# Singing Voice Detection in Electronic Music with a Long-Term Recurrent Convolutional Network
###### Abstract  
Singing Voice Detection (SVD) is a classification task that determines whether there is a singing voice in a given audio segment. While current systems produce high-quality results on this task, the reported experiments are usually limited to popular music. A Long-Term Recurrent Convolutional Network (LRCN) was adapted to detect vocals in a new dataset of electronic music to evaluate its performance in a different music genre and compare its results against those in other state-of-the-art experiments in pop music to prove its effectiveness across a different genre. Experiments on two datasets studied the impacts of different audio features and block size on LRCN temporal relationship learning, and the benefits of preprocessing on performance, and the results generate a benchmark to evaluate electronic music and its intricacies. singing voice detection (SVD); music information retrieval (MIR); electronic music; long-term recurrent convolutional network (LRCN) +
Footnote †: journal: Applied  
whereas [PERSON] et al. [14] did it with Discrete Fourier Transform (DFT) coefficients, obtaining higher detection accuracy than MFCCs. [PERSON] et al. [15] proved that a Recurrent Neural Network (RNN) outperformed GMM systems on voice detection activities, and [PERSON] et al. [16] increased its effectiveness with a Long Short-Term Memory (LSTM) network to detect vocal and nonvocal frames. Further attempts have mostly consisted of combining DL techniques to separate tasks and increase their performance. [PERSON] et al. [17] combined a bidirectional RNN with an LSTM (BLSTM-RNNs) and used a temporal context in order to classify the input vector, whereas [PERSON] et al. [18] used a Gated Recurrent Unit (GRU) to allow each unit to adaptively capture dependencies across time scales. Most recently, [PERSON] et al. [11] achieved state-of-the-art performance using SVS as a preprocessing method for classifying input vocal signals and a Long Short-Term Recurrent Convolutional Network (LRCN) to extract and sequence the vocal background in the time domain for vocal detection. The majority of SVD systems are designed and evaluated with popular music. However, [PERSON] et al. [19] showed that these tend to not generalize well to other genres unseen during training, implying that focusing solely on pop music limits their general applicability. There have been attempts to train SVD models with other music genres. [PERSON] et al. [20] proved the generalization capabilities and robustness of two models with opera recordings as a dataset. However, there are still many genres with their own peculiarities that have not been explored yet, such as electronic music. Electronic music has been studied in music generation. [PERSON] et al. [21] developed a Digital Audio Workstation (DAW) plug-in to generate chord sequences and define patterns in EDM through machine learning and model sampling. However, as electronic music focuses more on repetition and looping rather than singing, it has not been explored in other study fields such as MIR. Furthermore, it is evolving into various sub-genres such as dubstep, house, techno, glitch, drum and bass, among others, each one of them with their own intricacies and techniques, so it is still a fresh and developing study area to explore. Particular differences exists between popular and electronic music due to the singing techniques and instrumentation involved. Within electronic music, songs with a heavy emphasis on digital instruments such as synthesizers and samplers, and the usage of tools such as auto-tune, an audio processor that measures and applies subtle corrections to the pitch of vocal and instrument recordings, provide interesting opportunities to test the effectiveness of MIR algorithms in new scenarios. Additionally, previous research has shown that SVD systems may overfit to factors such as loudness [22] and singing style [19], and therefore it is hard to expect them to generalize to unseen genres.