| Paper ID | AUD-21.5 |
| Paper Title |
LOW RESOURCE AUDIO-TO-LYRICS ALIGNMENT FROM POLYPHONIC MUSIC RECORDINGS |
| Authors |
Emir Demirel, Queen Mary University of London, United Kingdom; Sven Ahlbäck, Doremir Music Research AB, Sweden; Simon Dixon, Queen Mary University of London, United Kingdom |
| Session | AUD-21: Music Information Retrieval and Music Language Processing 4: Structure and Alignment |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 14:00 - 14:45 |
| Presentation Time: | Thursday, 10 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Audio and Acoustic Signal Processing: [AUD-MIR] Music Information Retrieval and Music Language Processing |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Lyrics alignment in long music recordings can be memory exhaustive when performed in a single pass. In this study, we present a novel method that performs audio-to-lyrics alignment with a low memory consumption footprint regardless of the duration of the music recording. The proposed system first spots the anchoring words within the audio signal. With respect to these anchors, the recording is then segmented and a second-pass alignment is performed to obtain the word timings. We show that our audio-to-lyrics alignment system performs competitively with the state-of-the-art, while requiring much less computational resources. In addition, we utilise our lyrics alignment system to segment the music recordings into sentence-level chunks. Notably on the segmented recordings, we report the lyrics transcription scores on a number of benchmark test sets. Finally, our experiments highlight the importance of the source separation step for good performance on the transcription and alignment tasks. For reproducibility, we publicly share our code with the research community. |
