Parallel and Limited Data Voice Conversion Using Stochastic Variational Deep Kernel Learning

Typically, voice conversion is regarded as an engineering problem with limited training data. The reliance on massive amounts of data hinders the practical applicability of deep learning approaches, which have been extensively researched in recent years. On the other hand, statistical methods are effective with limited data but have difficulties in modelling complex mapping functions. This paper proposes a voice conversion method that works with limited data and is based on stochastic variational deep kernel learning (SVDKL). At the same time, SVDKL enables the use of deep neural networks' expressive capability as well as the high flexibility of the Gaussian process as a Bayesian and non-parametric method. When the conventional kernel is combined with the deep neural network, it is possible to estimate non-smooth and more complex functions. Furthermore, the model's sparse variational Gaussian process solves the scalability problem and, unlike the exact Gaussian process, allows for the learning of a global mapping function for the entire acoustic space. One of the most important aspects of the proposed scheme is that the model parameters are trained using marginal likelihood optimization, which considers both data fitting and model complexity. Considering the complexity of the model reduces the amount of training data by increasing the resistance to overfitting. To evaluate the proposed scheme, we examined the model's performance with approximately 80 seconds of training data. The results indicated that our method obtained a higher mean opinion score, smaller spectral distortion, and better preference tests than the compared methods

Pengetahuan dan minat terhadap kerjaya keushawanan : satu tinjauan di kalangan pelajar-pelajar bumiputera aliran kemahiran

Kajian ini adalah berkaitan dengan keIjaya keusahawanan di kalangan pelajar-pelajar bumiputera aliran kemahiran. Tujuan kajian ini diadakan adalah untuk melihat sarna ada pelajar-pelajar ini mempunyai pengetahuan dan minat terhadap keIjaya keusahawanan, atau tidak. Soal selidik telah diedarkan kepada 48 orang responden, dan kursus Teknologi Kejuruteraan A\\'am (Bangunan) di Institut Kemahiran Mara (l1Ovf), Manjung, Perak Darul Ridzuan. Data yang diperolehi dianalisis menggunakan pensian Srarv,·rical Package For Social Science (SPSS). Hasil analisis menunjukkan nilai skor min untuk kesemua item berada pad a tahap yang memuaskan. Ini membuktikan bahawa pelajar-pelajar bumiputera aliran kemahiran sememangnya mempunyai pengetahuan dan minat terhadap keIjaya keusahawanan. \Valaupun mempunyai pengetahuan dan minat terhadap keIjaya keusahawanan, mereka masih kekurangan maklumat berkenaan prosedur memulakan pemiagaan serta badan atau organisasi yang memberi kemudahan dana kewangan. Oleh yang demikian, 'Panduall Ringkas Mellceburi Bidang Keusahawanall' dihasilkan untuk memben maklumat ten tang langkah-langkah yang perlu dilakukan untuk memulakan sesuatu pemiagaan serta badan atau organisasi yang memben kemudahan dana kewangan, kepada para pelajar

Aerospace Medicine and Biology: A continuing bibliography with indexes (supplement 314)

This bibliography lists 139 reports, articles, and other documents introduced into the NASA scientific and technical information system in August, 1988

Channel Estimation in Multicarrier Communication Systems

The data rate and spectrum efficiency of wireless mobile communications have been significantly improved over the last decade or so. Recently, the advanced systems such as 3GPP LTE and terrestrial digital TV broadcasting have been sophisticatedly developed using OFDM and CDMA technology. In general, most mobile communication systems transmit bits of information in the radio space to the receiver. The radio channels in mobile radio systems are usually multipath fading channels, which cause inter-symbol interference (ISI) in the received signal. To remove ISI from the signal, there is a need of strong equalizer which requires knowledge on the channel impulse response (CIR).This is primarily provided by a separate channel estimator. Usually the channel estimation is based on the known sequence of bits, which is unique for a certain transmitter and which is repeated in every transmission burst. Thus, the channel estimator is able to estimate CIR for each burst separately by exploiting the known transmitted bits and the corresponding received samples. In this thesis we investigate and compare various efficient channel estimation schemes for OFDM systems which can also be extended to MC DS-CDMA systems.The channel estimation can be performed by either inserting pilot tones into all subcarriers of OFDM symbols with a specific period or inserting pilot tones into each OFDM symbol. Two major types of pilot arrangement such as block type and comb type pilot have been focused employing Least Square Error (LSE) and Minimum Mean Square Error (MMSE) channel estimators. Block type pilot sub-carriers is especially suitable for slow-fading radio channels whereas comb type pilots provide better resistance to fast fading channels. Also comb type pilot arrangement is sensitive to frequency selectivity when comparing to block type arrangement. However, there is another supervised technique called Implicit Training (IT) based channel estimation which exploits the first order statistics in the received data, induced by superimposing periodic training sequences with good correlation properties, along with the information symbols. Hence, the need for additional time slots for training the equalizer is avoided. The performance of the estimators is presented in terms of the mean square estimation error (MSEE) and bit error rate (BER)

Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)

Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression

Publications of the Jet Propulsion Laboratory, 1984

The Jet Propulsion Laboratory (JPL) bibliography 39-26 describes and indexes by primary author the externally distributed technical reporting, released during calendar year 1984, that resulted from scientific and engineering work performed, or managed, by the Jet Propulsion Laboratory. Three classes of publications are included: (1) JPL Publications (82-, 83-, 84-series, etc.), in which the information is complete for a specific accomplishment; (2) articles from the quarterly Telecommunications and Data Acquisition (TDA) Program Report (42-series); and (3) articles published in the open literature

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Autoencoding sensory substitution

Tens of millions of people live blind, and their number is ever increasing. Visual-to-auditory sensory substitution (SS) encompasses a family of cheap, generic solutions to assist the visually impaired by conveying visual information through sound. The required SS training is lengthy: months of effort is necessary to reach a practical level of adaptation. There are two reasons for the tedious training process: the elongated substituting audio signal, and the disregard for the compressive characteristics of the human hearing system. To overcome these obstacles, we developed a novel class of SS methods, by training deep recurrent autoencoders for image-to-sound conversion. We successfully trained deep learning models on different datasets to execute visual-to-auditory stimulus conversion. By constraining the visual space, we demonstrated the viability of shortened substituting audio signals, while proposing mechanisms, such as the integration of computational hearing models, to optimally convey visual features in the substituting stimulus as perceptually discernible auditory components. We tested our approach in two separate cases. In the first experiment, the author went blindfolded for 5 days, while performing SS training on hand posture discrimination. The second experiment assessed the accuracy of reaching movements towards objects on a table. In both test cases, above-chance-level accuracy was attained after a few hours of training. Our novel SS architecture broadens the horizon of rehabilitation methods engineered for the visually impaired. Further improvements on the proposed model shall yield hastened rehabilitation of the blind and a wider adaptation of SS devices as a consequence

Developing coherent optical wavelength conversion systems for reconfigurable photonic networks

In future optical networks that employ wavelength division multiplexing (WDM), the use of optical switching technologies on a burst or packet level, combined with advanced modulation formats would achieve greater spectral efficiency and utilize the existing bandwidth more efficiently. All-optical wavelength converters are expected to be one of the key components in these broadband networks. They can be used at the network nodes to avoid contention and to dynamically allocate wavelengths to ensure optimum use of fiber bandwidth. In this work, a reconfigurable wavelength converter comprising of a Semiconductor Optical Amplifier (SOA) as the nonlinear element and a fast-switching sampled grating distributed Bragg reflector (SG-DBR) tunable laser as one of the pumps is developed. The wavelength conversion of 12.5-Gbaud quadrature phase shift keying (QPSK) and Pol-Mul QPSK signals with switching time of tens of nanoseconds is experimentally achieved. Although the tunable DBR lasers can achieve ns tuning time, they present relatively large phase noise. The phase noise transfer from the pump to the converted signal can have a deleterious effect on signal quality and cause a performance penalty with phase modulated signals. To overcome the phase noise transfer issue, a wavelength converter using tunable dual-correlated pumps provided by the combination of a single-section quantum dash passively mode-locked laser (QD-PMLL) and a programmable tunable optical filter is designed and the wavelength conversion of QPSK and 16-quadrature amplitude modulation (16-QAM) signals at 12.5 GBaud is experimentally investigated. Nonlinear distortion of the wavelength converted signal caused by gain saturation effects in the SOA can significantly degrade the signal quality and cause difficulties for the practical wavelength conversion of sig nal data with advanced modulation formats. In this work, the machine learning clustering based nonlinearity compensation method is proposed to improve the tolerance to nonlinear distortion in an SOA based wavelength conversion system with 16 QAM and 64 QAM signals