Abstract
We present LyricAlly, a prototype that automatically aligns acoustic musical signals with their corresponding textual lyrics, in a manner similar to manually-aligned karaoke. We tackle this problem based on a multimodal approach, using an appropriate pairing of audio and text processing to create the resulting prototype. LyricAlly’s acoustic signal processing uses standard audio features but constrained and informed by the musical nature of the signal. The resulting detected hierarchical rhythm structure is utilized in singing voice detection and chorus detection to produce results of higher accuracy and lower computational costs than their respective baselines. Text processing is employed to approximate the length of the sung passages from the lyrics. Results show an average error of less than one bar for per-line alignment of the lyrics on a test bed of 20 songs (sampled from CD audio and carefully selected for variety). We perform a comprehensive set of system-wide and per-component tests and discuss their results. We conclude by outlining steps for further development.
Min-Yen Kan
Associate Professor
WING lead; interests include Digital Libraries, Information Retrieval and Natural Language Processing.