created at 8 GHz and 10 GHz. The proposed BPF with two notch has the insertion loss of 0.5 dB, and a return loss of 10 dB in the pass band, bandwidth of 9.14 GHz from 6.46 GHz to 15.6 GHz.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114077090","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840758
Shivani Dhok, P. Peshwe, Prabhat Kumar Sharma
In this paper, we consider an underlay cognitive molecular communication (MC) system inside a cylindrical diffusive-channel. We consider a primary and a secondary link operating simultaneously inside that channel, with the primary link having a higher priority over the secondary link. Based on the co-channel interference (CCI) at the primary receiver, the number of molecules transmitted by the secondary transmitter is controlled. The performance of the channel is analyzed based on the probability of error and the effects of the interference limits are analyzed. The Monte-Carlo simulations are presented for validating the derived expressions.
{"title":"Cognitive Molecular Communication Inside a Cylindrical Diffusive Channel","authors":"Shivani Dhok, P. Peshwe, Prabhat Kumar Sharma","doi":"10.1109/SPCOM55316.2022.9840758","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840758","url":null,"abstract":"In this paper, we consider an underlay cognitive molecular communication (MC) system inside a cylindrical diffusive-channel. We consider a primary and a secondary link operating simultaneously inside that channel, with the primary link having a higher priority over the secondary link. Based on the co-channel interference (CCI) at the primary receiver, the number of molecules transmitted by the secondary transmitter is controlled. The performance of the channel is analyzed based on the probability of error and the effects of the interference limits are analyzed. The Monte-Carlo simulations are presented for validating the derived expressions.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128249493","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 : 2022-07-11DOI: 10.1109/SPCOM55316.2022.9840851
Pushpendra Singh, Amit Singhal, Binish Fatimah
Speech analysis and various speech processing applications use instantaneous fundamental frequency $(F_{0})$ of voiced speech signal as a prime acoustic parameter. The low frequency component of the voiced speech possesses most of the energy in F0 neighbourhood and its few harmonics. In this study, a novel approach is proposed to extract the instantaneous F0 component from a voiced speech signal using Fourier decomposition method (FDM), which decomposes the signal into its amplitude-frequency modulated (AM-FM) components. We also demonstrate that these derived AM-FM components, obtained due to desired frequency band decomposition property of FDM, provides the most suitable representation for voiced speech as compared to other AM-FM models available in the literature. Numerical results are presented to validate the adequacy of proposed method in estimating F0, when compared with existing algorithms based on empirical mode decomposition (EMD) and other speech-related algorithms.
{"title":"Instantaneous Fundamental Frequency Estimation from Speech using Fourier Decomposition Method","authors":"Pushpendra Singh, Amit Singhal, Binish Fatimah","doi":"10.1109/SPCOM55316.2022.9840851","DOIUrl":"https://doi.org/10.1109/SPCOM55316.2022.9840851","url":null,"abstract":"Speech analysis and various speech processing applications use instantaneous fundamental frequency $(F_{0})$ of voiced speech signal as a prime acoustic parameter. The low frequency component of the voiced speech possesses most of the energy in F0 neighbourhood and its few harmonics. In this study, a novel approach is proposed to extract the instantaneous F0 component from a voiced speech signal using Fourier decomposition method (FDM), which decomposes the signal into its amplitude-frequency modulated (AM-FM) components. We also demonstrate that these derived AM-FM components, obtained due to desired frequency band decomposition property of FDM, provides the most suitable representation for voiced speech as compared to other AM-FM models available in the literature. Numerical results are presented to validate the adequacy of proposed method in estimating F0, when compared with existing algorithms based on empirical mode decomposition (EMD) and other speech-related algorithms.","PeriodicalId":246982,"journal":{"name":"2022 IEEE International Conference on Signal Processing and Communications (SPCOM)","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132842079","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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