958 research outputs found
Surfing the Waves: Live Audio Mosaicing of an Electric Bass Performance as a Corpus Browsing Interface
In this paper, the authors describe how they use an electric bass as a subtle, expressive and intuitive interface to browse the rich sample bank available to most laptop owners. This is achieved by audio mosaicing of the live bass performance audio, through corpus-based concatenative synthesis (CBCS) techniques, allowing a mapping of the multi-dimensional expressivity of the performance onto foreign audio material, thus recycling the virtuosity acquired on the electric instrument with a trivial learning curve. This design hypothesis is contextualised and assessed within the Sandbox#n series of bass+laptop meta-instruments, and the authors describe technical means of the implementation through the use of the open-source CataRT CBCS system adapted for live mosaicing. They also discuss their encouraging early results and provide a list of further explorations to be made with that rich new interface
Learning Timbre Analogies from Unlabelled Data by Multivariate Tree Regression
This is the Author's Original Manuscript of an article whose final and definitive form, the Version of Record, has been published in the Journal of New Music Research, November 2011, copyright Taylor & Francis. The published article is available online at http://www.tandfonline.com/10.1080/09298215.2011.596938
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
Speech synthesis, Speech simulation and speech science
Speech synthesis research has been transformed in recent years through the exploitation of speech corpora - both for statistical modelling and as a source of signals for concatenative synthesis. This revolution in methodology and the new techniques it brings calls into question the received wisdom that better computer voice output will come from a better understanding of how humans produce speech. This paper discusses the relationship between this new technology of simulated speech and the traditional aims of speech science. The paper suggests that the goal of speech simulation frees engineers from inadequate linguistic and physiological descriptions of speech. But at the same time, it leaves speech scientists free to return to their proper goal of building a computational model of human speech production
Using same-language machine translation to create alternative target sequences for text-to-speech synthesis
Modern speech synthesis systems attempt to produce
speech utterances from an open domain of words. In some situations, the synthesiser will not have the appropriate units to pronounce some words or phrases accurately but it still must attempt to pronounce them. This paper presents a hybrid machine translation and unit selection speech synthesis system. The machine translation system was trained with English as the source and target language. Rather than the synthesiser only saying the input text as would happen in conventional synthesis systems, the synthesiser may say an alternative utterance with the same
meaning. This method allows the synthesiser to overcome the
problem of insufficient units in runtime
Singing voice resynthesis using concatenative-based techniques
Tese de Doutoramento. Engenharia InformĂĄtica. Faculdade de Engenharia. Universidade do Porto. 201
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