Space produce string instruments
Since a space station typically contains regular air at normal pressure in order to keep the humans comfortable, the sounds created by playing a guitar will be the same as on earth. The weightless environment inside a space station has no effect on the guitar's ability to create sound. Sound is created by the strings and body of a guitar when they vibrate quickly after being plucked. These vibrations knock against the air, causing the air to vibrate quickly, which we humans experience as sound. The guitar strings vibrate back and forth so quickly when plucked because of a tug-of-war between two effects: the tension in the string and the inertia of the string. The tension in the string is a force which tends to pull the string from a stretched, bent shape back to a compact, straight shape.VIDEO ON THE TOPIC: Hyperion Strings Elements - Space & Effects Panel
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- Stringed instrument
- How To Mix Music (Part 5): Mixing Instruments & Synths
- Physics of Music: Why String Instruments Sound So Sweet
- PARTS of the CELLO
- Can 3D-printing musical instruments produce better sound than traditional instruments?
- Speakerless Immersive Sound: Continental and Sennheiser Revolutionize Vehicle Audio
- What actually happens when you play a musical instrument in space?
- Would a guitar sound the same on a space station?
- Spider silk spun into violin strings
Timbre is the attribute of sound that allows humans and other animals to distinguish among different sound sources. Studies based on psychophysical judgments of musical timbre, ecological analyses of sound's physical characteristics as well as machine learning approaches have all suggested that timbre is a multifaceted attribute that invokes both spectral and temporal sound features.
Here, we explored the neural underpinnings of musical timbre. We used a neuro-computational framework based on spectro-temporal receptive fields, recorded from over a thousand neurons in the mammalian primary auditory cortex as well as from simulated cortical neurons, augmented with a nonlinear classifier. The model was able to perform robust instrument classification irrespective of pitch and playing style, with an accuracy of Using the same front end, the model was also able to reproduce perceptual distance judgments between timbres as perceived by human listeners.
The study demonstrates that joint spectro-temporal features, such as those observed in the mammalian primary auditory cortex, are critical to provide the rich-enough representation necessary to account for perceptual judgments of timbre by human listeners, as well as recognition of musical instruments. Music is a complex acoustic experience that we often take for granted. Whether sitting at a symphony hall or enjoying a melody over earphones, we have no difficulty identifying the instruments playing, following various beats, or simply distinguishing a flute from an oboe.
Our brains rely on a number of sound attributes to analyze the music in our ears. These attributes can be straightforward like loudness or quite complex like the identity of the instrument. Of all perceptual attributes of music, timbre remains the most mysterious and least amenable to a simple mathematical abstraction. In this work, we examine the neural underpinnings of musical timbre in an attempt to both define its perceptual space and explore the processes underlying timbre-based recognition.
We propose a scheme based on responses observed at the level of mammalian primary auditory cortex and show that it can accurately predict sound source recognition and perceptual timbre judgments by human listeners. The analyses presented here strongly suggest that rich representations such as those observed in auditory cortex are critical in mediating timbre percepts.
PLoS Comput Biol 8 11 : e Editor: Frederic E. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.
A fundamental role of auditory perception is to infer the likely source of a sound; for instance to identify an animal in a dark forest, or to recognize a familiar voice on the phone.
Timbre, often referred to as the color of sound, is believed to play a key role in this recognition process . Though timbre is an intuitive concept, its formal definition is less so. The ANSI definition of timbre describes it as that attribute that allows us to distinguish between sounds having the same perceptual duration, loudness, and pitch, such as two different musical instruments playing exactly the same note .
As has been often been pointed out, this definition by the negative does not state what are the perceptual dimensions underlying timbre perception. Spectrum is obviously a strong candidate: physical objects produce sounds with a spectral profile that reflects their particular sets of vibration modes and resonances .
Measures of spectral shape have thus been proposed as basic dimensions of timbre e. But timbre is not only spectrum, as changes of amplitude over time, the so-called temporal envelope, also have strong perceptual effects  , . To identify the most salient timbre dimensions, statistical techniques such as multidimensional scaling have been used: perceptual differences between sound samples were collected and the underlying dimensionality of the timbre space inferred  , .
These studies suggest a combination of spectral and temporal dimensions to explain the perceptual distance judgments, but the precise nature of these dimensions varies across studies and sound sets  , . Importantly, almost all timbre dimensions that have been proposed to date on the basis of psychophysical studies  are either purely spectral or purely temporal.
The only spectro-temporal aspect of sound that has been considered in this context is related to the asynchrony of partials around the onset of a sound 8,9 , but the salience of this spectro-temporal dimension was found to be weak and context-dependent .
Technological approaches, not concerned with biology nor human perception, have explored much richer feature representations that span both spectral, temporal, and spectro-temporal dimensions. The motivation for these engineering techniques is an accurate recognition of specific sounds or acoustic events in a variety of applications e. Myriad spectral features have been proposed for audio content analysis, ranging from simple summary statistics of spectral shape e.
Such metrics have often been augmented with temporal information, which was found to improve the robustness of content identification  , . Common modeling of temporal dynamics also ranged from simple summary statistics such as onsets, attack time, velocity, acceleration and higher-order moments to more sophisticated statistical temporal modeling using Hidden Markov Models, Artificial Neural Networks, Adaptive Resonance Theory models, Liquid State Machine systems and Self-Organizing Maps  , .
Overall, the choice of features was very dependent on the task at hand, the complexity of the dataset, and the desired performance level and robustness of the system. Complementing perceptual and technological approaches, brain-imaging techniques have been used to explore the neural underpinnings of timbre perception. Correlates of musical timbre dimensions suggested by multidimensional scaling studies have been observed using event-related potentials . Other studies have attempted to identify the neural substrates of natural sound recognition, by looking for brain areas that would be selective to specific sound categories, such as voice-specific regions in secondary cortical areas  ,  and other sound categories such as tools  or musical instruments .
A hierarchical model consistent with these findings has been proposed in which selectivity to different sound categories is refined as one climbs the processing chain .
An alternative, more distributed scheme has also been suggested  ,  , which includes the contribution of low-level cues to the large perceptual differences between these high-level sound categories.
A common issue for the psychophysical, technological, and neurophysiological investigations of timbre is that the generality of the results is mitigated by the particular characteristics of the sound set used.
For multi-dimensional scaling behavioral studies, by construction, the dimensions found will be the most salient within the sound set; but they may not capture other dimensions which could nevertheless be crucial for the recognition of sounds outside the set. For engineering studies, dimensions may be designed arbitrarily as long as they afford good performance in a specific task.
For the imaging studies, there is no suggestion yet as to which low-level acoustic features may be used to construct the various selectivity for high-level categories while preserving invariance within a category. Furthermore, there is a major gap between these studies and what is known from electrophysiological recordings in animal models. Decades of work have established that auditory cortical responses display rich and complex spectro-temporal receptive fields, even within primary areas  , .
This seems at odds with the limited set of spectral or temporal dimensions that are classically used to characterize timbre in perceptual studies. To bridge this gap, we investigate how cortical processing of spectro-temporal modulations can subserve both sound source recognition of musical instruments and perceptual timbre judgments.
Specifically, cortical receptive fields and computational models derived from them are shown to be suited to classify a sound source from its evoked neural activity, across a wide range of instruments, pitches and playing styles, and also to predict accurately human judgments of timbre similarities.
Responses in primary auditory cortex A1 exhibit rich selectivity that extends beyond the tonotopy observed in the auditory nerve. A1 neurons are not only tuned to the spectral energy at a given frequency, but also to the specifics of the local spectral shape such as its bandwidth  , spectral symmetry  , and temporal dynamics  Figure 1.
Each panel shows the receptive field of 1 neuron with red indicating excitatory preferred responses, and blue indicating inhibitory suppressed responses. Examples vary from narrowly tuned neurons top row to broadly tuned ones middle and bottom row.
They also highlight variability in temporal dynamics and orientation upward or downward sweeps. This rich cortical mapping may reflect an elegant strategy for extracting acoustic cues that subserve the perception of various acoustic attributes pitch, loudness, location, and timbre as well as the recognition of complex sound objects, such as different musical instruments.
This hypothesis was tested here by employing a database of spectro-temporal receptive fields STRFs recorded from single units in primary auditory cortex of 15 awake non-behaving ferrets. Such STRFs with a variety of nonlinear refinements have been shown to capture and predict well cortical responses to a variety of complex sounds like speech, music, and modulated noise  — .
To test the efficacy of STRFs in generating a representation of sound that can distinguish among a variety of complex categories, sounds from a large database of musical instruments were mapped onto cortical responses using the physiological STRFs described above. The time-frequency spectrogram for each note was convolved with each STRF in our neurophysiology database to yield a firing rate that is then integrated over time.
This initial mapping was then reduced in dimensionality using singular value decomposition to a compact eigen-space; then augmented with a nonlinear statistical analysis using support vector machine SVM with Gaussian kernels  see METHODS for details. Briefly, support vector machines are classifiers that learn to separate, in our specific case, the patterns of cortical responses induced by the different instruments. The use of Gaussian kernels is a standard technique that allows to map the data from its original space where data may not be linearly separable onto a new representational space that is linearly separable.
Ultimately, the analysis constructed a set of hyperplanes that outline the boundaries between different instruments. The identity of a new sample was then defined based on its configuration in this expanded space relative to the set of learned hyperplanes Figure 2. An acoustic waveform from a test instrument is processed through a model of cochlear and midbrain processing; yielding a time-frequency representation called auditory spectrogram.
This later is further processed through the cortical processing stage through neurophysiological or model spectro-temporal receptive fields. Cortical responses of the target instrument are tested against boundaries of a statistical SVM timbre model in order to identify the instrument's identity. This high classification accuracy was a strong indicator that neural processing at the level of primary auditory cortex could not only provide a basis for distinguishing between different instruments, but also had a robust invariant representation of instruments over a wide range of pitches and playing styles.
Despite the encouraging results obtained using cortical receptive fields, the classification based on neurophysiological recordings was hampered by various shortcomings including recording noise and other experimental constraints.
Also, the limited selection of receptive fields being from ferrets tended to under-represent parameter ranges relevant to humans such as lower frequencies, narrow bandwidths limited to a maximum resolution of 1. To circumvent these biases, we employed a model that mimics the basic transformations along the auditory pathway up to the level of A1.
Effectively, the model mapped the one-dimensional acoustic waveform onto a multidimensional feature space. Importantly, the model allowed us to sample the cortical space more uniformly than physiological data available to us, in line with findings in the literature  ,  , . The model operates by first mapping the acoustic signal into an auditory spectrogram. This initial transformation highlights the time varying spectral energies of different instruments which is at the core of most acoustic correlates and machine learning analyses of musical timbre  ,  ,  ,  , .
For instance, temporal features in a musical note include fast dynamics that reflect the quality of the sound scratchy, whispered, or purely voiced , as well as slower modulations that carry nuances of musical timbre such as attack and decay times, subtle fluctuations of pitch vibrato or amplitude shimmer.
Some of these characteristics can be readily seen in the auditory spectrograms, but many are only implicitly represented. For example, Figure 3A contrasts the auditory spectrogram of a piano vs.
For violin, the temporal cross-section reflects the soft onset and sustained nature of bowing and typical vibrato fluctuations; the spectral slice captures the harmonic structure of the musical note with the overall envelope reflecting the resonances of the violin body.
By contrast, the temporal and spectral modulations of a piano playing the same note are quite different. Temporally, the onset of piano rises and falls much faster, and its spectral envelope is much smoother. A The plot shows the time-frequency auditory spectrogram of piano and violin notes. The temporal and spectral slices shown on the right are marked. B The plots show magnitude cortical responses of four piano notes left panels , played in normal left and Staccato right at F4 top and F 4 bottom ; and four violin notes right panels , played in normal left and Pizzicatto right also at pitch F4 top and F 4 bottom.
The white asterisks upper leftmost notes in each quadruplet indicate the notes shown in part A of this figure. The cortical stage of the auditory model further analyzes the spectral and temporal modulations of the spectrogram along multiple spectral and temporal resolutions. The model projects the auditory spectrogram onto a 4-dimensional space, representing time, tonotopic frequency, spectral modulations or scales and temporal modulations or rates.
The four dimensions of the cortical output can be interpreted in various ways. In one view, the cortical model output is a parallel repeated representation of the auditory spectrogram viewed at different resolutions. A different view is one of a bank of spectral and temporal modulation filters with different tuning from narrowband to broadband spectrally, and slow to fast modulations temporally.
In such view, the cortical representation is a display of spectro-temporal modulations of each channel as they evolve over time. Ultimately each filter acts as a model cortical neuron whose output reflects the tuning of that neuronal site. We have not performed an analysis of the number of neurons needed for such task. Nonetheless, a large and uniform sampling of the space seemed desirable. Each instrument here is played at two distinct pitches with two different playing styles. The panels provide estimates of the overall distribution of spectro-temporal modulation of each sound.
How To Mix Music (Part 5): Mixing Instruments & Synths
How precisely does an acoustic guitar or violin produce its sweet sound? There is a simple, centuries-old way to literally "see" the vibrational patterns that cause the guitar to resonate and produce audible tones. The striking method is shown in this beautiful video: Vibrations are movements of an object in back-and-forth wave patterns.
The study of acoustics can help scientists produce beautiful music even with musical instruments fashioned with high-tech methods, such as 3D printing. Xiaoyu Niu, from the University of Chinese Academy Sciences, and other researchers studied the sound quality of a 3D-printed ukulele and compared it to a standard wooden instrument. Niu's talk is part of a session on "General Topics in Musical Acoustics," to be held beginning at a. The ukulele studied by Niu's group was created with a 3D printer using a type of plastic known as polylactic acid, or PLA.
Physics of Music: Why String Instruments Sound So Sweet
More specifically, the term "lute" can refer to an instrument from the family of European lutes. The term also refers generally to any string instrument having the strings running in a plane parallel to the sound table in the Hornbostel—Sachs system. The strings are attached to pegs or posts at the end of the neck, which have some type of turning mechanism to enable the player to tighten the tension on the string or loosen the tension before playing which respectively raise or lower the pitch of a string , so that each string is tuned to a specific pitch or note. The lute is plucked or strummed with one hand while the other hand "frets" presses down the strings on the neck's fingerboard. By pressing the strings on different places of the fingerboard, the player can shorten or lengthen the part of the string that is vibrating, thus producing higher or lower pitches notes. The European lute and the modern Near-Eastern oud descend from a common ancestor via diverging evolutionary paths. The lute is used in a great variety of instrumental music from the Medieval to the late Baroque eras and was the most important instrument for secular music in the Renaissance. It is also an accompanying instrument in vocal works. The lute player either improvises "realizes" a chordal accompaniment based on the figured bass part, or plays a written-out accompaniment both music notation and tablature "tab" are used for lute. As a small instrument, the lute produces a relatively quiet sound.
PARTS of the CELLO
Timbre is the attribute of sound that allows humans and other animals to distinguish among different sound sources. Studies based on psychophysical judgments of musical timbre, ecological analyses of sound's physical characteristics as well as machine learning approaches have all suggested that timbre is a multifaceted attribute that invokes both spectral and temporal sound features. Here, we explored the neural underpinnings of musical timbre. We used a neuro-computational framework based on spectro-temporal receptive fields, recorded from over a thousand neurons in the mammalian primary auditory cortex as well as from simulated cortical neurons, augmented with a nonlinear classifier. The model was able to perform robust instrument classification irrespective of pitch and playing style, with an accuracy of
This application is claims priority to U. This invention relates generally to a stringed instrument and more particularly to an instantly playable stringed instrument that enables a player to instantly and with reduced effort play every chord available with the use of one, two or three fingers. Guitars have been around for centuries in various forms, shapes and sizes.
Can 3D-printing musical instruments produce better sound than traditional instruments?
Oxford University Press Bolero Ozon. Steve Savage. Making great audio recordings requires striking the right balance between technical know-how and practical understanding of recording sessions. Even in the digital age, some of the most important aspects of creating and recording music are non-technical and, as a result, are often overlooked by traditional recording manuals.
World Scientific Bolero Ozon. Richard L. A truly Galilean-class volume, this book introduces a new method in theory formation, completing the tools of epistemology. It covers a broad spectrum of theoretical and mathematical physics by researchers from over 20 nations from four continents. Like Vigier himself, the Vigier symposia are noted for addressing avant-garde, cutting-edge topics in contemporary physics.
Speakerless Immersive Sound: Continental and Sennheiser Revolutionize Vehicle Audio
The Antebellum Era was a complex time in American culture. Young ladies had suitors call upon them, while men often settled quarrels by dueling, and mill girls worked hour days to help their families make ends meet. Yet at the same time, a new America was emerging.
What actually happens when you play a musical instrument in space?
Stringed instrument , any musical instrument that produces sound by the vibration of stretched strings , which may be made of vegetable fibre, metal, animal gut, silk, or artificial materials such as plastic or nylon. In nearly all stringed instruments the sound of the vibrating string is amplified by the use of a resonating chamber or soundboard. The string may be struck, plucked, rubbed bowed , or, occasionally, blown by the wind ; in each case the effect is to displace the string from its normal position of rest and to cause it to vibrate in complex patterns. Stringed instruments seem to have spread rapidly from one society to another across the length and breadth of Eurasia by means of great population shifts, invasions and counterinvasions, trade, and, presumably, sheer cultural curiosity.
Would a guitar sound the same on a space station?
Tim Rutherford-Johnson is a London-based music journalist and critic. He was the contemporary music editor at Grove Music Online and edited the most recent edition of the Oxford Dictionary of Music. He has taught at Goldsmiths College and Brunel University, and since he has written about new music for his blog, The Rambler.
The instrument's controlling section usually consists of two metal antennas that sense the relative position of the thereminist's hands and control oscillators for frequency with one hand, and amplitude volume with the other. The electric signals from the theremin are amplified and sent to a loudspeaker. The sound of the instrument is often associated with eerie situations. The theremin is also used in concert music especially avant-garde and 20th- and 21st-century new music , and in popular music genres such as rock. The theremin was the product of Soviet government-sponsored research into proximity sensors.
Spider silk spun into violin strings
I n this episode I explain to you step by step how we go about mixing instruments and mixing synths. There are so many different types of sounds, and they all need to be treated differently. With this series I help explain and teach music mixing to you — musicians, producers, and aspiring mixing engineers. I share our years of experience and insight on music production, mixing and mastering. Covering the necessary preparations, tools, underlying physics and insider tips and tricks to achieve the perfect mix and master.
Казалось, она его не слышала. Хейл понимал, что говорит полную ерунду, потому что Стратмор никогда не причинит ей вреда, и она это отлично знает. Хейл вгляделся в темноту, выискивая глазами место, где прятался Стратмор. Шеф внезапно замолчал и растворился во тьме.