Utilization of Recycled Filament for 3D Printing for Consumer Goods

The 3D printing market has been used in a wide variety of manufacturing industries including textile and apparel. Many consumers can now own a personal 3D printer at home for recreational printing. There are even websites dedicated to 3D printing patterns made by consumers. However, the materials used in the 3D printing process pose a problem for the environment due to their plastic-based nature. 3D printing is a layered process with each layer being printed depending on the layer below it for strength and stability. During the 3D printing process, great amounts of waste are produced as a result of printing errors that, having occurred, cannot be reused. This waste is plastic based and therefore does not readily biodegrade. Using 3D printing filament created from recycled materials (i.e. plastic bottles) could transform the waste into new re-useable materials which ultimately could reduce the harmful effect of plastic products on the environment over time. One such plastic product is plastic instrument mouthpieces. The current plastic mouthpieces on the market are not created using recycled plastics, so when they break they only contribute to the plastic waste in landfills. Therefore, the study focused on creating functional 3D printed mouthpieces from rPETG filament (Recycled Polyethylene Terephthalate Glycol-modified filament) for the University of Arkansas Hogwild Band brass players to be used during performances. A total of 29 mouthpieces were created for trumpet, trombone, and tuba players in the band and were utilized for the 2020 Hogwild season. Participants were then asked to share their feedback about the performance of the mouthpieces for the final part of the study

Pitch-Informed Solo and Accompaniment Separation

Das 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 Musikstück zu isolieren, unabhängig 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 zusätzliche Information aus der Verteilung der Instrumente im Stereo-Panorama gewonnen werden. Die entwickelte Methode nutzt Tonhöhen-Information als Basis für 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 für die Separation genutzt. Tonobjekt-basierte Verarbeitung ermöglicht es, zusätzlich die Notenanfänge zu verfeinern, transiente Artefakte zu reduzieren, gemeinsame Amplitudenmodulation (Common Amplitude Modulation CAM) einzubeziehen und besser nichtharmonische Elemente der Töne abzuschätzen. Der vorgestellte Algorithmus zur Quellentrennung von Soloinstrument und Begleitung ermöglicht eine Echtzeitverarbeitung und ist somit relevant für den praktischen Einsatz. Ein Experiment zur besseren Modellierung der Zusammenhänge zwischen Magnitude, Phase und Feinfrequenz von isolierten Instrumententönen wurde durchgeführt. Als Ergebnis konnte die Kontinuität der zeitlichen Einhüllenden, die Inharmonizität bestimmter Musikinstrumente und die Auswertung des Phasenfortschritts für die vorgestellte Methode ausgenutzt werden. Zusätzlich wurde ein Algorithmus für die Quellentrennung in perkussive und harmonische Signalanteile auf Basis des Phasenfortschritts entwickelt. Dieser erreicht ein verbesserte perzeptuelle Qualität der harmonischen und perkussiven Signale gegenüber 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 Bewertungsmaße erzielt werden. Die Qualität eines Referenzalgorithmus im Hinblick auf den in dieser Dissertation beschriebenen Instrumentaldatensatz übertroffen werden. Als ein Anwendungsszenario für die Klangquellentrennung in Solo und Begleitung wurde ein Hörtest durchgeführt, der die Qualitätsanforderungen an Quellentrennung im Kontext von Musiklernsoftware bewerten sollte. Die Ergebnisse dieses Hörtests zeigen, dass die Solo- und Begleitspur gemäß unterschiedlicher Qualitätskriterien getrennt werden sollten. Die Musiklernsoftware Songs2See integriert die vorgestellte Klangquellentrennung bereits in einer kommerziell erhältlichen 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

A realtime feedback learning tool to visualize sound quality in violin performances

The assessment of the sound properties of a performed mu- sical note has been widely studied in the past. Although a consensus exist on what is a good or a bad musical performance, there is not a formal definition of performance tone quality due to its subjectivity. In this study we present a computational approach for the automatic assess- ment of violin sound production. We investigate the correlations among extracted features from audio performances and the perceptual quality of violin sounds rated by listeners using machine learning techniques. The obtained models are used for implementing a real-time feedback learning system

Who’s playing? Towards machine-assisted identification of jazz trumpeters by timbre

The goal of our proposed study is to contribute to the growing research in machine-assisted identification of jazz performers. In particular, we seek to identify unknown jazz trumpeters. We plan to take an approach that has not received recent attention; namely, using human observation to compare spectrograms and other data representing musical timbre. We believe that human observation, when combined with machine learning, will improve accuracy of timbre recognition and performer identification. We will collect 100 music samples: five each from 20 trumpeters. We will manually sort spectrograms and other data in order to distinguish the most salient timbre characteristics. Once we choose those features, we will use a computer to filter for them. If our approach is successful, we will develop a larger database of trumpet solos

Music Information Retrieval Meets Music Education

This paper addresses the use of Music Information Retrieval (MIR) techniques in music education and their integration in learning software. A general overview of systems that are either commercially available or in research stage is presented. Furthermore, three well-known MIR methods used in music learning systems and their state-of-the-art are described: music transcription, solo and accompaniment track creation, and generation of performance instructions. As a representative example of a music learning system developed within the MIR community, the Songs2See software is outlined. Finally, challenges and directions for future research are described

A microtonal wind controller building on Yamaha’s technology to facilitate the performance of music based on the “19-EDO” scale

We describe a project in which several collaborators adapted an existing instrument to make it capable of playing expressively in music based on the microtonal scale characterised by equal divsion of the octave into 19 tones (“19-EDO”). Our objective was not just to build this instrument, however, but also to produce a well-formed piece of music which would exploit it idiomatically, in a performance which would provide listeners with a pleasurable and satisfying musical experience. Hence, consideration of the extent and limits of the playing-techniques of the resulting instrument (a “Wind-Controller”) and of appropriate approaches to the composition of music for it were an integral part of the project from the start. Moreover, the intention was also that the piece, though grounded in the musical characteristics of the 19-EDO scale, would nevertheless have a recognisable relationship with what Dimitri Tymoczko (2010) has called the “Extended Common Practice” of the last millennium. So the article goes on to consider these matters, and to present a score of the resulting new piece, annotated with comments documenting some of the performance issues which it raises. Thus, bringing the project to fruition involved elements of composition, performance, engineering and computing, and the article describes how such an inter-disciplinary, multi-disciplinary and cross-disciplinary collaboration was co-ordinated in a unified manner to achieve the envisaged outcome. Finally, we consider why the building of microtonal instruments is such a problematic issue in a contemporary (“high-tech”) society like ours

Evaluation of Music Performance: Computerized Assessment Versus Human Judges.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2018

Extended playing techniques: The next milestone in musical instrument recognition

The expressive variability in producing a musical note conveys information essential to the modeling of orchestration and style. As such, it plays a crucial role in computer-assisted browsing of massive digital music corpora. Yet, although the automatic recognition of a musical instrument from the recording of a single "ordinary" note is considered a solved problem, automatic identification of instrumental playing technique (IPT) remains largely underdeveloped. We benchmark machine listening systems for query-by-example browsing among 143 extended IPTs for 16 instruments, amounting to 469 triplets of instrument, mute, and technique. We identify and discuss three necessary conditions for significantly outperforming the traditional mel-frequency cepstral coefficient (MFCC) baseline: the addition of second-order scattering coefficients to account for amplitude modulation, the incorporation of long-range temporal dependencies, and metric learning using large-margin nearest neighbors (LMNN) to reduce intra-class variability. Evaluating on the Studio On Line (SOL) dataset, we obtain a precision at rank 5 of 99.7% for instrument recognition (baseline at 89.0%) and of 61.0% for IPT recognition (baseline at 44.5%). We interpret this gain through a qualitative assessment of practical usability and visualization using nonlinear dimensionality reduction.Comment: 10 pages, 9 figures. The source code to reproduce the experiments of this paper is made available at: https://www.github.com/mathieulagrange/dlfm201