## 1 Introduction
The authors used the CNN approach to extract the hidden features of fixed length from the input image [11]. These hidden features are given as an input to the RNN for generating captions for the input image. Hence, [PERSON] et al. showed a methodology where the CNN can be used to extract the hidden features from an input image. Due to the successful usage of the CNN in the field of image classification and image captioning, researchers extended the usage of deep convolutional neural networks (DCNN) for automatic music classification [12; 13]. [PERSON], [PERSON], and [PERSON] [12] proposed the usage of the DCNN approach for automatically classifying the songs into different genres. The authors represented the audio signal presented in the song into the log-compressed mel spectrograms. The DCNN uses the mel spectrogram of the song for classifying the songs into different genres. [PERSON], [PERSON], and [PERSON] used the DCNN approach as a scientific way to classify the songs into their genres based on the audio signal presented in the songs. They do not use the metadata (e.g., album name, singer name, popularity, duration, etc.) presented in the songs for classifying the songs. [PERSON], [PERSON], and [PERSON] proved that by using the DCNN approach we can classify the song on different genres based on the audio signals presented in the song. [PERSON] and [PERSON] [13] proposed usage of the DCNN approach for environmental sound classifications (animals, natural sounds, water sounds, etc.) However, above-mentioned DCNN approaches are limited to classifying the songs into different genres based on the audio signals presented in the songs. They do not extract the latent music features from the audio signal presented in that song. They do not concentrate on how to use the metadata presented in the song for extracting latent music features. Therefore, in this paper, we investigate a personalized music recommendation system (PMRS), based on the DCNN approach to extract latent features from the metadata and the audio signal presented in the song. In the PMRS, we convert the audio signal presented in the song into the mel spectrograms as shown in [12]. We give these mel spectrograms and the metadata of the song as the input to the DCNN for extracting the latent features for that song. In this paper, we investigate an emotion-aware PMRS (EPMRS) to recommend songs of different genres based on the user's preference and the user's current emotion. The EPMRS uses music website crawlers to crawl the music data from the music websites. The crawled music data contains the metadata and the audio signals of the songs. Figure 1 shows the strategy of the EPMRS. From Figure 1, it can be depicted that the EPMRS provides music recommendations by correlating the user's data and the music data, which are stored in the database. The user's data contains the user's music listening history and the user's information (such as age, gender, location, ambient, time of the data, emotions, etc.). The EPMRS maintains the user's data for each user who is using the system in the database. Based on the two types of data available, the EPMRS uses two approaches: the DCNN approach and the weighted feature extraction (WFE) approach. The DCNN approach classifies the music data based on the metadata and the audio signals presented in the songs. The DCNN approach uses the data presented in the million-song dataset [14] for classifying the songs. We propose a WFE approach for extracting the latent features from the users to music relationships presented in the user data. The EPMRS combines the outputs of the WFE and the DCNN to learn the user's preference to recommend music according to the user's current emotion. The organization of this paper is as follows. In Section 2, we investigate a brief study of related works for the existing PMRS algorithms. We present the data flow design and the system architecture required for the EPMRS as Section 3. However, in Section 4, we show the mathematical model that we use in the EPMRS.