Manifold-Aware Deep Clustering: Maximizing Angles between Embedding Vectors Based on Regular Simplex

This paper presents a new deep clustering (DC) method called manifold-aware DC (M-DC) that can enhance hyperspace utilization more effectively than the original DC. The original DC has a limitation in that a pair of two speakers has to be embedded having an orthogonal relationship due to its use of the one-hot vector-based loss function, while our method derives a unique loss function aimed at maximizing the target angle in the hyperspace based on the nature of a regular simplex. Our proposed loss imposes a higher penalty than the original DC when the speaker is assigned incorrectly. The change from DC to M-DC can be easily achieved by rewriting just one term in the loss function of DC, without any other modifications to the network architecture or model parameters. As such, our method has high practicability because it does not affect the original inference part. The experimental results show that the proposed method improves the performances of the original DC and its expansion method.Comment: Accepted by Interspeech 202

Novel Audio Feature Projection Using KDLPCCA-Based Correlation with EEG Features for Favorite Music Classification

A novel audio feature projection using Kernel Discriminative Locality Preserving Canonical Correlation Analysis (KDLPCCA)-based correlation with electroencephalogram (EEG) features for favorite music classification is presented in this paper. The projected audio features reflect individual music preference adaptively since they are calculated by considering correlations with the user's EEG signals during listening to musical pieces that the user likes/dislikes via a novel CCA proposed in this paper. The novel CCA, called KDLPCCA, can consider not only a non-linear correlation but also local properties and discriminative information of each class sample, namely, music likes/dislikes. Specifically, local properties reflect intrinsic data structures of the original audio features, and discriminative information enhances the power of the final classification. Hence, the projected audio features have an optimal correlation with individual music preference reflected in the user's EEG signals, adaptively. If the KDLPCCA-based projection that can transform original audio features into novel audio features is calculated once, our method can extract projected audio features from a new musical piece without newly observing individual EEG signals. Our method therefore has a high level of practicability. Consequently, effective classification of user's favorite musical pieces via a Support Vector Machine (SVM) classifier using the new projected audio features becomes feasible. Experimental results show that our method for favorite music classification using projected audio features via the novel CCA outperforms methods using original audio features, EEG features and even audio features projected by other state-of-the-art CCAs

CrossNet-Open-Unmix for Music Source Separation (X-UMXL)

<p>Weights of CrossNet-Open-Unmix (X-UMX) trained on the internal 100h dataset which is larger than <a href="https://sigsep.github.io/datasets/musdb.html">MUSDB18</a>, named X-UMX Large (X-UMXL). The weights can be used with <a href="https://github.com/asteroid-team/asteroid/tree/master/egs/musdb18/X-UMX">X-UMX on Asteroid (PyTorch)</a>. The details of X-UMX are described in <a href="https://ieeexplore.ieee.org/document/9414044">here</a>.</p&gt

CrossNet-Open-Unmix for Music Source Separation (X-UMX)

<p>Weights of CrossNet-Open-Unmix (X-UMX) trained on <a href="https://sigsep.github.io/datasets/musdb.html">MUSDB18</a>. The weights can be used with <a href="https://github.com/asteroid-team/asteroid/tree/master/egs/musdb18/X-UMX">X-UMX on Asteroid (PyTorch)</a>. The details of X-UMX is described in <a href="https://arxiv.org/abs/2010.04228">here</a>.</p&gt

The Whole Is Greater than the Sum of Its Parts: Improving DNN-based Music Source Separation

This paper presents the crossing scheme (X-scheme) for improving the performance of deep neural network (DNN)-based music source separation (MSS) without increasing calculation cost. It consists of three components: (i) multi-domain loss (MDL), (ii) bridging operation, which couples the individual instrument networks, and (iii) combination loss (CL). MDL enables the taking advantage of the frequency- and time-domain representations of audio signals. We modify the target network, i.e., the network architecture of the original DNN-based MSS, by adding bridging paths for each output instrument to share their information. MDL is then applied to the combinations of the output sources as well as each independent source, hence we called it CL. MDL and CL can easily be applied to many DNN-based separation methods as they are merely loss functions that are only used during training and do not affect the inference step. Bridging operation does not increase the number of learnable parameters in the network. Experimental results showed that the validity of Open-Unmix (UMX) and densely connected dilated DenseNet (D3Net) extended with our X-scheme, respectively called X-UMX and X-D3Net, by comparing them with their original versions. We also verified the effectiveness of X-scheme in a large-scale data regime, showing its generality with respect to data size. X-UMX Large (X-UMXL), which was trained on large-scale internal data and used in our experiments, is newly available at https://github.com/asteroid-team/asteroid/tree/master/egs/musdb18/X-UMX.Comment: Submitted to IEEE TASLP (under review), 11 pages, 8 figure