Previous studies have shown the importance of introducing power compression on both feature and target when only the magnitude is considered in the dereverberation task. When both real and imaginary components are estimated without power compression, it has been shown that it is important to take magnitude constraint into account. In this paper, both power compression and phase estimation are considered to show their equal importance in the dereverberation task, where we propose to reconstruct the compressed real and imaginary components (cRI) for training. Both objective and subjective results reveal that better dereverberation can be achieved when using cRI.

Deep learning-based speech enhancement algorithms have shown their powerful ability in removing both stationary and non-stationary noise components from noisy speech observations. But they often introduce artificial residual noise, especially when the training target does not contain the phase information, e.g., ideal ratio mask, or the clean speech magnitude and its variations. It is well-known that once the power of the residual noise components exceeds the noise masking threshold of the human auditory system, the perceptual speech quality may degrade. One intuitive way is to further suppress the residual noise components by a postprocessing scheme. However, the highly non-stationary nature of this kind of residual noise makes the noise power spectral density (PSD) estimation a challenging problem. To solve this problem, the paper proposes three strategies to estimate the noise PSD frame by frame, and then the residual noise can be removed effectively by applying a gain function based on thedecision-directedapproach. The objective measurement results show that the proposed postfiltering strategies outperform the conventional postfilter in terms of segmental signal-to-noise ratio (SNR) as well as speech quality improvement. Moreover, the AB subjective listening test shows that the preference percentages of the proposed strategies are over 60%.

For voice communication, it is important to extract the speech from its noisy version without introducing unnaturally artificial noise. By studying the subband mean-squared error (MSE) of the speech for unsupervised speech enhancement approaches and revealing its relationship with the existing loss function for supervised approaches, this paper derives a generalized loss function that takes residual noise control into account with a supervised approach. Our generalized loss function contains the well-known MSE loss function and many other often-used loss functions as special cases. Compared with traditional loss functions, our generalized loss function is more flexible to make a good trade-off between speech distortion and noise reduction. This is because a group of well-studied noise shaping schemes can be introduced to control residual noise for practical applications. Objective and subjective test results verify the importance of residual noise control for the supervised speech enhancement approach.

Voice conversion is to transform a source speaker to the target one, while keeping the linguistic content unchanged. Recently, one-shot voice conversion gradually becomes a hot topic for its potentially wide range of applications, where it has the capability to convert the voice from any source speaker to any other target speaker even when both the source speaker and the target speaker are unseen during training. Although a great progress has been made in one-shot voice conversion, the naturalness of the converted speech remains a challenging problem. To further improve the naturalness of the converted speech, this paper proposes a two-level nested U-structure (U²-Net) voice conversion algorithm called U²-VC. The U²-Net can extract both local feature and multi-scale feature of log-mel spectrogram, which can help to learn the time-frequency structures of the source speech and the target speech. Moreover, we adopt sandwich adaptive instance normalization (SaAdaIN) in decoder for speaker identity transformation to retain more content information of the source speech while maintaining the speaker similarity between the converted speech and the target speech. Experiments on VCTK dataset show that U²-VC outperforms many SOTA approaches including AGAIN-VC and AdaIN-VC in terms of both objective and subjective measurements.

Elliptic Fourier analysis (EFA) is a powerful tool for shape analysis, which is often employed in geometric morphometrics. However, the normalization of elliptic Fourier descriptors has persistently posed challenges in obtaining unique results in basic contour transformations, requiring extensive manual alignment. Additionally, contemporary contour/outline extraction methods often struggle to handle complex digital images. Here, we reformulated the procedure of EFDs calculation to improve computational efficiency and introduced a novel approach for EFD normalization, termed true EFD normalization, which remains invariant under all basic contour transformations. These improvements are crucial for processing large sets of contour curves collected from different platforms with varying transformations. Based on these improvements, we developed ElliShape, a user-friendly software. Particularly, the improved contour/outline extraction employs an interactive approach that combines automatic contour generation for efficiency with manual correction for essential modifications and refinements. We evaluated ElliShape's stability, robustness, and ease of use by comparing it with existing software using standard datasets. ElliShape consistently produced reliable reconstructed shapes and normalized EFD values across different contours and transformations, and it demonstrated superior performance in visualization and efficient processing of various digital images for contour analysis.The output annotated images and EFDs could be utilized in deep learning-based data training, thereby advancing artificial intelligence in botany and offering innovative solutions for critical challenges in biodiversity conservation, species classification, ecosystem function assessment, and related critical issues.