Acoustic Modeling for Automatic Lyrics-to-Audio Alignment
This work addresses automatic lyrics-to-audio alignment for music processing applications, but it is incremental as it builds on existing methods with adaptations and feature enhancements.
The paper tackled the problem of aligning lyrics to polyphonic audio by addressing challenges like corrupted vocals and lack of annotated data, resulting in a significant error reduction in word boundary alignment, especially on challenging data with long musical interludes.
Automatic lyrics to polyphonic audio alignment is a challenging task not only because the vocals are corrupted by background music, but also there is a lack of annotated polyphonic corpus for effective acoustic modeling. In this work, we propose (1) using additional speech and music-informed features and (2) adapting the acoustic models trained on a large amount of solo singing vocals towards polyphonic music using a small amount of in-domain data. Incorporating additional information such as voicing and auditory features together with conventional acoustic features aims to bring robustness against the increased spectro-temporal variations in singing vocals. By adapting the acoustic model using a small amount of polyphonic audio data, we reduce the domain mismatch between training and testing data. We perform several alignment experiments and present an in-depth alignment error analysis on acoustic features, and model adaptation techniques. The results demonstrate that the proposed strategy provides a significant error reduction of word boundary alignment over comparable existing systems, especially on more challenging polyphonic data with long-duration musical interludes.