Two Auloi from Megara

Replica artefacts are a well-established feature of Roman archaeology, particularly as used in experimental archaeology, by re-enactors, and in museum education. 3D scanning offers a new methodology for the accurate production of such artefacts, which can then be used both in scholarly research and in public engagement activities. This article describes methodologies for 3D scanning and 3D printing, together with appropriate craft techniques, in the creation of replica musical instruments from the collection of UCL's Petrie Museum of Egyptian Archaeology in London. While there were some challenges in replica creation, discussed in further detail, 'functional replicas' were successfully made, that, we argue, replicate sufficiently accurately those features of the objects under investigation from a research perspective. These were the decibel levels (sound power levels), and, for some objects, frequency (pitch) ranges produced, and the variety of sounds that they could produce. This evidence makes an important contribution to our understanding of the contexts of use of the original instruments. Sound recordings virtually modelled in a likely use location, the courtyard of a typical house from Roman-period Egypt, were also produced and assist in our conceptualisation of the wider acoustic environment. Sound recordings and replicas were additionally used for public engagement purposes in a temporary exhibition at the Petrie Museum, and their contribution to museum education is assessed. 3D scanning and printing technology are demonstrated to be valuable techniques for the production of accurate replicas, which can be used successfully to contribute to scholarly research and museum education in new ways. Appendices include .stl files that may be downloaded and 3D printed, to make copies of the replicas for use in new research and education projects.

A novel approach to estimating sinusoidal parameters of musical tones is presented. The proposed algorithm uses a combined quadratic polynomial phase sinusoid and residual signal model, and a waveform fitting criterion, as opposed to the traditional energy fitting one as employed by short-time Fourier transform-based approaches. The proposed algorithm obtains sinusoidal parameters by minimizing the squared difference between the original and the synthesized musical tones. The signal synthesis is implemented implicitly and the synthesis errors are taken into account while the sinusoidal paremeters are estimated. A number of different phase models proposed for music analysis and synthesis are reviewed and their pros and cons are identified. This analysis justifies the use of the proposed signal model. Experiment results for the analysis and synthesis of a variety of musical instrument tones such as piano, trumpet, french horn, flute, and violin are given to demonstrate the accuracy and effectiveness of the proposed algorithm. A VLSI architecture for sinusoid synthesis using the parameters obtained from the proposed algorithm is also given to provide a complete solution to the parametric modeling-based music analysis and synthesis.

Musical instrument classification, as a fundamental task in music information retrieval (MIR), has broad applications in music analysis, education, and content management. However, existing research primarily focuses on short monophonic samples for classification, which fails to capture the timbral variation characteristics in real performance scenarios. Meanwhile, traditional deep learning models still have limitations in extracting complex timbral features. To address these challenges, this paper proposes ICKAN, a deep instrument classification model that incorporates the Kolmogorov-Arnold Network (KAN), and constructs a large-scale dataset containing 30,824 complete musical phrases. Experimental results demonstrate that ICKAN achieves a classification accuracy of 95.74% in a 20-class instrument classification task with 10-second audio segments, significantly outperforming current methods. This research introduces learnable nonlinear activation functions and comprehensive musical segments, offering new insights into improving the accuracy and practicality of instrument classification and contributing valuable references for the advancement of music information retrieval technology. The code and dataset are available at https://github.com/NMLAB8/ICKAN.

In the present paper, a hybrid approach that combines modular modelling and Timed Petri Nets model is proposed, the main interest of this combination is to ensure optimal supervisory control of urban traffic flows in two phases intersections of medium-sized cities. Each intersection is considered as a multi-agent system, composed of all agents who represent all physical spaces. The behaviours of interconnected agents are supervised by the Timed Petri Nets model that controls the traffic light cycles according to an optimal timing plan defined by the Continuous Genetic Algorithm. A real two phases intersection in Sidi Bel-Abbes City is presented as an example. The proposed timing plan is compared with the current one using SUMO software for the most important objective functions. Also, the proposed model's behaviour is compared with TSIS-CORSIM software's model, and all obtained experiments results show the effectiveness of the approach and its possibility to be extended and applied to a many-phases intersection.

Classification of musical segments is an interesting problem. It is a key technology in the development of content-based audio document indexing and retrieval. In this paper, we apply the feature extraction and modeling techniques commonly used in automatic speech recognition to solving the problem of segmentation and instrument identification of musical passages. The correlation among the different components in the feature space and the auto-correlation of each component are analyzed to demonstrate feasibility in musical signal analysis and instrument class modeling. Our experimental results are first evaluated on 3 instrument categories, i.e. vocal music, instrumental music, and their combinations. Furthermore each category is split into two individual cases to give a 6-class problem. Our results show that good performance could be obtained with simple features, such as mel-frequency cepstral coefficients and cepstral coefficients derived from linear prediction signal analysis. Even with a limited amount of training data, we could give an accuracy of 90.60% in the case of three categories. A slightly worse accuracy of 90.38% is obtained when we double the number of categories to six classes.

In the present paper, the effect of using Helical Orifice on transient heat-mass transfer and Combustion Indicated Quantities has been investigated in High Pressure Liquid Fuel Injector for different needle Movements using Avl Fire CFD code. The effect of each of the proposed needle movement profiles (triangular, trapezius, and boot) with the help of a three-dimensional moving mesh by numerical model in AVL Fire software, which has been validated with experimental data in each part, on the performance of the diesel engine and its air emission species has been investigated. Numerical results show that spiral nozzle leads to a rotational flow of fuel spray, increasing the spray cone angle and turbulence intensity inside the combustion chamber. Spiral Nozzle has more mean pressure and temperature than simple geometry of nozzle. Spiral motion leading to increasing turbulent kinetic energy and having more mean pressure, temperature, and wall heat flux. Increasing the resulting spray cone angle improves the performance of the diesel engine by reducing fuel consumption and increasing power and torque, and the production of nitrogen oxide and carbon monoxide pollutants due to a better fuel-air mixture, increased turbulence intensity and average temperature inside the chamber. fuel consumption decreases by 30% and its power and torque increase 55%. Also in this case, carbon monoxide pollutants decrease by 38% and nitrogen oxide pollutants decreasing by 30% for spiral nozzle with trapezius profile for needle movement.

The purpose of the study is to determine the vocal performance characteristics of the ‘Luna, Lussuria e Velocita’ work, written in the style of futurism in the interpretation of I. Nazarov, taking into account the peculiarities of the timbre of his voice. ‘Luna, Lussuria e Velocita’ is written in the futuristic style and is intended for bass/countertenor and electronic musical instruments. The purpose of the research is to determine the features of the vocal performance of the ‘Luna, Lussuria e Velocita’ work composed in the style of futurism, taking into account the features of I. Nazarov's voice timbre. The research methodology is based on the use of music analytical, historical and theoretical analysis methods. The analysis of vocal performance given in the research was carried out based on the note presented by the composer and the video material performed by I. Nazarov. At the same time, a number of scientific literature on futurism was referred to. The scientific novelty of the research is that the work of Italian composer S. Muscaritolo ‘Luna, Lussuria e Velocita’ was involved in the study for the first time. The work is designed for two registers (bass/countertenor) and is composed in the style of futurism, which characterizes its scientific novelty. The work uses non-traditional playing methods for bass and countertenor. In the research, the research of vocal performance methods used by I.Nazarov during the performance was carried out for the first time. Conclusions. I.Nazarov is the first and only singer in the history of Azerbaijani music culture with a countertenor voice and boundless vocal technique. Not only Azerbaijani but also European composers create works of various genres in accordance with the possibilities of his voice. These works are new due to their features and performance requirements. In ‘Luna, Lussuria e Velocita’ I. Nazarov showed himself as a singer with high technical capabilities, able to perfectly control both the baritone and countertenor sound timbre. The singer expanded the possibilities of performing the countertenor sound timbre, which gave rise to the assessment of the sound timbre from a new position.

Acoustic musical instruments enable very rich and subtle control when used by experienced musicians. Musicology has traditionally focused on analysis of scores and more recently audio recordings. However, most music from around the world is not notated, and many nuances of music performance are hard to recover from audio recordings. In this chapter, we describe hyperinstruments, i. e., acoustic instruments that are augmented with digital sensors for capturing performance information and in some cases offering additional playing possibilities. Direct sensors are integrated onto the physical instrument, possibly requiring modifications. Indirect sensors such as cameras and microphones can be used to analyze performer gestures without requiring modifications to the instrument. We describe some representative case studies of hyperinstruments from our own research as well as some representative case studies of the types of musicological analysis one can perform using this approach, such as performer identification, microtiming analysis, and transcription. Until recently, hyperinstruments were mostly used for electroacoustic music creation, but we believe they have a lot of potential in systematic musicological applications involving music performance analysis.

Experimental and numerical analyses of the structural response of adhesively reconstituted beech timber beams

A new model for a thermo-reactive fluid flow across a highly compressible porous medium has been proposed and employed to predict infusion-based manufacturing processes for polymer composites (Celle, 2006). These techniques consist in mixing the reinforcements and the resin during the manufacturing cycle, transversely to the fabrics plane, by applying a pressure on the resin/preform stacking. This yields cost reductions and avoids filling problems. The introduction of a numerical model in a finite element software to study infusion-based processes will increase their diffusion by a reliable prediction of both part thickness and porosity (Celle et al., 2008). The present paper deals mainly with the numerical treatments related to the resin layer.

A new model for a thermo-reactive fluid flow across a highly compressible porous medium has been proposed and employed to predict infusion-based manufacturing processes for polymer composites (Celle, 2006). These techniques consist in mixing the reinforcements and the resin during the manufacturing cycle, transversely to the fabrics plane, by applying a pressure on the resin/preform stacking. This yields cost reductions and avoids filling problems. The introduction of a numerical model in a finite element software to study infusion-based processes will increase their diffusion by a reliable prediction of both part thickness and porosity (Celle et al., 2008). The present paper deals mainly with the numerical treatments related to the resin layer.

The present paper deals with a new approach for evaluating the Thermal Comfort and Index the energy efficiency for perimeter zone in an iso-thermal environment taking into account the thermal sensation distribution. Using a simulation-aided design software, SAPLO and PMVs(Predicted Mean Vote simulated by SAPLO) model, which is derived from Fanger's comfort equations. We evaluated the energy efficiency of the single-glazed and double-glazing windows. The results of analyses indicate that the present approach is effective in assessing the relationship between the thermal comfort and the energy consumption.

Artificial Intelligence (AI) has revolutionized the field of music analysis by enabling advanced sound recognition and classification techniques. In the recent year, the music industry has had transformative evolution in recent years, significantly impacting user engagement, creativity, and technological innovation across various domains, including entertainment, education, and therapy. Musical instrument recognition is an emerging field within this landscape that could be used in applications such as automated music transcription and intelligent recommendation systems and adaptive music generation. Using Deep Learning (DL) alongside Artificial Intelligence has dramatically improved how we hear things as it has become a robust analytics tool for patterns in audio. The intricate signals from audio data together with overlapping frequencies and instrument diversity present significant challenges to accurate musical instrument prediction and classification. Traditional structured machine learning models cannot be applied successfully to dealing with complicated patterns, in contrast DL models possess superior designed system architectures and stronger feature extracting ability. Through the integration of these DNN with default layers with support vector machines, an instrument recognition framework is presented in this research, on publicly available dataset of diverse instruments with three second clips combined with Mel Spectrogram and its audio features. The standard measures are used for measuring performance of models such as accuracy, precision, recall and f1-score. The proposed model DNN achieves 98% classification precision over the SVM baseline with accuracy of 96% using musical instrument dataset with 24 classes. Using this research as the base of concept, it is shown that DL is better than current proposed methodologies at improving audio transformation processes, and it promises potential in improving the state of the art at musical instrument identification techniques that would yield useful results to intelligent music systems and AI audio analysis methodologies.

The present paper is a modest attempt to fill in one of the blank spots on our ethno- musicological map of East Africa. It cannot be more than a preliminary superficial account of music and musical instruments in the wide territory of Northern Mozambique. Further research trips will have to be done in the coming years in order to check all the information that was given to me, and to collect more material, so that we may gain a more extensive knowledge of the area. Many problems and questions have been raised in this paper. They can only be solved through intensive analytical and comparative studies of the present and additional material. The author would be glad, in this context, if he could be informed of any other recordings of music from Northern Mozambique that have been made or are being made. There remains still much of the collected material to study. In particular, an analysis of the 8 mm film strips, together with a detailed analysis of all the tape recordings, will give us plenty of further insight.

This study presents the implementation of Transfer learning using the ResNet-18 architecture for classifying 10 musical instrument categories based on visual representations of audio signals. The audio waveform is transformed into image-like inputs appropriate for CNN processing, accompanied by data augmentation and ImageNet-standard normalization. ResNet-18 is utilized due to its efficient feature extraction capability enabled by residual blocks, which help overcome vanishing gradient issues. The model was trained for 10 Epochs using the AdamW optimizer and Cross-Entropy Loss. Experimental results show that the model achieved a maximum validation accuracy of 77.35%, with a stable downward trend in training loss, indicating effective feature learning. However, several misclassification cases were observed, particularly among instruments with similar spectral characteristics, such as drum–violin and tabla–sitar. These findings demonstrate that while ResNet-18 performs reliably for musical instrument classification, further improvements remain possible through deeper architectures like ResNet-50, more comprehensive hyperparameter optimization, and the use of richer audio representations such as Mel-Spectrograms. This research provides an essential foundation for developing automated music analysis systems powered by Deep Learning.