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An artificial neural network is an important model for training features of voice conversion (VC) tasks. Typically, neural networks (NNs) are very effective in processing nonlinear features, such as Mel Cepstral Coefficients (MCC), which represent the spectrum features. However, a simple representation of fundamental frequency (F0) is not enough for NNs to deal with emotional voice VC. This is because the time sequence of F0 for an emotional voice changes drastically. Therefore, in our previous method, we used the continuous wavelet transform (CWT) to decompose F0 into 30 discrete scales, each separated by one third of an octave, which can be trained by NNs for prosody modeling in emotional VC. In this study, we propose the arbitrary scales CWT (AS-CWT) method to systematically capture F0 features of different temporal scales, which can represent different prosodic levels ranging from micro-prosody to sentence levels. Meanwhile, the proposed method uses deep belief networks (DBNs) to pre-train the NNs that then convert spectral features. By utilizing these approaches, the proposed method can change the spectrum and the F0 for an emotional voice simultaneously as well as outperform other state-of-the-art methods in terms of emotional VC.

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Deep learning techniques have been successfully applied to speech processing. Typically, neural networks (NNs) are very effective in processing nonlinear features, such as mel cepstral coefficients (MCC), which represent the spectrum features in voice conversion (VC) tasks. Despite these successes, the approach is restricted to problems with moderate dimension and sufficient data. Thus, in emotional VC tasks, it is hard to deal with a simple representation of fundamental frequency (F0), which is the most important feature in emotional voice representation, Another problem is that there are insufficient emotional data for training. To deal with these two problems, in this paper, we propose the adaptive scales continuous wavelet transform (AS-CWT) method to systematically capture the F0 features of different temporal scales, which can represent different prosodic levels ranging from micro-prosody to sentence levels. Meanwhile, we also use the pre-trained conversion functions obtained from other emotional datasets to synthesize new emotional data as additional training samples for target emotional voice conversion. Experimental results indicate that our proposed method achieves the best performance in both objective and subjective evaluations.

An artificial neural network is one of the most important models for training features of voice conversion (VC) tasks. Typically, neural networks (NNs) are very effective in processing nonlinear features, such as mel cepstral coefficients (MCC) which represent the spectrum features. However, a simple representation for fundamental frequency (F0) is not enough for neural networks to deal with an emotional voice, because the time sequence of F0 for an emotional voice changes drastically. Therefore, in this paper, we propose an effective method that uses the continuous wavelet transform (CWT) to decompose F0 into different temporal scales that can be well trained by NNs for prosody modeling in emotional voice conversion. Meanwhile, the proposed method uses deep belief networks (DBNs) to pretrain the NNs that convert spectral features. By utilizing these approaches, the proposed method can change the spectrum and the prosody for an emotional voice at the same time, and was able to outperform other state-of-the-art methods for emotional voice conversion.

The choice of image feature representation plays a crucial role in the analysis of visual information. Although vast numbers of alternative robust feature representation models have been proposed to improve the performance of different visual tasks, most existing feature representations (e.g. handcrafted features or Convolutional Neural Networks (CNN)) have a relatively limited capacity to capture the highly orientationinvariant (rotation/reversal) features. The net consequence is suboptimal visual performance. To address these problems, this study adopts a novel transformational approach, which investigates the potential of using polar feature representations. Our low level consists of a histogram of oriented gradient, which is then binned using annular spatial bin-type cells applied to the polar gradient. This gives gradient binning invariance for feature extraction. In this way, the descriptors have significantly enhanced orientation-invariant capabilities. The proposed feature representation, termed orientation-invariant histograms of oriented gradients (Oi-HOG), is capable of accurately processing visual tasks (e.g., facial expression recognition). In the context of the CNN architecture, we propose two polar convolution operations, referred to as Full Polar Convolution (FPolarConv) and Local Polar Convolution (LPolarConv), and use these to develop polar architectures for the CNN orientation-invariant representation. Experimental results show that the proposed orientation-invariant image representation, based on polar models for both handcrafted features and deep learning features, is both competitive with state-of-the-art methods and maintains a compact representation on a set of challenging benchmark image datasets. Index Terms-Rotation-invariant and reversal-invariant representation, HOG, CNN.

In this paper, we propose a novel neutral-to-emotional voice conversion (VC) model that can effectively learn a mapping from neutral to emotional speech with limited emotional voice data. Although conventional VC techniques have achieved tremendous success in spectral conversion, the lack of representations in fundamental frequency (F0), which explicitly represents prosody information, is still a major limiting factor for emotional VC. To overcome this limitation, in our proposed model, we outline the practical elements of the cross-wavelet transform (XWT) method, highlighting how such a method is applied in synthesizing diverse representations of F0 features in emotional VC. The idea is (1) to decompose F0 into different temporal level representations using continuous wavelet transform (CWT); (2) to use XWT to combine different CWT-F0 features to synthesize interaction XWT-F0 features; (3) and then use both the CWT-F0 and corresponding XWT-F0 features to train the emotional VC model. Moreover, to better measure similarities between the converted and real F0 features, we applied a VA-GAN training model, which combines a variational autoencoder (VAE) with a generative adversarial network (GAN). In the VA-GAN model, VAE learns the latent representations of high-dimensional features (CWT-F0, XWT-F0), while the discriminator of the GAN can use the learned feature representations as a basis for a VAE reconstruction objective.