text: Can we perform an end-to-end sound source separation (SSS) with a variable number of sources using a deep learning model? This paper presents an extension of the Wave-U-Net model which allows end-to-end monaural source separation with a non-fixed number of sources. Furthermore, we propose multiplicative conditioning with instrument labels at the bottleneck of the Wave-U-Net and show its effect on the separation results. This approach can be further extended to other types of conditioning such as audio-visual SSS and score-informed SSS.
  @cite_18: There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http: lmb.informatik.uni-freiburg.de people ronneber u-net .
  @cite_4: 
  @cite_8: The decomposition of a music audio signal into its vocal and backing track components is analogous to image-to-image translation, where a mixed spectrogram is transformed into its constituent sources. We propose a novel application of the U-Net architecture — initially developed for medical imaging — for the task of source separation, given its proven capacity for recreating the fine, low-level detail required for high-quality audio reproduction. Through both quantitative evaluation and subjective assessment, experiments demonstrate that the proposed algorithm achieves state-of-the-art performance.
summary: Wave-U-Net model @cite_4 is an adaptation of the U-Net @cite_18 , a convolutional encoder-decoder network developed for image segmentation. The U-Net approach has been adapted already for singing voice separation in @cite_8 , however this model applies 2D convolutions and works with spectrograms. Instead of doing a 2D convolution, Wave-U-Net performs series of 1D convolutions, downsampling and upsampling with skip connections on a raw waveform signal. The input to this network is a single channel audio mix, and the desired output is the separated @math channels of individual audio sources, where @math is the number of sources present in the audio mix. An interesting aspect of the Wave-U-Net is that it avoids implicit zero paddings in the downsampling layers, and it performs linear interpolation as opposed to de-convolution. This means that our dimension size is not preserved, and our output results will actually become a lot shorter compared to our input. However, by doing this we can better preserve temporal continuity and avoid audio artifacts in the results.