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Lyrics alignment in long music recordings can be memory exhaustive whenperformed in a single pass. In this study, we present a novel method thatperforms audio-to-lyrics alignment with a low memory consumption footprintregardless of the duration of the music recording. The proposed system firstspots the anchoring words within the audio signal. With respect to theseanchors, the recording is then segmented and a second-pass alignment isperformed to obtain the word timings. We show that our audio-to-lyricsalignment system performs competitively with the state-of-the-art, whilerequiring much less computational resources. In addition, we utilise our lyricsalignment system to segment the music recordings into sentence-level chunks.Notably on the segmented recordings, we report the lyrics transcription scoreson a number of benchmark test sets. Finally, our experiments highlight theimportance of the source separation step for good performance on thetranscription and alignment tasks. For reproducibility, we publicly share ourcode with the research community.

Low Resource Audio-to-Lyrics Alignment From Polyphonic Music Recordings