The simplest and most commonly used acoustic levitator is comprised of a transmitter and an opposing reflecting surface. This type of device, however, is only able to levitate objects along one direction, at distances multiple of half of a wavelength. In this work, we show how a customised reflective acoustic metamaterial enables the levitation of multiple particles, not necessarily on a line and with arbitrary mutual distances, starting with a generic input wave. We establish a heuristic optimisation technique for the design of the metamaterial, where the local height of the surface is used to introduce delay patterns to the reflected signals. Our method stands for any type and number of sources, spatial resolution of the metamaterial and system’s variables (i.e. source position, phase and amplitude, metamaterial’s geometry, relative position of the levitation points, etc.). Finally, we explore how the strength of multiple levitation points changes with their relative distance, demonstrating sub-wavelength field control over levitating polystyrene beads into various configurations.

The key parameters musicians consider when they evaluate the quality of a musical instrument are its tuning (reproducing a musical scale), sound quality, and ergonomics. Musical instrument makers, even up to this day, primarily base their practice on empirical knowledge and costly physical experimentation. A computational model that combines all the aforementioned key parameters is presented to predict the building details of an instrument. The simulation of the instrument is introduced to a multi-objective optimizer to calculate its optimal set of geometrical and material features, considering the importance of the three key parameters. For a string musical instrument this approach is based on a hybrid model of Digital Signal Processing simulating the vibrating string and of Finite Elements Method simulating the effect of the body. The simulation technique has been validated by building an ancient guitar and comparing its recordings with its analogous digital model. The proposed method can be put into practice to investigate the building details of any instrument by introducing the relevant simulation and the objective’s function parameters. This work is expected to provide a powerful tool for the musical instrument makers towards a more efficient design of a bespoke instrument.

We present a simulation method for the auralization of the ancient Greek double-reed wind instrument Aulos. The implementation is based on Digital Signal Processing and physical modeling techniques for the instrument's two parts: the excitation mechanism and the acoustic resonator with toneholes. Single-reeded instruments are in-depth studied firstly because their excitation mechanism is the one used in a great amount of modern wind-reed instruments and secondly because the physics governing the phenomena is less complicated than the double-reeded instruments. We here provide a detailed model of a system comprised of a double-reed linked to an acoustic resonator with toneholes to sonify Aulos. We validate our results by comparing our method's synthesized signal with recordings from a replica of Aulos of Poseidonia built in our lab. The comparison showed that the fundamental frequencies and the first three odd harmonics of the signals differ 6, 5, 3, and 2 cents on average, respectively, which is below the Just Noticeable Difference threshold.

Archeomusicologists commonly use methods based on the physical properties and the relative tuning system of a musical instrument in order to estimate its tones. However, because the musician often alters the tones’ frequency, for example, while playing in wind instruments by means of embouchure or by stressing the string in string instruments, the current methods that neglect the musician’s interaction with the instrument cannot provide solid results. In this work, we introduce ENTROTUNER, a computational method, based on mathematical optimization, to more accurately estimate the generated tones by considering: the instrument as a sound production mechanism, the relevant musical scale(s), and the musician’s interaction with the instrument. We simulate this interaction as a system that, by following tuning rules, aims to maximize the partials’ overlap (harmonicity), coded as entropy’s minimization of the aggregated tones’ spectrum. Last, we put ENTROTUNER into practice for the ancient Greek wind instrument Aulos. The results reveal that, compared with the traditional methods, ENTROTUNER highlights increased harmonicity (entropy decreased by 0.341bits), eleven additional consonant intervals, as well as 47.8% more tuning quality for the musical instrument.

A simulation of a musical instrument is considered to be a successful one when there is a good resemblance between the model’s synthesized sound and the real instrument’s sound. In this work, we propose the integration of physical modeling (PM) methods with an optimization process to regulate a generated digital signal. Its goal is to find a new set of values of the PM’s parameters’ that would lead to a synthesized signal matching as much as possible to reference signals corresponding to the physical musical instrument. The reference signals can be: (a) described by their acoustic characteristics (e.g., fundamental frequencies, inharmonicity, etc.) and/or (b) the signals themselves (e.g., impedances, recordings, etc.). We put this method into practice for a commercial recorder, simulated using the digital waveguides’ PM technique. The reference signals, in our case, are the recorded signals of the physical instrument. The degree of similarity between the synthesized (PM) and the recorded signal (musical instrument) is calculated by the signals’ linear cross-correlation. Our results show that the adoption of the optimization process resulted in more realistic synthesized signals by (a) enhancing the degree of similarity between the synthesized and the recorded signal (the average absolute Pearson Correlation Coefficient increased from 0.13 to 0.67), (b) resolving mistuning issues (the average absolute deviation of the synthesized from the recorded signals’ pitches reduced from 40 cents to the non-noticeable level of 2 cents) and (c) similar sound color characteristics and matched overtones (the average absolute deviation of the synthesized from the recorded signals’ first five partials reduced from 41 cents to 2 cents).