In this paper, a multi-hypothesis square-root cubature Kalman particle filter (MH-SRCKPF) is proposed for speaker tracking in noisy and reverberant environments with distributed microphone arrays. The conventional cubature Kalman particle filter (CKPF) uses the cubature Kalman filter (CKF) to generate its proposal for particle sampling. Such a proposal incorporates only one observation from a certain localization function for the state estimation, which is vulnerable to noise or reverberation, yielding the degraded tracking performance. To tackle the problem, by incorporating multiple possible observations into CKF for the proposal, a multi-hypothesis CKPF (MH-CKPF) algorithm is first developed. Furthermore, to improve the numerical stability, an MH-SRCKPF algorithm is developed, where the state estimate and the square root of the error covariance are propagated at each time. Finally, the MH-SRCKPF is applied to the speaker tracking problems in distributed microphone arrays. Experimental results demonstrate that the proposed MH-SRCKPF outperforms the competing methods in the presence of noise and reverberation. Meanwhile, by propagating the square root of the state covariance, the proposed method exhibits attractive numerical characteristics.

Coded caching scheme provides us an effective framework to realize additional coded multicasting gain by exploiting coding into multiple transmitted signals. The goal of coded caching design is to jointly optimize the placement and delivery scheme so as to minimize the amount of transmitted coded signals. However, few research efforts consider multiple-round coded caching design problem, in which fixed and mobile users may coexist in one network, namely different number of active users present in successive rounds. Obviously, such dynamic network configurations may lead to undesired frequent placement caching content updating at user sides, if we assume coded caching scheme for all users in each round separately. Thus how to tailor the coded caching design, such that the frequent caching content updating in the placement phase can be avoided, and simultaneously retaining full caching gain in delivery phase in multiple rounds will become highly desirable. In this paper, by carefully examining the bipartite graph representation of the coded caching scheme, a dynamic centralized coded caching design is proposed on the basis of the concatenating-based placement and the saturating matching based delivery scheme. Our analysis unveils that, the proposed dynamic coded caching scheme can realize the flexible coded multicast, and is order optimal.

Melody extraction from polyphonic music is one important but challenging task in the music information retrieval community. In this paper, a new melody extraction method based on the particle filter and dynamic programming is proposed. The constant-Q transform is first introduced for multiresolution spectral analysis of polyphonic music. Then, the melody extraction is modeled in the Bayesian filtering framework, and the particle filter is used to get a rough melody contour. Specially, the pitch transition probability of adjacent frames is approximated according to the statistical analysis based on one publicly available dataset, and the likelihood of frame-wise pitches is defined by considering pitch salience, spectral smoothness, and timbre similarity. After that, the preliminary melodic contour obtained by particle filter is smoothed to achieve the frame-wise pitch range limitation. Finally, the dynamic programming is used to accurately track the final melodic contour. The proposed method requires no prior information, and is suitable for both instrumental and vocal melodies. The experimental results show that the performances of the proposed method is robust among four publicly available datasets comparing with the state-of-the-art methods, and it achieves the highest averaged raw pitch accuracy and raw chroma accuracy performances with lower octave errors.

A speaker tracking method based on a distributed particle filter (DPF) for distributed microphone networks is proposed in this paper. First, the generalized cross-correlation (GCC) function is estimated at each node. To cope with the spurious effects due to the noise or reverberation, multiple delays related to the largest local peaks of the GCC constitute the local observation. Next, based on an optimal fusion rule, a modified DPF is presented, and a modified multiple-hypothesis model is also developed as its likelihood function by incorporating the information of the GCC. Finally, the modified DPF is used to track a moving speaker with a distributed microphone network. The proposed method requires only local communication among neighboring nodes, and is robust against nodes failure. Simulation and real-world experimental results demonstrate the validity of the proposed method.

In this paper, a distributed marginalized auxiliary particle filter (DMAPF) is proposed for speaker tracking in distributed microphone networks. After marginalizing the state-space model, the speaker's velocity and position are estimated using the distributed Kalman filter and the distributed auxiliary particle filter (APF), respectively. To overcome the adverse effects of noise and reverberation, a time difference of arrival selection scheme is presented to construct the local observation vector, based on the generalized cross-correlation function of the microphone pair signals at each node. Next, the multiple-hypothesis model is used as the local likelihood function of the DMAPF. Finally, the DMAPF is employed to estimate the time-varying positions of a moving speaker. The proposed method combines the strengths of the marginalized particle filter, APF, and distributed estimation. It can track the speaker successfully in noisy and reverberant environments. Moreover, it requires only local communication among neighboring nodes, and is scalable for speaker tracking. Experimental results reveal the validity of the proposed speaker tracking method.