Chord generation is an inherently constrained creative task that requires balancing stylistic diversity with music-theoretic feasibility. Existing approaches typically entangle candidate generation and constraint enforcement within a single model, making the diversity–feasibility trade-off difficult to control and interpret.

Image Coding for Machines (ICM) has yielded significant developments recently. Variable-rate support is necessary for image coding, while performance gap still exists, in learning-based image coding, between the single-model and multiple-fixed-models methods. This paper proposes a Machine Intelligence Variable-Rate Codec (MIVRCodec) with single-model method. We introduce a method to generate, compress, and utilize image semantic feature information, enabling the codec to adaptively process different semantic content of the image. Additionally, current studies employ fixed methods to remove redundant information between luminance and chrominance components, neglecting the dynamic characteristics of this redundancy and leading to its inappropriate utilization. We further propose a Color Dynamic Fusion Module (CDFM), which adaptively fuses image color component features based on various conditions (e.g., bitrate and image content) to utilize the redundancy among image color components as appropriately as possible. Lastly, we propose a Progressive Training Strategy (PTS) for training MIVRCodec. These proposed methods not only reduce performance loss in variable-rate ICM but also improve baseline performance. Experimental results demonstrate that our proposed MIVRCodec works well in the bitrate range corresponding to meaningful accuracy intervals in machine intelligence tasks using a single model, achieving coding efficiency on par with multiple fixed-rate models and surpassing existing state-of-the-art codecs.

Beat and downbeat tracking is to predict beat and downbeat time steps from a given music piece. Some deep learning models with a dilated structure such as Temporal Convolutional Network (TCN) and Dilated Self-Attention Network (DSAN) have achieved promising performance for this task. However, most of them have to see the whole music context during inference, which limits their deployment to online systems. In this paper, we propose LC-Beating, a novel latency-controlled (LC) mechanism for online beat and downbeat tracking, in which the model only looks ahead a few frames. By appending limited future information, the model can better capture the activity of relevant musical beats, which significantly boosts the performance of online algorithms with limited latency. Moreover, LC-Beating applies a novel real-time implementation of the LC mechanism to TCN and DSAN. The experimental results show that our proposed method outperforms the previous online models by a large margin and is close to the results of the offline models.

Chorus detection is a challenging problem in musical signal processing as the chorus often repeats more than once in popular songs, usually with rich instruments and complex rhythm forms. Most of the existing works focus on the receptiveness of chorus sections based on some explicit features such as loudness and occurrence frequency. These pre-assumptions for chorus limit the generalization capacity of these methods, causing misdetection on other repeated sections such as verse. To solve the problem, in this paper we propose an end-to-end chorus detection model DeepChorus, reducing the engineering effort and the need for prior knowledge. The proposed model includes two main structures: i) a Multi-Scale Network to derive preliminary representations of chorus segments, and ii) a Self-Attention Convolution Network to further process the features into probability curves representing chorus presence. To obtain the final results, we apply an adaptive threshold to binarize the original curve. The experimental results show that DeepChorus outperforms existing state-of-the-art methods in most cases.

As one of the fundamental tasks in music and speech signal processing, pitch tracking has been attracting attention for decades. While a human can focus on the voiced pitch even in highly noisy environments, most existing automatic pitch tracking systems show unsatisfactory performance encountering noise. To mimic human auditory, a data-driven model named GIO is proposed in this paper, in which timbre information is introduced to guide pitch tracking. The proposed model takes two inputs: a short audio segment to extract pitch from and a timbre embedding derived from the speaker's or singer's voice. In experiments, we use a music artist classification model to extract timbre embedding vectors. A dual-branch structure and a two-step training method are designed to enable the model to predict voice presence. The experimental results show that the proposed model gains a significant improvement in noise robustness and outperforms existing state-of-the-art methods with fewer parameters.

In the work, we present a novel multiviewpoint and multitemporal remote sensing image registration method based on a dual-features Student-$t$distribution mixture model (DSMM) under a variational Bayesian (VB) framework. The main contributions of the work are: 1) guided image filter (GIF) is adopted to smooth edges and strengthen ridges of images for heightening characteristics of feature point; 2) a Student-$t$distribution mixture model (SMM) based DSMM designs a global and local descriptor to estimate correspondences from local to global scale; and 3) local structure constraints are designed to preserve relationships of neighbors of points and the scale of neighborhood structure of points to constrain transformation. The experimental results demonstrate the better performance of our DSMM against five state-of-the-art methods.

Point-set registration is widely used in computer vision and pattern recognition. However, it has become a challenging problem since the current registration algorithms suffer from the complexities of the point-set distributions. To solve this problem, we propose a robust registration algorithm based on the estimation of distribution algorithm (EDA) to optimize the complex distributions from a global search mechanism. We propose an EDA probability model based on the asymmetric generalized Gaussian mixture model, which describes the area in the solution space as comprehensively as possible and constructs a probability model of complex distribution points, especially for missing and outliers. We propose a transformation and a Gaussian evolution strategy in the selection mechanism of EDA to process the deformation, rotation, and denoising of selected dominant individuals. Considering the complexity of the model, we choose to optimize from the perspective of variational Bayesian, and introduce a prior probability distribution through local variation to reinforce the convergence of the algorithm in dealing with complex point sets. In addition, a local search mechanism based on the simulated annealing algorithm is added to realize the coarse-to-fine registration. Experimental results show that our method has the best robustness compared with the state-of-the-art registration algorithms.

Point set registration plays an important role in computer vision and pattern recognition. In this article, we propose an adaptive hierarchical probabilistic model (HPM) under a variational Bayesian (VB) framework for point set registration problem. The main contributions of this article are given as follows. First, a dynamic putative inlier estimation strategy is proposed through the hesitant fuzzy Einstein weighted averaging based membership calculation and component estimation using symmetric cross entropy. Second, a student-t mixture model based HPM is designed to solve outlier and occlusion problems during registration. Third, a VB-based transformation updating is proposed to construct a robust and adjustable transformation for effectively fitting target point set while further resisting outliers. The performances of the proposed method in point set and image registrations against 11 state-of-the-art methods are evaluated, in which our method gives the best performance in most scenarios.