# Low Complexity Deep Learning Framework for Greek Orthodox Church Hymns Classification
### Related Work  
DL has found several applications in audio and music signal processing [13]. Although DL has advanced audio technology, leading to commercial applications such as music recommendation systems [14], its main success lies in music information retrieval (MIR) and music generation (MG). MIR may be defined as the set of techniques that are used to extract valuable information from audio data [2], while MG is formed by methods that generate new audio content [15]. Music data classification and identification are formulated as MIR problems; hence, our literature review will focus on this field.  
Several different CNN models have been utilized for music identification and audio classification. The authors in [16] train a CNN model and a tensor deep stacking network for sound classification. The CNN is trained on two datasets with environmental sound data samples. The DL architecture is structured with two convolution layers and one fully connected. Finally, the tensor deep stacking network is trained using two stacked blocks, then with three stacked blocks, and finally with four stacked blocks. The work presented in [7] employs an ensemble of learners for audio classification. More specifically, five pre-trained CNN models, namely AlexNet [17], GoogleNet [18], VGGNet [19], ResNet [20], and InceptionV3 [21], form an ensemble classifier that achieves better performance overall. Two of the most successful CNNs for audio identification tasks are the VGGish and the YAMNet models [22]. VGGish is a pre-trained CNN that is inspired by the VGG networks. The model consists of a series of convolution and activation layers, optionally followed by a max pooling layer. This network contains 17 layers in total. These two models, together with GoogleNet, SqueezeNet [23], and ShuffleNet [24] are tested in a comparative study that is provided in [25]. VGGish achieves the best classification accuracy.  
DL relies heavily on large amounts of data; hence, publicly available datasets are considered valuable. AudioSet [26] is an audio-event dataset widely used in audio classification problems. More specifically, DL models are often pre-trained on AudioSet before their application in a different problem, exploiting the transfer learning capabilities.  
Audio ML/DL on mobile devices has recently emerged as a new research area. In [27], ML algorithms are used for monitoring environmental sounds. The authors seek to find whether it is feasible to create a measurement system for environmental sound monitoring that runs on a handheld device and uses ML to provide meaningful readings. The authors in [28] perform audio-visual speech and gesture recognition using CNN architectures that combine convolution and recurrent operations. A novel DL-based speech enhancement method for dual microphone cell phones is presented in [29] utilizing a CNN with a recurrent architecture. The proposed densely connected convolutional recurrent network employs an encoder-decoder scheme to achieve both a good feature extraction and sequential modeling of the available audio data.  
Recently, DL has been employed for music genre recognition problems. A novel CNN model for Persian music genre recognition, namely PMG-Net, is introduced in [30]. The authors created and made publicly available the PMG-Data dataset, which consists of 500 music samples from different genres of Pop, Rap, Traditional, Rock, and Monody. Furthermore, after the data pre-processing, the desired features are fed to PMG-Net. PMG-Net is based on the VGG-16 architecture; however, it only consists of two equal-size filters in two subsequent layers. The work presented in [31] provides a novel feature extraction algorithm, also utilizing the tunable Q-wavelet transform. The extracted features are fed into several ML classifiers.