23,910 research outputs found
Machine learning-guided synthesis of advanced inorganic materials
Synthesis of advanced inorganic materials with minimum number of trials is of
paramount importance towards the acceleration of inorganic materials
development. The enormous complexity involved in existing multi-variable
synthesis methods leads to high uncertainty, numerous trials and exorbitant
cost. Recently, machine learning (ML) has demonstrated tremendous potential for
material research. Here, we report the application of ML to optimize and
accelerate material synthesis process in two representative multi-variable
systems. A classification ML model on chemical vapor deposition-grown MoS2 is
established, capable of optimizing the synthesis conditions to achieve higher
success rate. While a regression model is constructed on the
hydrothermal-synthesized carbon quantum dots, to enhance the process-related
properties such as the photoluminescence quantum yield. Progressive adaptive
model is further developed, aiming to involve ML at the beginning stage of new
material synthesis. Optimization of the experimental outcome with minimized
number of trials can be achieved with the effective feedback loops. This work
serves as proof of concept revealing the feasibility and remarkable capability
of ML to facilitate the synthesis of inorganic materials, and opens up a new
window for accelerating material development
Listening to features
This work explores nonparametric methods which aim at synthesizing audio from
low-dimensionnal acoustic features typically used in MIR frameworks. Several
issues prevent this task to be straightforwardly achieved. Such features are
designed for analysis and not for synthesis, thus favoring high-level
description over easily inverted acoustic representation. Whereas some previous
studies already considered the problem of synthesizing audio from features such
as Mel-Frequency Cepstral Coefficients, they mainly relied on the explicit
formula used to compute those features in order to inverse them. Here, we
instead adopt a simple blind approach, where arbitrary sets of features can be
used during synthesis and where reconstruction is exemplar-based. After testing
the approach on a speech synthesis from well known features problem, we apply
it to the more complex task of inverting songs from the Million Song Dataset.
What makes this task harder is twofold. First, that features are irregularly
spaced in the temporal domain according to an onset-based segmentation. Second
the exact method used to compute these features is unknown, although the
features for new audio can be computed using their API as a black-box. In this
paper, we detail these difficulties and present a framework to nonetheless
attempting such synthesis by concatenating audio samples from a training
dataset, whose features have been computed beforehand. Samples are selected at
the segment level, in the feature space with a simple nearest neighbor search.
Additionnal constraints can then be defined to enhance the synthesis
pertinence. Preliminary experiments are presented using RWC and GTZAN audio
datasets to synthesize tracks from the Million Song Dataset.Comment: Technical Repor
Mandarin Singing Voice Synthesis Based on Harmonic Plus Noise Model and Singing Expression Analysis
The purpose of this study is to investigate how humans interpret musical
scores expressively, and then design machines that sing like humans. We
consider six factors that have a strong influence on the expression of human
singing. The factors are related to the acoustic, phonetic, and musical
features of a real singing signal. Given real singing voices recorded following
the MIDI scores and lyrics, our analysis module can extract the expression
parameters from the real singing signals semi-automatically. The expression
parameters are used to control the singing voice synthesis (SVS) system for
Mandarin Chinese, which is based on the harmonic plus noise model (HNM). The
results of perceptual experiments show that integrating the expression factors
into the SVS system yields a notable improvement in perceptual naturalness,
clearness, and expressiveness. By one-to-one mapping of the real singing signal
and expression controls to the synthesizer, our SVS system can simulate the
interpretation of a real singer with the timbre of a speaker.Comment: 8 pages, technical repor
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