基于模型在线更新和平滑处理的音乐分割算法

Music segmentation based on model adaptation and smoothing processing

针对音乐分割中预训练模型和待分割信号间的不匹配问题,提出基于置信测度的自适应模型更新算法.在基于预训练模型的识别结果中,通过置信测度选择可靠的数据进行高斯混合模型在线自适应更新,获得与待分割音乐信号更匹配的声乐/非声乐模型.通过对识别结果进行平滑处理,进一步去除瞬时突变错误.实验表明,与初始模型和采用全部数据进行模型更新相比,该算法可获得与待分割信号更匹配的高斯混合模型,分割效果更佳.

An online model adaptation technique for music segmentation was proposed.A confidence measure derived from the recognition likelihoods was adopted for selecting the credible data.The selected data was then used for model adaptation.Compared to the pre-trained models,the adapted ones characterize the acoustic properties of the processing signals more accurately.It implies that higher segmentation accuracy can be achieved.A smoothing processing was applied to further reduce the short segment fluctuation errors from the recognition output.Experimental results show that the significant performance improvement due to the proposed algorithms.