Abstract
Singing voice detection or vocal detection is a classification task that determines whether there is a singing voice in a given audio segment. This process is a crucial preprocessing step that can be used to improve the performance of other tasks such as automatic lyrics alignment, singing melody transcription, singing voice separation, vocal melody extraction, and many more. This paper presents a survey on the techniques of singing voice detection with a deep focus on state-of-the-art algorithms such as convolutional LSTM and GRU-RNN. It illustrates a comparison between existing methods for singing voice detection, mainly based on the Jamendo and RWC datasets. Long-term recurrent convolutional networks have reached impressive results on public datasets. The main goal of the present paper is to investigate both classical and state-of-the-art approaches to singing voice detection.
| Original language | English |
|---|---|
| Article number | 114 |
| Journal | Entropy |
| Volume | 24 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 2022 |
Keywords
- Datasets
- Deep learning models
- Hidden Markov models
- Mel-frequency cepstrum coefficients
- Music information retrieval
- Perceptual linear prediction
- Short-time Fourier transform
- Singing voice detection
- Support vector machines
- Vocal detection
ASJC Scopus subject areas
- Information Systems
- Mathematical Physics
- Physics and Astronomy (miscellaneous)
- General Physics and Astronomy
- Electrical and Electronic Engineering
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