Real Time Blind Audio Source Separation Based on Machine Learning Algorithms

A. Alghamdi, G. Healy, Hoda A. Abdelhafez
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引用次数: 3

Abstract

Machine learning algorithms, such as ConvTasNet and Demucs, can separate between two interfering signals like music and speech, without any prior information about the mixing operation. The Conv-TasNet algorithm is a fully convolutional time-domain audio separation network while Demucs algorithm is a new waveform-to-waveform model. The Demucs algorithm employs a technique similar to the audio generation model and has larger decoder capacity. The criteria for comparison of these algorithms include high-quality signal separation (no artefacts) and less delay in the execution time. This research examined both algorithms in four experiments: music and male, music and female, music and conversation and music and child. The results were evaluated based on mir_eval and R square, root mean square error (RMSE) and mean absolute Error (MAE) scores. Conv-TasNet had the highest SDR score for music in the music and female experiment, with a high SDR score for child experiment. The SDR value of music in the music and female experiment was high using the Demucs algorithm (7.8), while the child experiment had the highest SDR value (8.15). In terms of average execution time, Conv-TasNet was seven times faster than Demucs. RMSE and MAE were also used for measuring accuracy. RMSE indicates absolute values, and MAE computes the average magnitude of errors between observations and prediction data. Both algorithms showed excellent results and high accuracy in the separation process.
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基于机器学习算法的实时盲音频源分离
机器学习算法,如ConvTasNet和Demucs,可以在没有任何关于混合操作的先验信息的情况下,在音乐和语音等两个干扰信号之间进行分离。卷积- tasnet算法是一种全卷积时域音频分离网络,而Demucs算法是一种新的波形-波形模型。Demucs算法采用了类似于音频生成模型的技术,具有更大的解码器容量。这些算法的比较标准包括高质量的信号分离(无伪影)和较小的执行时间延迟。这项研究在四个实验中检验了这两种算法:音乐与男性、音乐与女性、音乐与对话、音乐与孩子。根据mir_eval和R平方、均方根误差(RMSE)和平均绝对误差(MAE)评分对结果进行评估。在音乐和女性实验中,convs - tasnet对音乐的SDR得分最高,对儿童实验的SDR得分也较高。使用Demucs算法,音乐与女性实验中音乐的SDR值最高(7.8),儿童实验中音乐的SDR值最高(8.15)。就平均执行时间而言,convs - tasnet比Demucs快7倍。测量精度采用RMSE和MAE。RMSE表示绝对值,MAE计算观测数据和预测数据之间的平均误差幅度。两种算法在分离过程中均表现出良好的效果和较高的精度。
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