This paper presents a voice conversion technique using speaker-dependent Restricted Boltzmann Machines (RBM) to build highorder eigen spaces of source/target speakers, where it is easier to convert the source speech to the target speech than in the traditional cepstrum space. We build a deep conversion architecture that concatenates the two speakerdependent RBMs with neural networks, expecting that they automatically discover abstractions to express the original input features. Under this concept, if we train the RBMs using only the speech of an individual speaker that includes various phonemes while keeping the speaker individuality unchanged, it can be considered that there are fewer phonemes and relatively more speaker individuality in the output features of the hidden layer than original acoustic features. Training the RBMs for a source speaker and a target speaker, we can then connect and convert the speaker individuality abstractions using Neural Networks (NN). The converted abstraction of the source speaker is then back-propagated into the acoustic space (e.g., MFCC) using the RBM of the target speaker. We conducted speaker-voice conversion experiments and confirmed the efficacy of our method with respect to subjective and objective criteria, comparing it with the conventional Gaussian Mixture Model-based method and an ordinary NN. key words: voice conversion, restricted Boltzmann machine, deep learning, speaker individuality

https://www.jstage.jst.go.jp/article/transinf/E97.D/6/E97.D_1403/_pdf

Voice Conversion Based on Speaker-Dependent Restricted Boltzmann Machines

http://eee.sutech.ac.ir/sites/eee.sutech.ac.ir/files/Groups/com/DrDanyali/Course1/PD6-Lowbitrate%20SAR%20image%20coding%20based%20on%20adaptive%20multiscale%20bandlet.pdf

Low bit rate SAR image coding based on adaptive multiscale Bandelets and cooperative decision

Synthetic aperture radar (SAR) is an imaging system which provides high-resolution images of earth surface. Nowadays there is an ever-growing interest in the SAR data compression because of the huge resources which require for storage and transmission. In this paper, we address the problem of SAR image compression and apply the multiscale Bandelet with adaptive quadtree partition to solve it. Because Bandelet can provide an efficient representation of geometric structures existed in images, the Bandelet based method can capture abundant textures in SAR images. Moreover, the multiscale Bandelet with adaptive quadtree partition can approximate any piecewise Cα-regular functions whenαis unknown, so Bandelet can represent the hierarchy textures of SAR images efficiently. Embedded Block Coding with Optimal Truncation (EBCOT) coding algorithm is employed in our proposed method. Experimental results show that our method outperforms JPEG2000 and the second generation Bandelet (2G-Bandelet) compression methods on SAR image encoding at low bit rates.

Low bit-rate SAR image encoding using Bandelets with adaptive quadtree partition

Voice Conversion using GMM with Minimum Distance Spectral Mapping Plus Amplitude Scaling

Synthetic Aperture Radar (SAR) images are usually impaired by Speckle Noise that appears as granules that result in degrading the fine details in the image. In order to reduce/remove such speckle noise, various processing algorithms have been considered in literature. In this paper, we introduce a Hybrid Algorithm based on spatial domain as well asfrequency domain processing of SAR images so as to allow for better removal of speckle noise and preservation of the fine details. The proposed hybrid algorithm resulted in better than 30dB improvement in the signal to noise ratio; i.e. in significant reduction/removal of most of the speckle noise from the image while preserving of image’s fine details. Theperformance of the implemented hybrid algorithm for speckle noise reduction has been compared with the published respective performance results obtained by other algorithms. Pros and cons of various algorithms are also given along with conclusions and directions for future work.

https://asat.journals.ekb.eg/article_23253_ec6f95fe26a0432df66db685fcb3c350.pdf

A Hybrid Algorithm for Speckle Noise Reduction in SAR Images

Synthetic aperture radar (SAR) image compression is important in image transmission and archiving. In this paper, SAR image compression using embedded zerotree wavelets algorithm, based on discrete wavelet transform (DWT), is researched. Aiming at special characteristics of SAR image such as speckle noise, the author added a denoise step in the flow of compression, trying to do some meaningful attempt in SAR image compression. Experiments carried out show that the improvement can reduce the speckle noise and improve the computation precision and time.

An Algorithm for SAR Image Embedded Compression Based on Wavelet Transform

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An Algorithm for SAR Image Embedded Compression Based on Wavelet Transform