Search

The recently-proposed deep clustering-based algorithms represent a fundamental advance towards the single-channel multi-speaker speech sep- aration problem. These methods use an ideal binary mask to construct the objective function and K-means clustering method to estimate the ideal bina- ry mask. However, when sources belong to the same class or the number of sources is large, the assumption that one time-frequency unit of the mixture is dominated by only one source becomes weak, and the IBM-based separation causes spectral holes or aliasing. Instead, in our work, the quantized ideal ratio mask was proposed, the ideal ratio mask is quantized to have the output of the neural network with a limited number of possible values. Then the quan- tized ideal ratio mask is used to construct the objective function for the case of multi-source domination, to improve network performance. Furthermore, a network framework that combines a residual network, a recurring network, and a fully connected network was used for exploiting correlation information of frequency in our work. We evaluated our system on TIMIT dataset and show 1.6 dB SDR improvement over the previous state-of-the-art methods.

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.