Singing voice separation is one of the most widely studied area in the field of audio signal processing. Recent years, the performance of singing voice separation have been greatly improved with fast development of deep learning. As it seems that the separation quality is almost saturated to be improved using audio contents only, we are digging the way of using highly informative additional information for singing voice, lyrics, to get further improved model. So far, we researched the effectiveness of phonetic information of aligned lyrics for singing voice separation. With recently presented MUSDB18 lyrics dataset, we are currently working on unaligned lyrics-informed singing voice separation network.
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Abstract
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In this paper, we propose a method of utilizing aligned lyrics as additional information to improve the performance of singing voice separation. We have combined the highway network-based lyrics encoder into Open-unmix separation network and show that the model trained with the aligned lyrics indeed results in a better performance than the model that was not informed. The question now remains whether the increase of performance is actually due to the phonetic contents that lie in the informed aligned lyrics or not. To this end, we investigated the source of performance increase in multifaceted ways by observing the change of performance when incorrect lyrics were given to the model. Experiment results show that the model can use not only just vocal activity information but also the phonetic contents from the aligned lyrics.
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To be continued