Multilevel Stereo Attention Model for Center Channel Extraction Abstract In recent years, the spatial audio reproduction of digital media has become popular. Despite the demand for such spatial audio content, very little content is produced with multichannel audio. Moreover, it is difficult to provide interactive services to users owing to the lack of objectbased content. In this paper, we propose a center channel extraction method based on a multilevel convolutional neural network structure to generate objectbased content. In addition, we present a novel stereo attention model which considers each channelu0027s characteristics. By applying the proposed method to stereo audio content, we achieve better extraction performance than existing commercial application.

Improving Machine Hearing on Limited Data Sets Convolutional neural network (CNN) architectures have originated and revolutionized machine learning for images. In order to take advantage of CNNs in predictive modeling with audio data, standard FFTbased signal processing methods are often applied to convert the raw audio waveforms into an imagelike representations (e.g. spectrograms). Even though conventional images and spectrograms differ in their feature properties, this kind of preprocessing reduces the amount of training data necessary for successful training. In this contribution we investigate how input and target representations interplay with the amount of available training data in a music information retrieval setting. We compare the standard melspectrogram inputs with a newly proposed representation, called Mel scattering. Furthermore, we investigate the impact of additional target data representations by using an augmented target loss function which incorporates unused available information. We observe that all proposed methods outperform the standard meltransform representation when using a limited data set and discuss their strengths and limitations. The source code for reproducibility of our experiments as well as intermediate results and model checkpoints are available in an online repository.