592 research outputs found
Pitch-Informed Solo and Accompaniment Separation
ï»żDas Thema dieser Dissertation ist die Entwicklung eines Systems zur
Tonhöhen-informierten Quellentrennung von Musiksignalen in Soloinstrument
und Begleitung. Dieses ist geeignet, die dominanten Instrumente aus einem
MusikstĂŒck zu isolieren, unabhĂ€ngig von der Art des Instruments, der
Begleitung und Stilrichtung. Dabei werden nur einstimmige
Melodieinstrumente in Betracht gezogen. Die Musikaufnahmen liegen monaural
vor, es kann also keine zusÀtzliche Information aus der Verteilung der
Instrumente im Stereo-Panorama gewonnen werden.
Die entwickelte Methode nutzt Tonhöhen-Information als Basis fĂŒr eine
sinusoidale Modellierung der spektralen Eigenschaften des Soloinstruments
aus dem Musikmischsignal. Anstatt die spektralen Informationen pro Frame zu
bestimmen, werden in der vorgeschlagenen Methode Tonobjekte fĂŒr die
Separation genutzt. Tonobjekt-basierte Verarbeitung ermöglicht es,
zusÀtzlich die NotenanfÀnge zu verfeinern, transiente Artefakte zu
reduzieren, gemeinsame Amplitudenmodulation (Common Amplitude Modulation
CAM) einzubeziehen und besser nichtharmonische Elemente der Töne
abzuschÀtzen. Der vorgestellte Algorithmus zur Quellentrennung von
Soloinstrument und Begleitung ermöglicht eine Echtzeitverarbeitung und ist
somit relevant fĂŒr den praktischen Einsatz.
Ein Experiment zur besseren Modellierung der ZusammenhÀnge zwischen
Magnitude, Phase und Feinfrequenz von isolierten Instrumententönen wurde
durchgefĂŒhrt. Als Ergebnis konnte die KontinuitĂ€t der zeitlichen
EinhĂŒllenden, die InharmonizitĂ€t bestimmter Musikinstrumente und die
Auswertung des Phasenfortschritts fĂŒr die vorgestellte Methode ausgenutzt
werden. ZusĂ€tzlich wurde ein Algorithmus fĂŒr die Quellentrennung in
perkussive und harmonische Signalanteile auf Basis des Phasenfortschritts
entwickelt. Dieser erreicht ein verbesserte perzeptuelle QualitÀt der
harmonischen und perkussiven Signale gegenĂŒber vergleichbaren Methoden nach
dem Stand der Technik.
Die vorgestellte Methode zur Klangquellentrennung in Soloinstrument und
Begleitung wurde zu den Evaluationskampagnen SiSEC 2011 und SiSEC 2013
eingereicht. Dort konnten vergleichbare Ergebnisse im Hinblick auf
perzeptuelle BewertungsmaĂe erzielt werden. Die QualitĂ€t eines
Referenzalgorithmus im Hinblick auf den in dieser Dissertation
beschriebenen Instrumentaldatensatz ĂŒbertroffen werden.
Als ein Anwendungsszenario fĂŒr die Klangquellentrennung in Solo und
Begleitung wurde ein Hörtest durchgefĂŒhrt, der die QualitĂ€tsanforderungen
an Quellentrennung im Kontext von Musiklernsoftware bewerten sollte. Die
Ergebnisse dieses Hörtests zeigen, dass die Solo- und Begleitspur gemĂ€Ă
unterschiedlicher QualitÀtskriterien getrennt werden sollten. Die
Musiklernsoftware Songs2See integriert die vorgestellte
Klangquellentrennung bereits in einer kommerziell erhÀltlichen Anwendung.This thesis addresses the development of a system for pitch-informed solo
and accompaniment separation capable of separating main instruments from
music accompaniment regardless of the musical genre of the track, or type
of music accompaniment. For the solo instrument, only pitched monophonic
instruments were considered in a single-channel scenario where no panning
or spatial location information is available.
In the proposed method, pitch information is used as an initial stage of a
sinusoidal modeling approach that attempts to estimate the spectral
information of the solo instrument from a given audio mixture. Instead of
estimating the solo instrument on a frame by frame basis, the proposed
method gathers information of tone objects to perform separation.
Tone-based processing allowed the inclusion of novel processing stages for
attack refinement, transient interference reduction, common amplitude
modulation (CAM) of tone objects, and for better estimation of non-harmonic
elements that can occur in musical instrument tones. The proposed solo and
accompaniment algorithm is an efficient method suitable for real-world
applications.
A study was conducted to better model magnitude, frequency, and phase of
isolated musical instrument tones. As a result of this study, temporal
envelope smoothness, inharmonicty of musical instruments, and phase
expectation were exploited in the proposed separation method. Additionally,
an algorithm for harmonic/percussive separation based on phase expectation
was proposed. The algorithm shows improved perceptual quality with respect
to state-of-the-art methods for harmonic/percussive separation.
The proposed solo and accompaniment method obtained perceptual quality
scores comparable to other state-of-the-art algorithms under the SiSEC 2011
and SiSEC 2013 campaigns, and outperformed the comparison algorithm on the
instrumental dataset described in this thesis.As a use-case of solo and
accompaniment separation, a listening test procedure was conducted to
assess separation quality requirements in the context of music education.
Results from the listening test showed that solo and accompaniment tracks
should be optimized differently to suit quality requirements of music
education. The Songs2See application was presented as commercial music
learning software which includes the proposed solo and accompaniment
separation method
Automatic characterization and generation of music loops and instrument samples for electronic music production
Repurposing audio material to create new music - also known as sampling - was a foundation of electronic music and is a fundamental component of this practice. Currently, large-scale databases of audio offer vast collections of audio material for users to work with. The navigation on these databases is heavily focused on hierarchical tree directories. Consequently, sound retrieval is tiresome and often identified as an undesired interruption in the creative process.
We address two fundamental methods for navigating sounds: characterization and generation. Characterizing loops and one-shots in terms of instruments or instrumentation allows for organizing unstructured collections and a faster retrieval for music-making. The generation of loops and one-shot sounds enables the creation of new sounds not present in an audio collection through interpolation or modification of the existing material. To achieve this, we employ deep-learning-based data-driven methodologies for classification and generation.Repurposing audio material to create new music - also known as sampling - was a foundation of electronic music and is a fundamental component of this practice. Currently, large-scale databases of audio offer vast collections of audio material for users to work with. The navigation on these databases is heavily focused on hierarchical tree directories. Consequently, sound retrieval is tiresome and often identified as an undesired interruption in the creative process.
We address two fundamental methods for navigating sounds: characterization and generation. Characterizing loops and one-shots in terms of instruments or instrumentation allows for organizing unstructured collections and a faster retrieval for music-making. The generation of loops and one-shot sounds enables the creation of new sounds not present in an audio collection through interpolation or modification of the existing material. To achieve this, we employ deep-learning-based data-driven methodologies for classification and generation
16th Sound and Music Computing Conference SMC 2019 (28–31 May 2019, Malaga, Spain)
The 16th Sound and Music Computing Conference (SMC 2019) took place in Malaga, Spain, 28-31 May 2019 and it was organized by the Application of Information and Communication Technologies Research group (ATIC) of the University of Malaga (UMA). The SMC 2019 associated Summer School took place 25-28 May 2019. The First International Day of Women in Inclusive Engineering, Sound and Music Computing Research (WiSMC 2019) took place on 28 May 2019. The SMC 2019 TOPICS OF INTEREST included a wide selection of topics related to acoustics, psychoacoustics, music, technology for music, audio analysis, musicology, sonification, music games, machine learning, serious games, immersive audio, sound synthesis, etc
Final Research Report on Auto-Tagging of Music
The deliverable D4.7 concerns the work achieved by IRCAM until M36 for the âauto-tagging of musicâ. The deliverable is a research report. The software libraries resulting from the research have been integrated into Fincons/HearDis! Music Library Manager or are used by TU Berlin. The final software libraries are described in D4.5.
The research work on auto-tagging has concentrated on four aspects:
1) Further improving IRCAMâs machine-learning system ircamclass. This has been done by developing the new MASSS audio features, including audio augmentation and audio segmentation into ircamclass. The system has then been applied to train HearDis! âsoftâ features (Vocals-1, Vocals-2, Pop-Appeal, Intensity, Instrumentation, Timbre, Genre, Style). This is described in Part 3.
2) Developing two sets of âhardâ features (i.e. related to musical or musicological concepts) as specified by HearDis! (for integration into Fincons/HearDis! Music Library Manager) and TU Berlin (as input for the prediction model of the GMBI attributes). Such features are either derived from previously estimated higher-level concepts (such as structure, key or succession of chords) or by developing new signal processing algorithm (such as HPSS) or main melody estimation. This is described in Part 4.
3) Developing audio features to characterize the audio quality of a music track. The goal is to describe the quality of the audio independently of its apparent encoding. This is then used to estimate audio degradation or music decade. This is to be used to ensure that playlists contain tracks with similar audio quality. This is described in Part 5.
4) Developing innovative algorithms to extract specific audio features to improve music mixes. So far, innovative techniques (based on various Blind Audio Source Separation algorithms and Convolutional Neural Network) have been developed for singing voice separation, singing voice segmentation, music structure boundaries estimation, and DJ cue-region estimation. This is described in Part 6.EC/H2020/688122/EU/Artist-to-Business-to-Business-to-Consumer Audio Branding System/ABC D
Thematic Interconnectivity as an Innate Musical Quality: An Investigation of Jandek's "European Jewel" Guitar Riffs
This dissertation is divided into two main areas. The first of these explores Jandek-related discourse and contextualizes the project. Also discussed is the interconnectivity that runs through the project through the self-citation of various lyrical, visual, and musical themes. The second main component of this dissertation explores one of these musical themes in detail: the guitar riffs heard in the âEuropean Jewelâ song-set and the transmigration/migration of the riff material used in the song to other non-âEuropean Jewelâ tracks.
Jandek is often described in related discourse as an âoutsider musician.â A significant point of discussion in the first area of this dissertation is the outsider music genre as it relates to Jandek. In part, this dissertation responds to an article by Martin James and Mitzi Waltz which was printed in the periodical Popular Music where it was suggested that the marketing of a musician as an outsider risks diminishing the âinnate qualitiesâ of the so-called outsider musiciansâ works. While the outsider label is in itself problematicâthis is discussed at length in Chapter Twoâthe analysis which comprises the second half of this dissertation delves into self-citation and thematic interconnection as innate qualities within the project.
Explored at length in this dissertation are the guitar riffs of the Jandek song âEuropean Jewel,â the closing track appearing on the artistâs debut album, Ready for the House (1978). The riffs are heard 37 times over the course of five different versions of the song. Elements of the riffs also appear in tracks that are not labeled as âEuropean Jewelâ variants. A larger structural form in which the song-set is situated has been observed. When heard outside of the âEuropean Jewelâ song-set the riffs appear in fragmented form. Continued use of the âEuropean Jewelâ riff material lasts until the album One Foot in the North (1991). Much attention has been given to the interconnection between certain visual and lyrical ideas present in the project by Jandek fans; however, Jandek has not been investigated at any great length in music scholarship, popular or otherwise. In part, this investigation contributes to the breadth of popular music scholarship by exploring this underrepresented act. It also delves into the sonic qualities which are intrinsic to Jandek. This type of sonic analysis is performed in order to separate Jandekâs sonic qualities from non-sonic discussions of the project. Finally, this dissertation poses the question of whether or not these qualities are of value to fans and scholars
Deep Learning Techniques for Music Generation -- A Survey
This paper is a survey and an analysis of different ways of using deep
learning (deep artificial neural networks) to generate musical content. We
propose a methodology based on five dimensions for our analysis:
Objective - What musical content is to be generated? Examples are: melody,
polyphony, accompaniment or counterpoint. - For what destination and for what
use? To be performed by a human(s) (in the case of a musical score), or by a
machine (in the case of an audio file).
Representation - What are the concepts to be manipulated? Examples are:
waveform, spectrogram, note, chord, meter and beat. - What format is to be
used? Examples are: MIDI, piano roll or text. - How will the representation be
encoded? Examples are: scalar, one-hot or many-hot.
Architecture - What type(s) of deep neural network is (are) to be used?
Examples are: feedforward network, recurrent network, autoencoder or generative
adversarial networks.
Challenge - What are the limitations and open challenges? Examples are:
variability, interactivity and creativity.
Strategy - How do we model and control the process of generation? Examples
are: single-step feedforward, iterative feedforward, sampling or input
manipulation.
For each dimension, we conduct a comparative analysis of various models and
techniques and we propose some tentative multidimensional typology. This
typology is bottom-up, based on the analysis of many existing deep-learning
based systems for music generation selected from the relevant literature. These
systems are described and are used to exemplify the various choices of
objective, representation, architecture, challenge and strategy. The last
section includes some discussion and some prospects.Comment: 209 pages. This paper is a simplified version of the book: J.-P.
Briot, G. Hadjeres and F.-D. Pachet, Deep Learning Techniques for Music
Generation, Computational Synthesis and Creative Systems, Springer, 201
DELAY AND MODULATION PROCESSING AS MUSICAL TECHNIQUE IN ROCK
This thesis presents an analytic model for investigating the musical functions of delay and modulation signal processing in a pop/rock context. In so doing, it challenges prevalent academic assumptions about what, specifically, constitutes âmusical practice,â focusing analytic attention on musical procedures and terms reserved for recordists that, until very recently, have only registered in research as extra-musical technologizations of âliveâ exchange, if at all. Recordists do not create space via delay and modulation processing. Rather, they use delay and modulation processing, among other techniques, to provide psychoacoustic information which listeners require to infer space. Put differently, recordists use delay and modulation processing, among other techniques, to add psychoacoustic information to tracks and, in the process, to situate them within the broader space represented by a mix. This musical process is what I ultimately intend to elucidate through the model I present in this thesis
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