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Object-based audio content is becoming the main form of audio content, because it is more interactive and flexible than traditional channel-based audio content. The Spatial Audio Object Coding (SAOC) method is proposed to encode multiple audio objects at low bitrate. However, SAOC extracts only a few parameters for each frame signal, which leads to low parameter frequency resolution. So the decoded signals have serious aliasing distortion which will destroy the sound quality. In this paper, we present a novel audio object coding method. We are the first to analyze how the signal distortion varies with parameter frequency resolution, and determine the optimal resolution to reduce aliasing distortion. In addition, we also achieve low coding bitrate by the dimensional reduction algorithm. Both the objective and subjective experiments confirm that the proposed method can provide higher sound quality of output signals than the state-of-the-art methods at equivalent bitrate.

Object-based audio coding is the main technique of audio scene coding. It can effectively reconstruct each object trajectory, besides provide sufficient flexibility for personalized audio scene reconstruction. So more and more attentions have been paid to the object-based audio coding. However, existing object-based techniques have poor sound quality because of low parameter frequency domain resolution. In order to achieve high quality audio object coding, we propose a new coding framework with introducing the non-negative matrix factorization (NMF) method. We extract object parameters with high resolution to improve sound quality, and apply NMF method to parameter coding to reduce the high bitrate caused by high resolution. And the experimental results have shown that the proposed framework can improve the coding quality by 25%, so it can provide a better solution to encode audio scene in a more flexible and higher quality way.

In this paper, a new attractor points estimation method for DANet algorithm used in single channel multi-speaker speech separation has been proposed. A prerequisite is that there must be separate segments of each source in the mixture. This condition is met in the actual situation because the source signal is not overlapping at any time. With this prerequisite, an isolated source segments extracted from the mixture is converted to the embedding space. With the embedding of isolated source segments, a more accurately attractor point for each source will be created, due to it does not contain components of other sources. In addition, a gaussian mixture model(GMM) clustering method instead of K-means clustering method were used at run time. The experiment demonstrated that the proposed method gets a better separation performance than state of the art method up to 1.04dB in SDR.

Head related transfer function HRTF is the main technique of binaural synthesis, which is used to reconstruct spatial sound image, and the HRTF data only can be obtained by measurement. A high resolution HRTF database contains too many HRTFs, the workload of measurement is too huge to be finished. As a solution, in order to calculate new HRTF by measured HRTFs, many researchers concentrate on the interpolation of HRTF. But, before interpolating, HRTFs should be aligned because there is time delay between different HRTFs. Some researchers try to implement aligning operation based on phase, but the method is not appropriate since the periodicity of phase. Another idea to align HRTFs is by detecting method, however, the time difference is too tiny to detect exactly. None of the methods can provide a good and stable performance. In this paper, we propose a new method to align HRTFs based on correlation. And the experiments show that the proposed aligning method improves the accuracy index SDR 18.5i¾?dB for the most, furthermore, the proposed method could improve the accuracy for all positions.