# Exploring Channel Properties to Improve Singing Voice Detection with Convolutional Neural Networks
###### Abstract
presented a SVD method using the vibrato and tremolo parameters, generated from the selection of partials extracted from the audio [20]. It was supposed that the singing voice was characterized by harmonicity, formants, vibrato, and tremolo. This idea originated purely from the musical characteristics. [PERSON] et al. combined four features [21] for vocal activity detection. These features were not only drawn from speech recognition, but also from pitch fluctuation and melody. Similarly, [PERSON] et al. added the Fluctogram, which is a discrete measure of pitch fluctuation, and reliability indicators, to the feature combination [11], and argued that the algorithm was loudness invariant. [PERSON] et al. also employed the feature from the music characteristic, i.e., Chroma, in his bucket, which was composed of eight different features [16]. Figure 1: An illustration of singing voice detection. The upper image is the waveform of a music clip, and the lower image is the spectrogram. As a result of feature engineering plus powerful classification capability, DNNs have resulted in state-of-the-art SVD performance. [PERSON] proposed a state-of-the-art method, based on a LSTM (Long Short-Term Memory) RNN, having 29 features including the Fluctogram and MFCCs. [PERSON] presented state-of-the-art performance based on a CNN with data augmentation [10]. CNNs can effectively work in the spatial domain due to the convolution between neurons and feature maps, which can enable good performance in the image processing field. However, because the CNNs are not capable of memorization, they are not suitable for processing sequences associated with time. In [16], [PERSON] et al. tried to employ a LRCN (Long-Term Recurrent Convolutional Network) to integrate the advantages of CNN and RNN, i.e., spatially learning features via CNN and temporally learning the relationships via RNN. In their algorithm, they fed the feature engineered vector, and the 288 combined coefficients, to the LRCN. With the help of the singing voice separation preprocessing step and the HMM-based postprocessing step, they obtained very good results on the public datasets. DNNs are not only capable of classification, but also feature learning. Therefore, the feature learning capability of DNNs can be used to undertake the work of feature engineering in the SVD framework, and, ultimately, this step can even be eliminated. Feature engineering has a large number of advantages, including interpretability, deterministic nature, and low computation cost. However, every coin has two sides. One of the disadvantages of feature engineering is that the artificial feature has poor adaptability. Thus, a new dataset or situation requires new engineering, such as in the case of designing reliability indicators for the Fluctogram [11]. Another disadvantage is that, when elaborating the complicated high-level features for the singing voice characteristics, some useful information is lost because high-level features are irreversible to the original signal. As a result, the DNN cannot be trained to find that useful information. Therefore, there is a trend towards feature learning with DNNs in which only low-level representation is fed to them. In this way, [PERSON] [10; 22] used a CNN and [PERSON] used an RNN [15]. The input of the former was the mel spectrogram (in the current study, we identified the macroscopic spectrogram with the microscopic image from the spectrogram fed to the DNN), whereas that of the latter was the log-mel spectrogram. Only slight differences were seen in the two approaches, which achieved performances on par with the state-of-the-art methods using feature engineering. In this study, we focused on exploring channel properties to improve the SVD performance based on the condition that a CNN is used for feature learning with low-level representation input, i.e., the log-mel spectrogram. CNNs work spatially and the channels hold the core information residing on the layers of the CNNs.