Singing voice conversion is a task to convert a song sang by a source singer to the voice of a target singer. In this paper, we propose using a parallel data free, many-to-one voice conversion technique on singing voices. A phonetic posterior feature is first generated by decoding singing voices through a robust Automa…
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WeSinger improves singing voice synthesis with data augmentation and specialized modules.
Deep Autotuner corrects singing pitch using neural networks.
Improved U-Nets with various intermediate blocks enhance singing voice separation.
We study the problem of semi-supervised singing voice separation, in which the training data contains a set of samples of mixed music (singing and instrumental) and an unmatched set of instrumental music. Our solution employs a single mapping function g, which, applied to a mixed sample, recovers the underlying instrum…
SING improves state inference in latent SDE models for better drift function estimation.
We study codimension one (transversally oriented) foliations $\fa$ on oriented closed manifolds having non-empty compact singular set $\sing(\fa)$ which is locally defined by Bott-Morse functions. We prove that if the transverse type of $\fa$ at each singular point is a center and $\fa$ has a compact leaf with fini…
Jukebox generates high-fidelity songs with singing in raw audio.
In recent years, deep learning has surpassed traditional approaches to the problem of singing voice separation. The Wave-U-Net is a recent deep network architecture that operates directly on the time domain. The standard Wave-U-Net is trained with data augmentation and early stopping to prevent overfitting. Minimum hyp…
Deep neural networks with convolutional layers usually process the entire spectrogram of an audio signal with the same time-frequency resolutions, number of filters, and dimensionality reduction scale. According to the constant-Q transform, good features can be extracted from audio signals if the low frequency bands ar…
Singing voice separation attempts to separate the vocal and instrumental parts of a music recording, which is a fundamental problem in music information retrieval. Recent work on singing voice separation has shown that the low-rank representation and informed separation approaches are both able to improve separation qu…
Simplicial sets deformation retract onto transverse simplices.
This paper summarizes some recent advances on a set of tasks related to the processing of singing using state-of-the-art deep learning techniques. We discuss their achievements in terms of accuracy and sound quality, and the current challenges, such as availability of data and computing resources. We also discuss the i…
Separating a singing voice from its music accompaniment remains an important challenge in the field of music information retrieval. We present a unique neural network approach inspired by a technique that has revolutionized the field of vision: pixel-wise image classification, which we combine with cross entropy loss a…
Parallel transport in a fibre bundle with respect to smooth paths in the base space B have recently been extended to representations of the smooth singular simplicial set Sing_{smooth}(B). Inspired by these extensions,I revisit the development of a notion of `parallel' transport in the topological setting of fibrations…
We prove the factoriality of the following nodal threefolds: a complete intersection of hypersurfaces and of degree and respectively, where is smooth, , ; a double cover of a smooth hypersurface $F\subset\mathbb{P}^{…
If and are homotopic embedded surfaces in a -manifold then they may be related by a regular homotopy (at the expense of introducing double points) or by a sequence of stabilisations and destabilisations (at the expense of adding genus). This naturally gives rise to two integer-valued notions of distance bet…
A new algorithm for training generative models using Sinkhorn divergence.
Recent progress in deep learning for audio synthesis opens the way to models that directly produce the waveform, shifting away from the traditional paradigm of relying on vocoders or MIDI synthesizers for speech or music generation. Despite their successes, current state-of-the-art neural audio synthesizers such as Wav…
The paper proves smoothness of almost-minimizers' boundaries near the free boundary.
{\bf Construction.} For a dominating polynomial mapping {} with an isolated critical value at 0 ( an algebraically closed field of characteristic zero) we construct a closed {\it bundle} . We restrict over the critical points of in and partiti…
The singular set of a foliation is always connected under certain conditions.
We present a deep learning method for singing voice conversion. The proposed network is not conditioned on the text or on the notes, and it directly converts the audio of one singer to the voice of another. Training is performed without any form of supervision: no lyrics or any kind of phonetic features, no notes, and …
Recently, the principal component pursuit has received increasing attention in signal processing research ranging from source separation to video surveillance. So far, all existing formulations are real-valued and lack the concept of phase, which is inherent in inputs such as complex spectrograms or color images. Thus,…
Let be a compact and irreducible Hermitian complex space. This paper is devoted to various questions concerning the analytic K-homology of . In the fist part, assuming either or , we show that the rolled-up operator of the minimal -$\overline{\pa…
Establishes functoriality of Baum-Bott residues under specific conditions.
We describe a machine-learning approach to pitch correcting a solo singing performance in a karaoke setting, where the solo voice and accompaniment are on separate tracks. The proposed approach addresses the situation where no musical score of the vocals nor the accompaniment exists: It predicts the amount of correctio…
Study on singularities of area-minimizing currents, focusing on frequency and branch points.
Study minimal hypersurfaces with bounded area and high Morse index using combinatorial methods.
Study quantifies properties of PMC hypersurfaces with area bounds.
In this work we prove a Baum-Bott type formula for non-compact complex manifold of the form , where is a complex compact manifold and is a normal crossing divisor on . As applications, we provide a Poincaré-Hopf type Theorem and an optimal description for a smooth hypersur…
One way to interpret trained deep neural networks (DNNs) is by inspecting characteristics that neurons in the model respond to, such as by iteratively optimising the model input (e.g., an image) to maximally activate specific neurons. However, this requires a careful selection of hyper-parameters to generate interpreta…
Neural networks approximate Calabi-Yau metrics and curvature.
Defines axial curvatures for corank 1 singular manifolds in higher dimensions.
Algorithm learns non-Gaussian graphical models via Hessian scores and triangular transport.
Let be a smooth manifold and let $\F$ be a codimension one, foliation on , with isolated singularities of Morse type. The study and classification of pairs $(M,\F)$ is a challenging (and difficult) problem. In this setting, a classical result due to Reeb \cite{Reeb} states that a manifold admitting a …
Let be a -dimensional complex manifold and two distinct holomorphic self-maps. Suppose that and coincide on a globally irreducible compact hypersurface . We show that if one of the two maps is a local biholomorphism around and, if needed, sits into …
We extend the results of our recent preprint [arXiv: 1811.00515] into higher dimensions . For minimizing harmonic maps from -dimensional domains into the two dimensional sphere we prove: (1) An extension of Almgren and Lieb's linear law, namely \[\mathcal{H}^{n-3}(\textrm{sin…
Sound source separation has attracted attention from Music Information Retrieval(MIR) researchers, since it is related to many MIR tasks such as automatic lyric transcription, singer identification, and voice conversion. In this paper, we propose an intuitive spectrogram-based model for source separation by adapting U-…
Optimal regularity theory for stable minimal hypersurfaces with small singular set.
In this paper, we propose a provably correct algorithm for convolutive nonnegative matrix factorization (CNMF) under separability assumptions. CNMF is a convolutive variant of nonnegative matrix factorization (NMF), which functions as an NMF with additional sequential structure. This model is useful in a number of appl…
Global singularities propagate in magnetic mechanical systems on Riemannian manifolds.
Vector-valued neural learning has emerged as a promising direction in deep learning recently. Traditionally, training data for neural networks (NNs) are formulated as a vector of scalars; however, its performance may not be optimal since associations among adjacent scalars are not modeled. In this paper, we propose a n…
In the top-down approach to multi-name credit modeling, calculation of singe name sensitivities appears possible, at least in principle, within the so-called random thinning (RT) procedure which dissects the portfolio risk into individual contributions. We make an attempt to construct a practical RT framework that enab…
Framework converts singer identity and vocal technique from non-parallel corpora.
Deep clustering is the first method to handle general audio separation scenarios with multiple sources of the same type and an arbitrary number of sources, performing impressively in speaker-independent speech separation tasks. However, little is known about its effectiveness in other challenging situations such as mus…
We study the asymptotics as of stationary -harmonic maps from a compact manifold to , satisfying the natural energy growth condition Along a subsequence , we show that the singular sets converge to the sup…
We consider minimizing harmonic maps from into a closed Riemannian manifold and prove: (1) an extension to of Almgren and Lieb's linear law. That is, if the fundamental group of the target manifold is finite, we have \[ \mathcal{H}^{n-3}(\textrm{sing } …