## 1 Introduction  
Background music is a sensitive issue concerning copyright. This issue occurs in a variety of places such as broadcasting, music stores, online music streaming services and so on. To charge for copyright, it is important to know what title the background music is and how long it has been played. It is inaccurate and takes a lot of time and work to manually record the title and the playing time of background music. In order to solve this problem, automatic music identification and music section detection techniques are required. In this study, we perform automatic music identification on broadcast content. Due to the nature of broadcast content, background music is mostly mixed with speech louder than music. This characteristic results in lowering automatic music-identification performance. Therefore, we attempt to apply background music separation technique before automatic music identification. Music signal separation was conventionally done by the traditional methods used in blind source separation (BSS) [1] such as independent component analysis (ICA) [2], non-negative matrix factorization (NMF) [3], and sparse component analysis (SCA) [4]. For monophonic music source separation, which is the same task as in this paper, NMF showed better performance than ICA and SCA [1]. However, NMF does not yield good separation performance in real environmental conditions because the NMF algorithm has inherently linear characteristics [5]. Recently, deep learning-based music source separation algorithms achieved good performance and outperformed the NMF algorithm [6; 7; 8; 9]. In addition, deep learning-based music source separation algorithms have the advantage that they do not have the permutation problem [10] because they are trained to directly produce the target signal. [PERSON] et al. [6] used the feed-forward neural network (FNN) architecture in speech source separation task and achieved better performance than NMF. This means that deep learning is suitable for replacing the conventional algorithms. [PERSON] et al. [7] and [PERSON] et al. [8] experimented with music source separation using the FNN architecture, which is the basic structure in deep learning. They used different datasets, but they showed better performance compared to NMF. After then, a lot of new neural network architectures were applied to audio source separation: convolutional neural network (CNN), recurrent neural network (RNN), bidirectional long-term memory (BLSTM), and so on [11; 12]. In the time-frequency spectrogram domain, the time-axis frames are used as input to the RNN or BLSTM architecture [11; 12]. In a previous study [12], music source separation was performed with a BLSTM architecture. As the layer of BLSTM was stacked up to 3 layers, it showed good performance. When the number of layers was equal to FNN, BLSTM showed better performance than FNN. Data augmentation and Wiener filter for eliminating the stationary noise of the estimated target spectrogram further improved performance. Also, it showed better performance in a multi-channel environment than single-channel. The best performance in this experiment was obtained by weighted summation of estimated signals from BLSTM and FNN. Recently, the CNN-based U-Net [13], stacked hourglass [14], and dense convolutional network (DenseNet) [15], which showed good performance in the image domain [16; 17; 18], were also successfully applied to audio source separation. The CNN-based U-Net [13] and stacked hourglass network [14] have an encoder-decoder style structure. Bottleneck features are generated between encoder-decoder processes. In U-Net, the feature map of each encoder is concatenated with the decoder feature map of the same resolution. The stacked hourglass network adds the feature map of the encoder to the decoder feature map of the same resolution via convolution. The connections between these encoders and decoders are advantageous for the transmission of information and the transmission of gradients.