Automatic Identification of the Sung Language in Popular Music Recordings
Bibliographic Data
| ID | 14872171 |
|---|---|
| Authors | Wei-Ho Tsai (0000-0002-0867-7951, National Taipei University of Technology, corresponding author), Hsin-Min Wang, Hsin‐Min Wang (0000-0003-3599-5071, Academia Sinica) |
| Year | 2007 |
| Volume | 36 |
| Issue | 2 |
| Pages | 105-114 |
| Publication date | 2007-06-01 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of New Music Research (JOURNAL) |
| Journal identifiers | ISSN: 0929-8215 • E-ISSN: 1744-5027 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/09298210701755206 |
| OpenAlex | W2027376282 |
| Language | EN |
| References cited | 14 |
As part of the research into content-based music information retrieval (MIR), this paper presents a preliminary attempt to automatically identify the language sung in popular music recordings. It is assumed that each language has its own set of constraints that specify the sequence of basic linguistic events when lyrics are sung. Thus, the acoustic structure of individual languages may be characterized by statistically modelling those constraints. To achieve this, the proposed method employs vector clustering to convert a singing signal from its spectrum-based feature representation into a sequence of smaller basic phonological units. The dynamic characteristics of the sequence are then analysed using bigram language models. As vector clustering is performed in an unsupervised manner, the resulting system does not need sophisticated linguistic knowledge; therefore, it is easily portable to new language sets. In addition, to eliminate interference from background music, we leverage the statistical estimation of the background musical accompaniment of a song so that the vector clustering truly reflects the solo singing voices in the accompanied signals
Bigram · Cluster analysis · Hidden Markov model · Language identification · Language model · Lyrics · Natural language · Natural language processing · Singing · Speech recognition · Trigram · Computer Science · Music and Audio Processing · Speech and Audio Processing · Speech Recognition and Synthesis · Artificial Intelligence
| Highly cited | No |
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