PLXTRM : prediction-led eXtended-guitar tool for real-time music applications and live performance

peer reviewedThis article presents PLXTRM, a system tracking picking-hand micro-gestures for real-time music applications and live performance. PLXTRM taps into the existing gesture vocabulary of the guitar player. On the first level, PLXTRM provides a continuous controller that doesn’t require the musician to learn and integrate extrinsic gestures, avoiding additional cognitive load. Beyond the possible musical applications using this continuous control, the second aim is to harness PLXTRM’s predictive power. Using a reservoir network, string onsets are predicted within a certain time frame, based on the spatial trajectory of the guitar pick. In this time frame, manipulations to the audio signal can be introduced, prior to the string actually sounding, ’prefacing’ note onsets. Thirdly, PLXTRM facilitates the distinction of playing features such as up-strokes vs. down-strokes, string selections and the continuous velocity of gestures, and thereby explores new expressive possibilities

Automatic Transcription of Bass Guitar Tracks applied for Music Genre Classification and Sound Synthesis

Musiksignale bestehen in der Regel aus einer Überlagerung mehrerer Einzelinstrumente. Die meisten existierenden Algorithmen zur automatischen Transkription und Analyse von Musikaufnahmen im Forschungsfeld des Music Information Retrieval (MIR) versuchen, semantische Information direkt aus diesen gemischten Signalen zu extrahieren. In den letzten Jahren wurde häufig beobachtet, dass die Leistungsfähigkeit dieser Algorithmen durch die Signalüberlagerungen und den daraus resultierenden Informationsverlust generell limitiert ist. Ein möglicher Lösungsansatz besteht darin, mittels Verfahren der Quellentrennung die beteiligten Instrumente vor der Analyse klanglich zu isolieren. Die Leistungsfähigkeit dieser Algorithmen ist zum aktuellen Stand der Technik jedoch nicht immer ausreichend, um eine sehr gute Trennung der Einzelquellen zu ermöglichen. In dieser Arbeit werden daher ausschließlich isolierte Instrumentalaufnahmen untersucht, die klanglich nicht von anderen Instrumenten überlagert sind. Exemplarisch werden anhand der elektrischen Bassgitarre auf die Klangerzeugung dieses Instrumentes hin spezialisierte Analyse- und Klangsynthesealgorithmen entwickelt und evaluiert.Im ersten Teil der vorliegenden Arbeit wird ein Algorithmus vorgestellt, der eine automatische Transkription von Bassgitarrenaufnahmen durchführt. Dabei wird das Audiosignal durch verschiedene Klangereignisse beschrieben, welche den gespielten Noten auf dem Instrument entsprechen. Neben den üblichen Notenparametern Anfang, Dauer, Lautstärke und Tonhöhe werden dabei auch instrumentenspezifische Parameter wie die verwendeten Spieltechniken sowie die Saiten- und Bundlage auf dem Instrument automatisch extrahiert. Evaluationsexperimente anhand zweier neu erstellter Audiodatensätze belegen, dass der vorgestellte Transkriptionsalgorithmus auf einem Datensatz von realistischen Bassgitarrenaufnahmen eine höhere Erkennungsgenauigkeit erreichen kann als drei existierende Algorithmen aus dem Stand der Technik. Die Schätzung der instrumentenspezifischen Parameter kann insbesondere für isolierte Einzelnoten mit einer hohen Güte durchgeführt werden.Im zweiten Teil der Arbeit wird untersucht, wie aus einer Notendarstellung typischer sich wieder- holender Basslinien auf das Musikgenre geschlossen werden kann. Dabei werden Audiomerkmale extrahiert, welche verschiedene tonale, rhythmische, und strukturelle Eigenschaften von Basslinien quantitativ beschreiben. Mit Hilfe eines neu erstellten Datensatzes von 520 typischen Basslinien aus 13 verschiedenen Musikgenres wurden drei verschiedene Ansätze für die automatische Genreklassifikation verglichen. Dabei zeigte sich, dass mit Hilfe eines regelbasierten Klassifikationsverfahrens nur Anhand der Analyse der Basslinie eines Musikstückes bereits eine mittlere Erkennungsrate von 64,8 % erreicht werden konnte.Die Re-synthese der originalen Bassspuren basierend auf den extrahierten Notenparametern wird im dritten Teil der Arbeit untersucht. Dabei wird ein neuer Audiosynthesealgorithmus vorgestellt, der basierend auf dem Prinzip des Physical Modeling verschiedene Aspekte der für die Bassgitarre charakteristische Klangerzeugung wie Saitenanregung, Dämpfung, Kollision zwischen Saite und Bund sowie dem Tonabnehmerverhalten nachbildet. Weiterhin wird ein parametrischerAudiokodierungsansatz diskutiert, der es erlaubt, Bassgitarrenspuren nur anhand der ermittel- ten notenweisen Parameter zu übertragen um sie auf Dekoderseite wieder zu resynthetisieren. Die Ergebnisse mehrerer Hötest belegen, dass der vorgeschlagene Synthesealgorithmus eine Re- Synthese von Bassgitarrenaufnahmen mit einer besseren Klangqualität ermöglicht als die Übertragung der Audiodaten mit existierenden Audiokodierungsverfahren, die auf sehr geringe Bitraten ein gestellt sind.Music recordings most often consist of multiple instrument signals, which overlap in time and frequency. In the field of Music Information Retrieval (MIR), existing algorithms for the automatic transcription and analysis of music recordings aim to extract semantic information from mixed audio signals. In the last years, it was frequently observed that the algorithm performance is limited due to the signal interference and the resulting loss of information. One common approach to solve this problem is to first apply source separation algorithms to isolate the present musical instrument signals before analyzing them individually. The performance of source separation algorithms strongly depends on the number of instruments as well as on the amount of spectral overlap.In this thesis, isolated instrumental tracks are analyzed in order to circumvent the challenges of source separation. Instead, the focus is on the development of instrument-centered signal processing algorithms for music transcription, musical analysis, as well as sound synthesis. The electric bass guitar is chosen as an example instrument. Its sound production principles are closely investigated and considered in the algorithmic design.In the first part of this thesis, an automatic music transcription algorithm for electric bass guitar recordings will be presented. The audio signal is interpreted as a sequence of sound events, which are described by various parameters. In addition to the conventionally used score-level parameters note onset, duration, loudness, and pitch, instrument-specific parameters such as the applied instrument playing techniques and the geometric position on the instrument fretboard will be extracted. Different evaluation experiments confirmed that the proposed transcription algorithm outperformed three state-of-the-art bass transcription algorithms for the transcription of realistic bass guitar recordings. The estimation of the instrument-level parameters works with high accuracy, in particular for isolated note samples.In the second part of the thesis, it will be investigated, whether the sole analysis of the bassline of a music piece allows to automatically classify its music genre. Different score-based audio features will be proposed that allow to quantify tonal, rhythmic, and structural properties of basslines. Based on a novel data set of 520 bassline transcriptions from 13 different music genres, three approaches for music genre classification were compared. A rule-based classification system could achieve a mean class accuracy of 64.8 % by only taking features into account that were extracted from the bassline of a music piece.The re-synthesis of a bass guitar recordings using the previously extracted note parameters will be studied in the third part of this thesis. Based on the physical modeling of string instruments, a novel sound synthesis algorithm tailored to the electric bass guitar will be presented. The algorithm mimics different aspects of the instrument’s sound production mechanism such as string excitement, string damping, string-fret collision, and the influence of the electro-magnetic pickup. Furthermore, a parametric audio coding approach will be discussed that allows to encode and transmit bass guitar tracks with a significantly smaller bit rate than conventional audio coding algorithms do. The results of different listening tests confirmed that a higher perceptual quality can be achieved if the original bass guitar recordings are encoded and re-synthesized using the proposed parametric audio codec instead of being encoded using conventional audio codecs at very low bit rate settings

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

A perceptually evaluated signal model:Collisions between a vibrating object and an obstacle

The collision interaction mechanism between a vibrating string and a non-resonant obstacle is at the heart of many musical instruments. This paper focuses on the identification of perceptually salient auditory features related to this phenomenon. The objective is to design a signal-based synthesis process, with an eye towards developing intuitive control strategies. To this end, a database of synthesized sounds is assembled through physics-based emulation of a string/obstacle collision, in order to characterize the effect of collisions on time-frequency content. The investigation of this database reveals characteristic time-frequency patterns related to the position of the obstacle during the interaction. In particular, a frequency shift of certain modes is apparent for strong interactions, which, alongside the generation of new frequency components, leads to increased perceived roughness and inharmonicity. These observations enable the design of a real-time compatible signal-based sound synthesis process, with a mapping of synthesis parameters linked to the perceived location of the obstacle. The accuracy of the signal model with respect to the physical model sound output and recorded sounds was evaluated through listening tests: time-frequency patterns reproduced by the signal model enabled listeners to precisely recognize the transverse location of the obstacle

Physically Informed Subtraction of a String's Resonances from Monophonic, Discretely Attacked Tones : a Phase Vocoder Approach

A method for the subtraction of a string's oscillations from monophonic, plucked- or hit-string tones is presented. The remainder of the subtraction is the response of the instrument's body to the excitation, and potentially other sources, such as faint vibrations of other strings, background noises or recording artifacts. In some respects, this method is similar to a stochastic-deterministic decomposition based on Sinusoidal Modeling Synthesis [MQ86, IS87]. However, our method targets string partials expressly, according to a physical model of the string's vibrations described in this thesis. Also, the method sits on a Phase Vocoder scheme. This approach has the essential advantage that the subtraction of the partials can take place \instantly", on a frame-by-frame basis, avoiding the necessity of tracking the partials and therefore availing of the possibility of a real-time implementation. The subtraction takes place in the frequency domain, and a method is presented whereby the computational cost of this process can be reduced through the reduction of a partial's frequency-domain data to its main lobe. In each frame of the Phase Vocoder, the string is encoded as a set of partials, completely described by four constants of frequency, phase, magnitude and exponential decay. These parameters are obtained with a novel method, the Complex Exponential Phase Magnitude Evolution (CSPME), which is a generalisation of the CSPE [SG06] to signals with exponential envelopes and which surpasses the nite resolution of the Discrete Fourier Transform. The encoding obtained is an intuitive representation of the string, suitable to musical processing

Music in Health and Diseases

It is well recognized that music is a unique and cost-effective solution for the rehabilitation of patients with cognitive deficits. However, music can also be used as a non-invasive and non-pharmacological intervention modality not only for the management of various disease conditions but also for maintaining good health overall. Music-based therapeutic strategies can be used as complementary methods to existing diagnostic approaches to manage cognitive deficits as well as clinical and physiological abnormalities of individuals in need. This book focuses on various aspects of music and its role in enhancing health and recovering from a disease. Chapters explore music as a healing method across civilizations and measure the effect of music on human physiology and functions

Biomechanical Modelling of Musical Performance: A Case Study of the Guitar

Merged with duplicate record 10026.1/2517 on 07.20.2017 by CS (TIS)Computer-generated musical performances are often criticised for being unable to match the expressivity found in performances by humans. Much research has been conducted in the past two decades in order to create computer technology able to perform a given piece music as expressively as humans, largely without success. Two approaches have been often adopted to research into modelling expressive music performance on computers. The first focuses on sound; that is, on modelling patterns of deviations between a recorded human performance and the music score. The second focuses on modelling the cognitive processes involved in a musical performance. Both approaches are valid and can complement each other. In this thesis we propose a third complementary approach, focusing on the guitar, which concerns the physical manipulation of the instrument by the performer: a biomechanical approach. The essence of this thesis is a study on capturing, analyzing and modelling information about motor and biomechanical processes of guitar performance. The focus is on speed, precision, and force of a guitarist's left-hand. The overarching questions behind our study are: 1) Do unintentional actions originating from motor and biomechanical functions during musical performance contribute a material "human feel" to the performance? 2) Would it be possible determine and quantify such unintentional actions? 3) Would it be possible to model and embed such information in a computer system? The contributionst o knowledgep ursued in this thesis include: a) An unprecedented study of guitar mechanics, ergonomics, and playability; b) A detailed study of how the human body performs actions when playing the guitar; c) A methodologyt o formally record quantifiable data about such actionsin performance; d) An approach to model such information, and e) A demonstration of how the above knowledge can be embeddedin a system for music performance