Singing voice detection or vocal detection is a classification task that determines whether a given audio segment contains singing voices. This task plays a very important role in vocal-related music information retrieval tasks, such as singer identification. Although humans can easily distinguish between singing and nonsinging parts, it is still very difficult for machines to do so. Most existing methods focus on audio feature engineering with classifiers, which rely on the experience of the algorithm designer. In recent years, deep learning has been widely used in computer hearing. To extract essential features that reflect the audio content and characterize the vocal context in the time domain, this study adopted a long-term recurrent convolutional network (LRCN) to realize vocal detection. The convolutional layer in LRCN functions in feature extraction, and the long short-term memory (LSTM) layer can learn the time sequence relationship. The preprocessing of singing voices and accompaniment separation and the postprocessing of time-domain smoothing were combined to form a complete system. Experiments on five public datasets investigated the impacts of the different features for the fusion, frame size, and block size on LRCN temporal relationship learning, and the effects of preprocessing and postprocessing on performance, and the results confirm that the proposed singing voice detection algorithm reached the state-of-the-art level on public datasets.

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.

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