We are often surrounded by intelligent devices with one or more acoustic sensors, which constitute a wireless acoustic sensor network (WASN) and can be exploited for various audio/speech processing tasks. As the captured audio signals are inevitably corrupted by ambient noises, signal enhancement is vital in WASNs. To this end, this paper proposes a distributed-robust and energy-efficient MVDR beamformer (BF) for WASNs. Specifically, a distributed MVDR BF is first derived by recursively updating the inverse of the noise correlation matrix, which requires fewer data transmission without sacrificing performance. Then, its robust version is further designed to alleviate the adverse effects of parameter mismatches. Finally, an energy-efficient network topology control (EENTC) is carried out to reduce the energy consumption, by optimizing the weight matrix of the distributed averaging process. Since the proposed EENTC strategy involves non-convex programming, we transform it into a convex one and solve it via the Dinkelbach algorithm. Unlike the centralized BFs, the proposed method works without an additional central processor. Moreover, it is robust against parameter mismatch during beamforming and can reduce a large amount of data transmission. Simulation and real-world experimental results confirm the validity of the proposed method.

Multi-pitch estimation is a fundamental and key problem in music information retrieval, but still remains challenging due to the intrinsic complexity of polyphonic music. To address this problem, a pseudo 2-D spectrum-based method is proposed in this article. The pseudo 2-D spectrum is first constructed to map the time domain signal into the 2-D frequency space, where the harmonic signal exhibits a typical 2-D pattern. Then, pitch estimation is carried out by cross-correlation between the pseudo 2-D spectrum and the fixed 2-D harmonic template. Finally, the pitches of adjacent frames are grouped into pitch contours, where the contours whose lengths are shorter than the minimum note length limitation are discarded. And the remained pitches are refined using the estimates of neighboring frames by removing probable errors and reconstructing estimates. The proposed method exploits the harmonic structure of pitched sounds in a two-dimensional frequency plane, can work in the case where some notes contain few harmonics, and the harmonic overlap proportions are reduced greatly in the harmony cases. The experimental results show that the proposed method achieves promising performance comparing with the state-of-the-art methods on the evaluation datasets, and outperforms the bispectrum-based method on both evaluation datasets.

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.

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.