Head-related transfer function (HRTF) plays an important role in the construction of 3D auditory display. This paper presents an individual HRTF modeling method using deep neural networks based on spatial principal component analysis. The HRTFs are represented by a small set of spatial principal components combined with frequency and individual-dependent weights. By estimating the spatial principal components using deep neural networks and mapping the corresponding weights to a quantity of anthropometric parameters, we predict individual HRTFs in arbitrary spatial directions. The objective and subjective experiments evaluate the HRTFs generated by the proposed method, the principal component analysis (PCA) method, and the generic method. The results show that the HRTFs generated by the proposed method and PCA method perform better than the generic method. For most frequencies the spectral distortion of the proposed method is significantly smaller than the PCA method in the high frequencies but significantly larger in the low frequencies. The evaluation of the localization model shows the PCA method is better than the proposed method. The subjective localization experiments show that the PCA and the proposed methods have similar performances in most conditions. Both the objective and subjective experiments show that the proposed method can predict HRTFs in arbitrary spatial directions.

Human listeners are able to selectively attend to target speech in a noisy environment with multiple-people talking. Using recordings of scalp electroencephalogram (EEG), this study investigated how selective attention facilitates the cortical representation of target speech under a simulated “cocktail-party” listening condition with speech-on-speech masking. The result shows that the cortical representation of target-speech signals under the multiple-people talking condition was specifically improved by selective attention relative to the non-selective-attention listening condition, and the beta-band activity was most strongly modulated by selective attention. Moreover, measured with the Granger causality (GC) value, selective attention to the single target speech in the mixed-speech complex enhanced the following 4 causal connectivities for the beta-band oscillation: the ones (1) from site FT7 to the right motor area, (2) from the left frontal area to the right motor area, (3) from the central frontal area to the right motor area, and (4) from the central frontal area to the right frontal area. However, the selective-attention-induced change in beta-band causal connectivity from the central frontal area to the right motor area, but not other beta-band causal connectivities, was significantly correlated with the selective-attention-induced change in the cortical beta-band representation of target speech. These findings suggest that under the “cocktail-party” listening condition, the beta band oscillation in EEGs to target speech is specifically facilitated by selective attention to the target speech that is embedded in the mixed-speech complex. The selective attention-induced unmasking of target speech may be associated with the improved beta-band functional connectivity from the central frontal area to the right motor area, suggesting a top-down attentional modulation of the speech-motor process.

The performance of higherorder Ambisonics (HOA) signals obtained using spherical harmonics decomposition method is disturbed by two primary sources of errors, the noise pollution in low-frequency band and the spatial aliasing in high-frequency band. Inspired by the HOA signals upscale method, which is performed using the sparse character of the sound field, this paper propose a sound field decomposition model based on a sparse deep neural network that offers HOA signals with wider frequency bandwidth. We use the frequency domain multi-scale convolutional network to realize the spherical harmonics decomposition, as well as learning the spatial aliasing pattern, based on which the aliasing-free HOA signals can be derived. Besides, we apply a sparse encoding network to cpature the sparse feature of the sound field which will improve the model performance when the sparse condition is satisfied. The experiments results prove that the proposed model can obtain HOA signals with wider frequency range of operation under multiple sources (up to 10 sources) and low reverberant environments ( $T_{60}\le$ 400 ms). When the sparsity feature cannot be satisfied ( $T_{60} =$ 800 ms), the proposed network model still maintain the same performance as the traditional methods.

To reduce the burden of storing and transmitting audio signals, they are often compressed with a lossy single-channel code. Because the high-frequency components are effectively truncated when using a low bitrate encoder, listeners may experience the sound as being uncomfortable, muffled, or dull. To compensate for the perceived degradation, bandwidth extension technology can be used to regenerate the missing high frequencies from the low-frequency components during the decoding process. In...

The Ambisonic technique has been widely used in fields such as sound-field reproduction, sound source localization, and acoustic noise control. In this paper, a recently proposed Ambisonic decoding method, the partially matching projection method (PMPD), was described in detail and extended for more practical use with near-field sources and reverberant playback environments. Several experiments were conducted to evaluate the performance of the method. In the experiments, we compared performances of PMPD method with three other Ambisonic decoding methods, “All-round Ambisonic Decoding” (AllRAD) method, mode matching decoding (MMD) method and regularized mode matching decoding (RMMD) method. Different playback conditions were considered to investigate the generalization of the method, including unevenly and evenly distributed loudspeakers, as well as free-field and reverberant playback environments. In terms of reproduction errors near sweep-spot and energy of loudspeaker gains, PMPD method worked well for different signals and playback environments. Whereas the other three methods encontered ill-posed problems or large reproduction errors in certain conditions. This means that PMPD method achieves a balence between precise reproductioin near sweet-spot and energy of loudspeaker gains with ill-posed problems. PMPD method has a more robust performance than the other three compared methods for different signals and playback environments. And we conclude that PMPD method is effective to be used as an Ambisonic decoding method, especially for different playback environments.

Existing methods utilizing spatial information for sound source separation require prior knowledge of the direction of arrival (DOA) of the source or utilize estimated but imprecise localization results, which impairs the separation performance, especially when the sound sources are moving. In fact, sound source localization and separation are interconnected problems, that is, sound source localization facilitates sound separation while sound separation contributes to refined source localization. This paper proposes a method utilizing the mutual facilitation mechanism between sound source localization and separation for moving sources. The proposed method comprises three stages. The first stage is initial tracking, which tracks each sound source from the audio mixture based on the source signal envelope estimation. These tracking results may lack sufficient accuracy. The second stage involves mutual facilitation: Sound separation is conducted using preliminary sound source tracking results. Subsequently, sound source tracking is performed on the separated signals, thereby refining the tracking precision. The refined trajectories further improve separation performance. This mutual facilitation process can be iterated multiple times. In the third stage, a neural beamformer estimates precise single-channel separation results based on the refined tracking trajectories and multi-channel separation outputs. Simulation experiments conducted under reverberant conditions and with moving sound sources demonstrate that the proposed method can achieve more accurate separation based on refined tracking results.