The frequencies of notes made by a musical instrument are determined by the physical properties of the instrument. Consequently, by measuring the frequency of a note, one can infer information about the instrument's physical properties. In this work, we show that by modifying a musical instrument to contain a sample and analyzing the instrument's pitch, we can make precision measurements of the physical properties of the sample. We used the mbira, a 3000-year-old African musical instrument that consists of metal tines attached to a wooden board; these tines are plucked to play musical notes. By replacing the mbira's tines with bent steel tubing, filling the tubing with a sample, using a smartphone to record the sound while plucking the tubing, and measuring the frequency of the sound using a free software tool on our website, we can measure the density of the sample with a resolution of about 0.012 g/mL. Unlike existing tools for measuring density, the mbira sensor can be made and used by virtually anyone in the world. To demonstrate the mbira sensor's capabilities, we used it to successfully distinguish diethylene glycol and glycerol, two similar chemicals that are sometimes mistaken for each other in pharmaceutical manufacturing (leading to hundreds of deaths). We also show that consumers could use mbira sensors to detect counterfeit and adulterated medications (which represent around 10% of all medications in low- and middle-income countries). We expect that many other musical instruments can function as sensors and find important and lifesaving applications.
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http://dx.doi.org/10.1021/acsomega.8b01673 | DOI Listing |
J Neurosci
January 2025
Sony Computer Science Laboratories Inc., Tokyo, Japan.
Dexterous motor skills, like those needed for playing musical instruments and sports, require the somatosensory system to accurately and rapidly process somatosensory information from multiple body parts. This is challenging due to the convergence of afferent inputs from different body parts into a single neuron and the overlapping representation of neighboring body parts in the somatosensory cortices. How do trained individuals, such as pianists and athletes, manage this? Here, a series of five experiments with pianists and nonmusicians (female and male) shows that pianists have enhanced inhibitory function in the somatosensory system, which isolates the processing of somatosensory afferent inputs from each finger.
View Article and Find Full Text PDFFront Artif Intell
December 2024
Computer Science and Software Engineering Department, Auckland University of Technology, Auckland, New Zealand.
Introduction: Musical instrument recognition is a critical component of music information retrieval (MIR), aimed at identifying and classifying instruments from audio recordings. This task poses significant challenges due to the complexity and variability of musical signals.
Methods: In this study, we employed convolutional neural networks (CNNs) to analyze the contributions of various spectrogram representations-STFT, Log-Mel, MFCC, Chroma, Spectral Contrast, and Tonnetz-to the classification of ten different musical instruments.
PLoS One
December 2024
Faculty of Education, Shaanxi Normal University, Xi'an, China.
Assisting pre-service teachers in developing readiness for interdisciplinary teaching has been recognized as a crucial direction in teacher education in China. However, there is currently a lack of reliable instrument to measure the readiness. We developed and validated an Interdisciplinary Teaching Readiness Scale (ITRS) for pre-service teachers to fill the gap.
View Article and Find Full Text PDFData Brief
December 2024
Department of Computer Science, University of Sheffield, UK.
This paper presents the Cadenza Woodwind Dataset. This publicly available data is synthesised audio for woodwind quartets including renderings of each instrument in isolation. The data was created to be used as training data within Cadenza's second open machine learning challenge (CAD2) for the task on rebalancing classical music ensembles.
View Article and Find Full Text PDFFront Robot AI
December 2024
CREATE-Lab, Department of Mechanical Engineering, Swiss Federal Technology Institute of Lausanne (EPFL), Lausanne, Switzerland.
Creativity and style in music playing originates from constraints and imperfect interactions between instruments and players. Digital and robotic systems have so far been unable to capture this naturalistic playing. Whether as an additional tool for musicians, function restoration with prosthetics, or artificial intelligence-powered systems, the physical embodiment and interactions generated are critical for expression and connection with an audience.
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