A Comparison of Sound Segregation Techniques for Predominant Instrument Recognition in Musical Audio Signals
A Comparison of Sound Segregation Techniques for Predominant Instrument Recognition in Musical Audio Signals
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发表时间：
2012
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通讯作者：
Juan J. Bosch;J. Janer;Ferdinand Fuhrmann;P. Herrera
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作者：
Juan J. Bosch;J. Janer;Ferdinand Fuhrmann;P. Herrera
作者解决了多音色音频中主要乐器的识别问题，方法是将原始信号分成几个流。几种策略进行了评估，从低到高的复杂性相对于隔离算法和模型用于分类。感兴趣的数据集是由专业制作的录音构建的，这通常会给最先进的源分离算法带来问题。与原始算法相比，仅使用平移信息进行简单的声音分离预处理，识别结果提高了19%。为了进一步改善结果，我们评估了使用复杂源分离作为前置步骤。结果表明，只有使用从分离的音频流中提取的特征对识别模型进行训练，才能提高识别性能。通过这种方法，识别了现有分离算法的典型误差，使原始仪器识别算法的性能提高了32%。
The authors address the identification of predominant music instruments in polytimbral audio by previously dividing the original signal into several streams. Several strategies are evaluated, ranging from low to high complexity with respect to the segregation algorithm and models used for classification. The dataset of interest is built from professionally produced recordings, which typically pose problems to state-of-art source separation algorithms. The recognition results are improved a 19% with a simple sound segregation pre-step using only panning information, in comparison to the original algorithm. In order to further improve the results, we evaluated the use of a complex source separation as a pre-step. The results showed that the performance was only enhanced if the recognition models are trained with the features extracted from the separated audio streams. In this way, the typical errors of state-of-art separation algorithms are acknowledged, and the performance of the original instrument recognition algorithm is improved in up to 32%.