# Technical Details

# Datasets and Dataloaders

When designing a machine-learnig based method, our first step is to encapsulate cleanly the data-processing aspects.

  • Datasets: we support the MUSDB18 which is the most established dataset for music separation, that we released some years ago (Rafii et al. 2017). The dataset contains 150 full-lengths music tracks (~10h duration) of different musical styles along with their isolated drums, bass, vocals and others stems. MUSDB18 is split into training (100 songs) and test subsets (50 songs). All files from the MUSDB18 dataset are encoded in the Native Instruments stems format (.mp4) to reduce the file size. It is a multitrack format composed of 5 stereo streams, each one encoded in AAC @256kbps. Since AAC is bandwidth limited to 16 kHz instead of 22 kHz for full bandwidth, any model trained on MUSDB18 would not be able to output high-quality content. As part of the release of Open-Unmix, we also released MUSDB18-HQ (Rafii et al. 2019), which is the uncompressed, full-quality version of the MUSDB18 dataset.
  • Efficient data-loading and transforms: since preparing the batches for training is often the efficiency bottleneck, extra-care was taken to optimize speed and performance. Here, we use a framework-specific data loading API instead of a generic module. For all frameworks we use the builtin STFT transform operator, when available, that works on the GPU to improve performance (See (Choi, Joo, and Kim 2017)).
  • Essential augmentations: the data augmentation techniques we adopted here for source separation are described in (Uhlich et al. 2017). They enable to attain good performance even though the audio datasets such as MUSDB18 are often of limited size.
  • Post processing: is an important step that helps to improve the overall performance by combining the outputs of all instrument DNNs. We use a multichannel Wiener filter (MWF) as was proposed in (Nugraha, Liutkus, and Vincent 2016; Sivasankaran et al. 2015) and which we open-sourced in the sigsep.norbert repository

# Model

The system is trained to predict a separated source from the observation of its mixture with other sources. The corresponding training is done in a discriminative way, i.e. through a dataset of mixtures paired with their true separated sources. These are used as ground truth targets from which gradients are computed. Although alternative ways to train a separation system have emerged recently, notably through generative strategies trained through adversarial cost functions, they still did not lead to comparable performance. Even if we acknowledge that such an approach could, in theory, allow scaling the size of training data since it can be done in an unpaired manner, we feel that this direction is still in progress and cannot be considered state-of-the-art today. That said, the Open-Unmix system can easily be extended to such generative training, and the community is much welcome to exploit it for that purpose.


The constitutive parts of the actual deep model used in Open-Unmix only comprise very classical elements, depicted in the Figure above.

  • LSTM: The core of Open-Unmix is a three-layer bidirectional LSTM network (Hochreiter and Schmidhuber 1997). Due to its recurrent nature, the model can be trained and evaluated on arbitrary length of audio signals. Since the model takes information from the past and future simultaneously, the model cannot be used in an online/real-time manner. An uni-directional model can easily be trained.
  • Fully connected time-distributed layers are used for dimensionality reduction and augmentation, thus encoding/decoding the input and output. They allow control over the number of parameters of the model and prove to be crucial for generalization.
  • Skip connections are used in two ways: i/ the output to recurrent layers are augmented with their input, and this proved to help convergence. ii/ The output spectrogram is computed as an element-wise multiplication of the input. This means that the system has to learn how much each TF bin does belong to the target source and not the actual value of that bin. This is critical for obtaining good performance and combining the estimates given for several targets, as done in Open-unmix.
  • Non linearities are of three kinds: i/ rectified linear units (ReLU) allow intermediate layers to comprise nonnegative activations, which long proved effective in TF modeling. ii/ tanh are known to be necessary for good training of LSTM model, notably because they avoid exploding input and output. iii/ a sigmoid activation is chosen before masking, to mimic the way legacy systems take the outputs as a filtering of the input.
  • Batch normalization long proved important for stable training, because it makes the different batches more similar in terms of distributions. In the case of audio where signal dynamics can be very important, this is crucial.

Note that the model can process and predict multichannel spectrograms by stacking features. Furthermore, please note that the input and output to the Open-Unmix core deep model are magnitude spectrograms. Although using phase as additional input feature (Muth et al. 2018) or estimating the instrument phase (Le Roux et al. 2019; Takahashi et al. 2018) are interesting approaches, they have not yet been submitted to international evaluation campaigns like SiSEC for music separation.