3,167 research outputs found
Emergentism and musicology: an alternative perspective to the understanding of dissonance.
In this paper we develop an approach to musicology within the
discussion of emergentism. First of all, we claim that some theories of
musicology could be insufficient in describing and explaining musical
phenomena when emergent properties are not taken into account. Actually,
musicology usually considers just syntactical elements, structures and
processes and puts only a little emphasis, if any, over perceptual aspects of
human hearing. On the other hand, recent research efforts are currently being
directed towards an understanding of the emergent properties of auditory
perception, especially in fields such as cognitive science. Such research leads
to other views concerning old issues in musicology and could create a fruitful
approach, filling the gap between musicology and auditory perception
How do we think : Modeling Interactions of Perception and Memory
A model of artificial perception based on self-organizing data into hierarchical structures is generalized to abstract thinking. This approach is illustrated using a two-level perception model, which is justified theoretically and tested empirically. The model can be extended to an arbitrary number of levels, with abstract concepts being understood as patterns of stable relationships between data aggregates of high representation levels
Real-Time Audio-to-Score Alignment of Music Performances Containing Errors and Arbitrary Repeats and Skips
This paper discusses real-time alignment of audio signals of music
performance to the corresponding score (a.k.a. score following) which can
handle tempo changes, errors and arbitrary repeats and/or skips (repeats/skips)
in performances. This type of score following is particularly useful in
automatic accompaniment for practices and rehearsals, where errors and
repeats/skips are often made. Simple extensions of the algorithms previously
proposed in the literature are not applicable in these situations for scores of
practical length due to the problem of large computational complexity. To cope
with this problem, we present two hidden Markov models of monophonic
performance with errors and arbitrary repeats/skips, and derive efficient
score-following algorithms with an assumption that the prior probability
distributions of score positions before and after repeats/skips are independent
from each other. We confirmed real-time operation of the algorithms with music
scores of practical length (around 10000 notes) on a modern laptop and their
tracking ability to the input performance within 0.7 s on average after
repeats/skips in clarinet performance data. Further improvements and extension
for polyphonic signals are also discussed.Comment: 12 pages, 8 figures, version accepted in IEEE/ACM Transactions on
Audio, Speech, and Language Processin
Conceptual design study of advanced acoustic composite nacelle
Conceptual nacelle designs for wide-bodied and for advanced-technology transports were studied with the objective of achieving significant reductions in community noise with minimum penalties in airplane weight, cost, and in operating expense by the application of advanced composite materials to nacelle structure and sound suppression elements. Nacelle concepts using advanced liners, annular splitters, radial splitters, translating centerbody inlets, and mixed-flow nozzles were evaluated and a preferred concept selected. A preliminary design study of the selected concept, a mixed flow nacelle with extended inlet and no splitters, was conducted and the effects on noise, direct operating cost, and return on investment determined
Deep Learning for Audio Signal Processing
Given the recent surge in developments of deep learning, this article
provides a review of the state-of-the-art deep learning techniques for audio
signal processing. Speech, music, and environmental sound processing are
considered side-by-side, in order to point out similarities and differences
between the domains, highlighting general methods, problems, key references,
and potential for cross-fertilization between areas. The dominant feature
representations (in particular, log-mel spectra and raw waveform) and deep
learning models are reviewed, including convolutional neural networks, variants
of the long short-term memory architecture, as well as more audio-specific
neural network models. Subsequently, prominent deep learning application areas
are covered, i.e. audio recognition (automatic speech recognition, music
information retrieval, environmental sound detection, localization and
tracking) and synthesis and transformation (source separation, audio
enhancement, generative models for speech, sound, and music synthesis).
Finally, key issues and future questions regarding deep learning applied to
audio signal processing are identified.Comment: 15 pages, 2 pdf figure
Pitch ability as an aptitude for tone learning
Tone languages such as Mandarin use voice pitch to signal lexical contrasts, presenting a challenge for second/foreign language (L2) learners whose native languages do not use pitch in this manner. The present study examined components of an aptitude for mastering L2 lexical tone. Native English speakers with no previous tone language experience completed a Mandarin word learning task, as well as tests of pitch ability, musicality, L2 aptitude, and general cognitive ability. Pitch ability measures improved predictions of learning performance beyond musicality, L2 aptitude, and general cognitive ability and also predicted transfer of learning to new talkers. In sum, although certain nontonal measures help predict successful tone learning, the central components of tonal aptitude are pitch-specific perceptual measures
The Art of Multiphonics: a Progressive Method for Trombone
The Art of Multiphonics serves as a resource for trombone players and teachers wanting to develop multiphonic technique in order to approach the literature for which this technique is required. Its further aims are to provide accurate and relevant information regarding (1) subjective tones and beats, (2) the tuning of multiphonic intervals, and (3) vocal technique. Designed to be accessible to players of intermediate to advanced abilities, this method contains progressively arranged exercises for vocal and split-tone multiphonics. The first five chapters of the method are devoted to vocal multiphonics and contain individual subchapters that focus on one aspect of the larger section. Sections one and two contain interval studies. Chapter one contains exercises where the sung note lies in unison or above the played note; chapter two contains exercises where the sung note lies in unison or below the played note. Each subchapter found in the first two sections corresponds to specific intervals and begins with the most basic exercise. Following preliminary exercises, each subchapter progresses toward parallel and contrary motion while building on previously established technique. Chapter four begins with a simple part-crossing exercise before advancing to multiphonic flexibility exercises. This chapter also includes subsections devoted to more advanced part-crossing exercises, short-and-long-range glissandi, and exercises that incorporate all previously learned techniques. The chapter on split-tone multiphonics (chapter five) is placed at the end of the method because this technique is seen less frequently and is more difficult than vocal multiphonics. Split-tone multiphonics is introduced through a series of lip-bending exercises beginning on the second partial. The appendices include the method with two short practice routines to supplement the player’s daily routine. These routines are designed to ensure that continuous progress is made and that previously established technique is not lost. Additional documents found in the appendices include a list of solo literature requiring multiphonics, a subjective tone table, and a beat table
Lauluyhtyeen intonaation automaattinen määritys
The objective of this study is a specific music signal processing task, primarily intended to help vocal ensemble singers practice their intonation. In this case intonation is defined as deviations of pitch in relation to the note written in the score which are small, less than a semitone. These can be either intentional or unintentional. Practicing intonation is typically challenging without an external ear. The algorithm developed in this thesis combined with the presented application concept can act as the external ear, providing real-time information on intonation to support practicing. The method can be applied to the analysis of recorded material as well. The music signal generated by a vocal ensemble is polyphonic. It contains multiple simultaneous tones with partly or completely overlapping harmonic partials. We need to be able to estimate the fundamental frequency of each tone, which then indicates the pitch of each singer. Our experiments show, that the fundamental frequency estimation method based on the Fourier analysis developed in this thesis can be applied to the automatic analysis of vocal ensembles. A sufficient frequency resolution can be achieved without compromising the time resolution too much by using an adequately sized window. The accuracy and robustness can be further increased by taking advantage of solitary partials. The greatest challenge turned out to be the estimation of tones in octave and unison relationships. These intervals are fairly common in tonal music. This question requires further investigation or another type of approach.Tässä työssä tutkitaan erityistä musiikkisignaalin analysointitehtävää, jonka tarkoi- tuksena on auttaa lauluyhtyelaulajia intonaation harjoittelussa. Intonaatiolla tar- koitetaan tässä yhteydessä pieniä, alle puolen sävelaskeleen säveltasoeroja nuottiin kirjoitettuun sävelkorkeuteen nähden, jotka voivat olla joko tarkoituksenmukaisia tai tahattomia. Intonaation harjoittelu on tyypillisesti haastavaa ilman ulkopuolista korvaa. Työssä kehitetty algoritmi yhdessä esitellyn sovelluskonseptin kanssa voi toimia harjoittelutilanteessa ulkopuolisena korvana tarjoten reaaliaikaista tietoa intonaatiosta harjoittelun tueksi. Vaihtoehtoisesti menetelmää voidaan hyödyntää harjoitusäänitteiden analysointiin jälkikäteen. Lauluyhtyeen tuottama musiikki- signaali on polyfoninen. Se sisältää useita päällekkäisiä säveliä, joiden osasävelet menevät toistensa kanssa osittain tai kokonaan päällekkäin. Tästä signaalista on pystyttävä tunnistamaan kunkin sävelen perustaajuus, joka puolestaan kertoo lau- lajan laulaman sävelkorkeuden. Kokeellisten tulosten perusteella työssä kehitettyä Fourier-muunnokseen perustuvaa taajuusanalyysiä voidaan soveltaa lauluyhtyeen intonaation automaattiseen määritykseen, kun nuottiin kirjoitettua sointua hyödyn- netään analyysin lähtötietona. Sopivankokoista näyteikkunaa käyttämällä päästiin riittävään taajuusresoluutioon aikaresoluution säilyessä kohtuullisena. Yksinäisiä osasäveliä hyödyntämällä voidaan edelleen parantaa tarkkuutta ja toimintavar- muutta. Suurimmaksi haasteeksi osoittautui oktaavi- ja priimisuhteissa olevien intervallien luotettava määritys. Näitä intervallisuhteita esiintyy tonaalisessa musii- kissa erityisen paljon. Tämä kysymys vaatii vielä lisätutkimusta tai uudenlaista lähestymistapaa
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