Automatic lyrics alignment for Cantonese popular music

Wong, Chi Hang; Szeto, Wai; Wong, Kin Hong

doi:10.1007/s00530-006-0055-8

Cited by 18 publications

(8 citation statements)

References 17 publications

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“…When compared to lyric transcription, lyric synchronization presents a simpler prob lem so it can be achieved at a practical level of performance with relative ease and, except for some cases [40], it can target singing with musical accompaniment. Research into lyric synchronization targeting singing with accompaniment can be divided into two cate gories: that using no forced alignment [41][42][43] and that using forced alignment [44][45][46][47][48] . When textual chord information is additionally given, it can be used to improve the accuracy of lyric synchroniza tion [49].…”

Section: Lyric Transcription and Synchronizationmentioning

confidence: 99%

Singing information processing

Goto

2014

2014 12th International Conference on Signal Processing (ICSP)

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This paper introduces singing information processing, which is de fined as music information processing for singing voices. As many people listen to music with a focus on singing, singing is one of the most important elements of music. Singing information process ing attracts attention not only from a scientific point of view but also from the standpoint of commercial applications, such as singing syn thesis, automatic singing pitch correction, query-by-humming, and singing skill evaluation for karaoke. The concept of singing infor mation processing is broad and still emerging.Index Terms-Singing information processing, music informa ti on research

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Section: Lyric Transcription and Synchronizationmentioning

confidence: 99%

Singing information processing

Goto

2014

2014 12th International Conference on Signal Processing (ICSP)

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“…Then, these feature sequences were aligned using dynamic time warping (DTW). In the lyrics and Cantonese pop song alignment system [Wong et al 2006], the vocal line was first enhanced by suppressing the bass clef notes. Then the syllables and their pitches were detected.…”

Section: Related Workmentioning

confidence: 99%

Word level automatic alignment of music and lyrics using vocal synthesis

Maddage¹,

Sim

2010

ACM Trans. Multimedia Comput. Commun. Appl.

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We propose a signal-based approach instead of the commonly used model-based approach, to automatically align vocal music with text lyrics at the word level. In this approach, we use a text-to-speech system to synthesize the singing voice according to the lyrics. In this way, aligning the music signal with the corresponding text lyrics becomes the alignment of two audio signals. This study uses the results of music information modeling and singing voice synthesis. In music information modeling, we study different music representation strategies for music segmentation, music region indexing and region content descriptions; in singing voice synthesis, we generate singing voice by making use of music knowledge to approximate the target vocal line in terms of tempo. The experimental results on a 20-song database show 26.3% and 36.1% word level alignment error rates at eighth note and sixteenth note alignment tolerances respectively. The proposed approach presents an alternative and effective solution to music-lyrics alignment which may require less training dataset.

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“…It basically uses the duration of each phoneme as a cue for synchronization, but it is not always effective because the duration of uttered phonemes differs with their location in the lyrics. Wong et al [2] developed an automatic synchronization system for Cantonese popular music. It uses the tonal characteristics of Cantonese language and compares the tone of each word in the lyrics with the fundamental frequency (F0) of the singing voice.…”

Section: Introductionmentioning

confidence: 99%

Three techniques for improving automatic synchronization between music and lyrics: Fricative detection, filler model, and novel feature vectors for vocal activity detection

Fujihara

Goto

2008

2008 IEEE International Conference on Acoustics, Speech and Signal Processing

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Three techniques are described that improve a previously developed system for automatically synchronizing lyrics with musical audio signals. Although this system achieves state-of-the-art accuracy by extracting vocal vowels from polyphonic sound mixtures and using forced alignment between those vowels and a phoneme network of the lyrics, there was still room for improvement. The first technique detects nonexistence regions in which fricative consonant sounds do not exist, which were not utilized in the previous system, and prohibits the alignment of the fricative phonemes to those regions. The second technique inserts a filler model between phrases of the phoneme network. This model improves the accuracy of the forced alignment by ignoring inter-phrase vowel utterances not included in the lyrics. The third technique introduces novel feature vectors for vocal activity detection that enable a distance calculation between two sets of the harmonic structure without estimating their spectral envelopes. Experimental results showed that all three techniques contribute to improved synchronization.

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Automatic lyrics alignment for Cantonese popular music

Cited by 18 publications

References 17 publications

Singing information processing

Singing information processing

Word level automatic alignment of music and lyrics using vocal synthesis

Three techniques for improving automatic synchronization between music and lyrics: Fricative detection, filler model, and novel feature vectors for vocal activity detection

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