Recently, deep neural networks (DNNs) have become the mainstream strategy for speech enhancement task because it can achieve the higher speech quality and intelligibility than the traditional methods. However, these DNN-based methods always need a large number of parallel corpus consisting of clean speech and noise to produce noisy data for the training of the DNN in order to improve the generalization of the network. As a result, this implies that many noisy speech signals that are collected in real environment cannot be used to train the DNN because of the lack of corresponding clean speech and noise. Additionally, as we know, noise varies with the time and scenario, so we cannot obtain parallel speech and noise due to infinite noise data and some limited speech data. Thus, the network training with unparallel speech and noise data is essential for the generalization of the network. To address this problem, we propose a novel parallel-data-free speech enhancement method, in which the cycle-consistent generative adversarial network (CycleGAN) and multi-objective learning are employed. Our method is also able to make best use of the benefits of multi-objective learning. On the training stage, we utilize two different encoders to encode the features of clean speech and noisy speech, respectively. Then, two forward generators are immediately used to predict the ideal time-frequency (T-F) mask and log-power spectrum (LPS) of clean speech. Two inverse generators are applied to map the magnitude spectrum (MS) and LPS of noisy speech, respectively. In addition, four discriminators are used to distinguish the real speech features from the generated features. Two encoders, four generators and four discriminators are simultaneously trained by using adversarial, identity-mapping, latent similarity and cycle-consistent loss. On the test stage, we directly utilize the forward generators and encoders to acquire the enhanced speech. The experimental results indicate that the proposed approach is able to achieve the better speech enhancement performance than the reference methods. Moreover, the proposed method is also effective to improve speech quality and intelligibility when the networks are trained under the parallel data.

In this paper, the direction of arrival (DOA) estimation of multiple speech sources in reverberant environments is investigated based on the recording of a soundfield microphone. First, the recordings are analyzed in the time-frequency (T-F) domain to detect both “points” (single T-F points) and “regions” (multiple, adjacent T-F points) corresponding to a single source with low reverberation (known as low-reverberant-single-source (LRSS) points). Then, a LRSS point detection algorithm is proposed based on a joint dominance measure and instantaneous single-source point (SSP) identification. Following this, initial DOA estimates obtained for the detected LRSS points are analyzed using a Gaussian Mixture Model (GMM) derived by the Expectation-Maximization (EM) algorithm to cluster components into sources or outliers using a rule-based method. Finally, the DOA of each actual source is obtained from the estimated source components. Experiments on both simulated data and data recorded in an actual acoustic chamber demonstrate that the proposed algorithm exhibits improved performance for the DOA estimation in reverberant environments when compared to several existing approaches.

Variations of speech content increase the difficulty of speaker verification. In this paper, to alleviate the negative effect of the variations, phoneme-unit-specific time-delay neural network (PUSTDNN) is proposed and applied to the state-of-the-art x-vector system. It models each phoneme unit with an individual time-delay neural network (TDNN). That is to say, each TDNN mainly deals with a phoneme unit. Compared with handling all phoneme units together, when handling a phoneme unit, a TDNN can extract more discriminative speaker information, thus improving the system performance. Two realizations of the PUSTDNN are proposed. The first one can retain speech temporal information. The second one further combines all the TDNNs in a PUSTDNN into a larger TDNN to reduce computational complexity. To avoid model overfitting, the phoneme units are obtained by clustering phonemes based on the phonetic knowledge and phonetic sparsity degree. The PUSTDNN is also compared with two other techniques, i.e., phonetic vector and multitask. Experiments on the Fisher, NIST SRE10, and VoxCeleb datasets show that the phonetic vector technique is most robust to the phoneme unit recognition accuracy. When the accuracy is high enough, the multitask performs better than the phonetic vector, and the PUSTDNN performs best and can achieve over 10% relative improvement compared with the x-vector baseline.

Multi-channel speech coding and enhancement is an indispensable technology in speech communication. In order to verify the effectiveness of multi-channel speech coding and enhancement methods in the research and development, a microphone array speech simulator (MASS) used in room acoustic environment is proposed. The proposed MASS is the improvement and extension of the existing multi-channel speech simulator. It aims to simulate clean speech, noisy speech, clean speech with reverberation, noisy speech with reverberation, and noise signals by microphone array used for multi-channel coding and enhancement of speech signal in room acoustic environment. The experimental results of the multi-channel speech coding and enhancement prove that the MASS could well simulate the signals used in real room acoustic environment and can be applied to the research of the related fields.

This paper proposes a method to design a planar first-order loudspeaker array structure for global active noise control. Compared with the traditional spherical loudspeaker array, the planar array provides a practical design with flexible source locations. The planar array is capable of achieving global noise control, provided that the loudspeakers have general variable first-order responses in elevation. On x-y plane, we use spherical harmonics to analyze the required first-order loudspeakers consisting of monopole and tangential dipole components. By exploiting the properties of the associated Legendre functions and its derivative, we can divide the primary soundfield into even harmonics controlled by the monopole component, and odd harmonics controlled by the dipole component. Through the appropriate choice of radii of circles, we avoid the ill-conditioning problem of matrix inversion and derive a robust solution for loudspeaker weights to suppress the primary noise field. Besides, we use the closely-located monopole pairs, instead of the ideal general first-order loudspeakers, to design an alternative planar array for practical implementation. As an illustration, we use several simulation examples to validate the performance of the two proposed planar loudspeaker arrays.