Singing Voice Detection: A Survey

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.

Vocal Melody Extraction via HRNet-Based Singing Voice Separation and Encoder-Decoder-Based F0 Estimation

Vocal melody extraction is an important and challenging task in music information retrieval. One main difficulty is that, most of the time, various instruments and singing voices are mixed according to harmonic structure, making it hard to identify the fundamental frequency (F0) of a singing voice. Therefore, reducing the interference of accompaniment is beneficial to pitch estimation of the singing voice. In this paper, we first adopted a high-resolution network (HRNet) to separate vocals from polyphonic music, then designed an encoder-decoder network to estimate the vocal F0 values. Experiment results demonstrate that the effectiveness of the HRNet-based singing voice separation method in reducing the interference of accompaniment on the extraction of vocal melody, and the proposed vocal melody extraction (VME) system outperforms other state-of-the-art algorithms in most cases.