# Exploring Channel Properties to Improve Singing Voice Detection with Convolutional Neural Networks
###### Abstract  
Singing voice detection is still a challenging task because the voice can be obscured by instruments having the same frequency band, and even the same timbre, produced by mimicking the mechanism of human singing. Because of the poor adaptability and complexity of feature engineering, there is a recent trend towards feature learning in which deep neural networks play the roles of feature extraction and classification. In this paper, we present two methods to explore the channel properties in the convolution neural network to improve the performance of singing voice detection by feature learning. First, channel attention learning is presented to measure the importance of a feature, in which two attention mechanisms are exploited, i.e., the scaled dot-product and squeeze-and-excitation. This method focuses on learning the importance of the feature map so that the neurons can place more attention on the more important feature maps. Second, the multi-scale representations are fed to the input channels, aiming at adding more information in terms of scale. Generally, different songs need different scales of a spectrogram to be represented, and multi-scale representations ensure the network can choose the best one for the task. In the experimental stage, we proved the effectiveness of the two methods based on three public datasets, with the accuracy performance increasing by up to 2.13 percent compared to its already high initial level. singing voice detection; convolutional neural network; scaled dot-product; squeeze-and-excitation; multi-scale channels +
Footnote †: journal: Medical  
voice and non-voice classes using a classifier with the extracted feature as the input. Several classifiers have been employed, from the traditional methods, such as Hidden Markov Models (HMM) [4; 6], Gaussian Mixture Models (GMM) [5], Support Vector Machines [7], and Random Forests [8], to the modern Deep Neural Networks (DNN), such as Convolution Neural Networks (CNNs) [9; 10; 11; 12; 13], and Recurrent Neural Networks (RNNs) [14; 15]. In addition to the feature extraction and classification steps, the preprocessing and postprocessing steps are also beneficial to the results. Singing voice separation is treated as the main preprocessing method for the SVD task [15; 16]. Regarding postprocessing, [PERSON] et al. [16] addressed two methods: the median filter and HMM. To date, the majority of SVD algorithms have focused on feature engineering, and early features drew on the experience of speech recognition. In addition to LPCs and MFCCs mentioned above, many other speech recognition-inspired features have been proposed for SVD, such as Perceptual Linear Prediction Coefficients (PLPCs) [17], Zero-Crossing Rate (ZCR), Spectral Flux (SF), Harmonic Coefficients (HCs), and Log Frequency Power Coefficients (LFPCs) [6]. [PERSON] et al. compared the six features (MFCCs, LFPCs, SF, PLPCs, HCs, and Pitch) based on their delta and double delta coefficients, and found that MFCCs were the most effective features for SVD. However, in the literature, the singing voice is usually represented by the combination of a number of features, rather than a single feature aiming at describing its characteristic from the different aspects. For example, a combination of four features, namely, MFCCs, LPCs, PLPs, and HCs, were employed by [PERSON] and [PERSON] [18] to detect the singing voice, where an HMM was used as a classifier. [PERSON] et al. utilized a more comprehensive combination of the features including MFCCs, LPCs, ZCR, sharpness, spread, f0, and other descriptors, and then fed these into the SVM classifier [19]. Early features identified singing voice as speech, treating the SVD task as discriminating the human voice from a natural environment, in which researchers did not highlight the musical characteristics. Recently, however, researchers have begun to explore the musical characteristics of the singing voice, developing features and adding them as the parts of the feature combination. [PERSON] et al.