Instrument Sounds Research Articles

Neuromusic: Music and its Influence on the Purchase Process in the Markets

Music the art of combining sounds, maintains a sequence of time with harmony, melody and rhythm, generates changes in cognitive processes, improves concentration, memory, influences mood, produces changes in emotions, helps control anxiety, increases motivation, and decreases cortisol levels, the stress hormone, activates the hormone of happiness and rest, serotonin and melatonin. The objective was to analyze the influence of music in the purchase process and decision. The approach was qualitative-quantitative, exploratory level, descriptive statistics was applied to identify the main trends and musical genres. The survey was carried out online, in the "La Condamine" market (November-December 2022). The sample 354 customers. The results of the investigation determined that customers enjoy listening to instrumental sounds in the food court, and in the vegetable, fruit and vegetable section; and the musical genre pop and/or reggaeton in the clothing section. The survey data was corroborated with the results of the biometric eyes tracking equipment in eight participants, attention, meditation and blinking were analyzed, with two sounds, sound 1: folk music and sound 2: pop; the average of the evaluations determined the level of acceptance of the participants for the musical genre, confirming the traditional survey with the results of the biometric equipment. Hearing goes beyond what can be perceived with sight and smell; there are sounds and melodies that connect us with emotions (laugh, cry, enjoy) and memories. It is concluded, (i) consider the musical genre according to the segment, (ii) the hours of greatest influx of customers, (iii) areas or sections of the market with greater or lesser product sales, (iv) slow music in places with more expensive products to extend their stay in the establishment.

КЫРГЫЗ ТИЛИНДЕГИ АЙРЫМ БАЙЫРКЫ МУЗЫКАЛЫК АСПАПТАРГА БАЙЛАНЫШТУУ СӨЗДӨРДҮН ЛЕКСИКА-СЕМАНТИКАЛЫК ӨЗГӨЧӨЛҮГҮ

The article presents the linguistic function of words Kyrgyz language. The lexical- semantic meanings of musical instruments, which appeared depending on the phenomena in the environment and the objects used, were interpreted. Musical instruments were used both as toys and as objects with religious beliefs. It was revealed that most of the names are formed from verbs, which were formed from imitative words. It is confirmed that the origin of the names is related to the material from which it is made and the function it performs. Synonymous variants of some ancient musical instruments in the Kyrgyz language are defined. It has been found that the sounds of instruments are closely related to the sounds of nature, the objects we use, the movements and sounds of animals. The lexical-semantic features of words related to Kyrgyz musical instruments are determined and the forms of their production are analyzed. The article reveals the role of ancient Kyrgyz national musical instruments in life and their linguistic function.

Neural Coincidence Detection Strategies during Perception of Multi-Pitch Musical Tones

Multi-pitch perception is investigated in a listening test using 30 recordings of musical sounds with two tones played simultaneously, except for two gong sounds with inharmonic overtone spectra, judging roughness and separateness as the ability to tell the two tones in each recording apart. Of the sounds, 13 were from a Western guitar playing all 13 intervals in one octave, the other sounds were mainly from non-Western instruments, comparing familiar with unfamiliar instrument sounds for Western listeners. Additionally the sounds were processed in a cochlea model, transferring the mechanical basilar membrane motion into neural spikes followed by post-processing simulating different degrees of coincidence detection. Separateness perception showed a clear distinction between familiar and unfamiliar sounds, while roughness perception did not. By correlating perception with simulation different perception strategies were found. Familiar sounds correlated strongly positively with high degrees of coincidence detection, where only 3–5 periodicities were left, while unfamiliar sounds correlated with low coincidence levels. This corresponds to an attention to pitch and timbre, respectively. Additionally, separateness perception showed an opposite correlation between perception and neural correlates between familiar and unfamiliar sounds. This correlates with the perceptional finding of the distinction between familiar and unfamiliar sounds with separateness.

Open-Set Recognition of Pansori Rhythm Patterns Based on Audio Segmentation

Pansori, a traditional Korean form of musical storytelling, is characterized by performances involving a vocalist and a drummer. It is well-known for the singer’s expressive narrative (aniri) and delicate gesture with fan in hand. The classical Pansori repertoires mostly tell love, satire, and humor, as well as some social lessons. These performances, which can extend from three to five hours, necessitate that the vocalist adheres to precise rhythmic structures. The distinctive rhythms of Pansori are crucial for conveying both the narrative and musical expression effectively. This paper explores the challenge of open-set recognition, aiming to efficiently identify unknown Pansori rhythm patterns while applying the methodology to diverse acoustic datasets, such as sound events and genres. We propose a lightweight deep learning-based encoder–decoder segmentation model, which employs a 2-D log-Mel spectrogram as input for the encoder and produces a frame-based 1-D decision along the temporal axis. This segmentation approach, processing 2-D inputs to classify frame-wise rhythm patterns, proves effective in detecting unknown patterns within time-varying sound streams encountered in daily life. Throughout the training phase, both center and supervised contrastive losses, along with cross-entropy loss, are minimized. This strategy aimed to create a compact cluster structure within the feature space for known classes, thereby facilitating the recognition of unknown rhythm patterns by allocating ample space for their placement within the embedded feature space. Comprehensive experiments utilizing various datasets—including Pansori rhythm patterns (91.8%), synthetic datasets of instrument sounds (95.1%), music genres (76.9%), and sound datasets from DCASE challenges (73.0%)—demonstrate the efficacy of our proposed method to detect unknown events, as evidenced by the AUROC metrics.

Impact of interference on vocal and instrument recognition.

Voices arguably occupy a superior role in auditory processing. Specifically, studies have reported that singing voices are processed faster and more accurately and possess greater salience in musical scenes compared to instrumental sounds. However, the underlying acoustic features of this superiority and the generality of these effects remain unclear. This study investigates the impact of frequency micro-modulations (FMM) and the influence of interfering sounds on sound recognition. Thirty young participants, half with musical training, engage in three sound recognition experiments featuring short vocal and instrumental sounds in a go/no-go task. Accuracy and reaction times are measured for sounds from recorded samples and excerpts of popular music. Each sound is presented in separate versions with and without FMM, in isolation or accompanied by a piano. Recognition varies across sound categories, but no general vocal superiority emerges and no effects of FMM. When presented together with interfering sounds, all sounds exhibit degradation in recognition. However, whereas /a/ sounds stand out by showing a distinct robustness to interference (i.e., less degradation of recognition), /u/ sounds lack this robustness. Acoustical analysis implies that recognition differences can be explained by spectral similarities. Together, these results challenge the notion of general vocal superiority in auditory perception.

The Creative of Isan Music in Soundscape, Battle of Naga

Objectives: This article is part of the research with the objective of creating Isan music in the soundscape, "Battle of Naga." Methods: The researcher used a creative process in music creation, consisting of two parts. The first part is song design, where the researcher took five parts from the story of Sinxay on the Battle of Naga to use as the song structure. Isan melody was used to reinterpret and give new meaning. The melodies included Lai Thang San, Lai Thang Yao, Lai Ka Ten Gon, Lai An Nangseu, Phaya poem reading, and Sinxay melody. The second part consisted of finding the soundscape from Isan musical instruments through the process of playing and adjusting the sounds using technology. This involved capturing sounds of Naga, water, wind, atmosphere, and various situational sounds from the story, and projecting the sounds of Isan musical instruments into water, mixing them with actual instrument sounds. Results: The sounds included the sound of Naga, water, wind, atmosphere, the sounds of situations happening in the story, and the sound projection of Isan musical instruments into the water mixed with the actual sounds of the instruments. At the same time, the sound was mixed with the blowing of wind into the microphone to create the sound of the Garuda’s wings. Additionally, the researcher used the microphone to rub against a fishtank to create sound effects for accompanying the music, such as in the battle scene. Conclusion: The creative process used in this research successfully blended traditional Isan melodies with innovative soundscapes, creating a unique auditory experience for the "Battle of Naga." This approach not only preserved the cultural essence of Isan music but also introduced new dimensions through the use of technology, enhancing the storytelling and emotional impact of the music.

Musical Instrument Identification Using Machine Learning

Music is a beautiful form of expression that is universally understood. In fact, people across all cultural backgrounds, even those without extensive exposure to musical education, can easily recognize the unique sounds of different instruments. This paper explores the use of machine learning to identify various instruments. The scope of this project focuses on audio recordings with one instrument playing one pitch, and the specific instruments in this study were viola, piano, and ukulele. This paper analyzes ways to quantify the differences in sounds of instruments using the harmonic frequency content apparent in spectrograms. It proposes the use of a simple but efficient K-Nearest-Neighbors machine learning algorithm. The results achieved 80% accuracy; using a larger dataset and using convolutional neural networks could improve accuracy of classification. The machine learning algorithm can be applied to the broader world of sound classification, and it can eventually surpass a human’s ability at identifying sounds (e.g. telling apart viola from violin, which the average human cannot do). There are numerous real-world applications for musical instrument recognition. It can enable automatic sorting and searching of massive musical collections with ease, which could be useful to streaming platforms that provide personalized recommendations to their users.

Acoustical Survey and Finite Element Analyses of Late Baroque Mandolin

Abstract Replicating ancient musical instruments is a method to protect fragile originals from extensive playing. In the case of stringed instruments, replicas are generally realized by luthiers using identical wood species and geometry, according to dimensional surveys. Although this procedure yields a highly similar visual aspect, the intrinsic variability of wood properties does not ensure an identical sound. Therefore, acoustic surveys are a fundamental step in reproducing the sounds of original instruments. In this work, we report the acoustical survey of a late baroque mandolin preserved at Museo degli Strumenti Musicali del Castello Sforzesco di Milano. The survey was conducted using portable equipment and included measurements of the radiated sound spectrum, admittance, monopole mobility, and mode shape assignment. Finite Element Analyses (FEA) enabled the assignment of mode shapes and quantification of the effect of a crack on the structural integrity and acoustics of the instrument. This study has laid the foundation for the creation of a replica that, beyond the visual aspect, would resemble the original instrument in terms of sound to the extent feasible.

Electronic wind instruments for mobility-restricted musicians

One of the earliest applications of analog music synthesizers was the simulation of orchestral instruments. Electronic wind instrument sounds were played through piano-like keyboards or interfaces with breath controllers that mimicked physical wind instruments. Synthesizers are also often the best alternative for artists with disabilities to play a musical instrument. This talk focuses on methods and experiences the authors were involved with to design electronic alternatives to wind instruments for people who lost control of their arms. The Jamboxx is a USB controller that was specifically designed as a head-only interface using a breath controller to simulate airflow but also operates as a suck/puff device to activate menus and buttons, a slider to manipulate pitch, and a tilt-sensor to control expression parameters. The Jamboxx also addressed musicians seeking a harmonica-type interface to augment their guitar performance. The AUMI instrument is a touchless device based on camera tracking. This talk will address different sound synthesis methods to achieve realistic wind instruments, the ability to integrate these instruments with other off-the-shelf products (e.g., VST plugins), and special requirements for artists with special needs to play these instruments without too much fatigue. [Work supported by the Craig H. Neilsen Foundation.]

Auditory brightness in room reverberation

This work examines the impact of room reverberation on the auditory perceptual brightness of sounds. We hypothesized that room size would impact the perception of brightness, with smaller rooms (less reverberant) leading to sounds being perceived as more bright than larger rooms. We generated single-note instrument sounds from five different instruments using Logic Pro which were reverberated in three different-sized simulated rooms. Reverberant stimuli were presented in sequential room-size pairs (small-medium, small-large, medium-large) in a randomized order. Participants (15 musicians) were asked to rate the brightness of the second sound as compared with the first using a Likert-style scale, and the stimuli were blocked by instrument such that in each block a participant would only hear room-size comparisons of one instrument. Results were analyzed using an ordered-probit regression of the brightness ratings against the spectral centroid of the stimuli. We found no clear effect of room size. However, brightness ratings were weakly correlated with the average spectral centroid as shown in previous literature, and a stronger correlation was found with the steady-state portion of the sound. This suggests room size has little effect on brightness. Future work should examine the effect of different segments (e.g., attack, steady-state) on perceived brightness.

Transfer Learning Using Musical Instrument Audio for Improving Automatic Singing Label Calibration

AbstractAutomatic Singing Label Calibration (ASLC) aims to enhance the labeling accuracy of coarse singing labels through the analysis of raw audio. However, the ASLC model faces limitations due to the challenges and costs associated with generating or augmenting real‐world songs. To address this problem, we propose a novel approach to strengthen limited singing audio using easily available musical instrument audio. Directly using the musical instrument audio as a data augmentation for the singing audio is unreliable due to the distinct differences between vocal and instrumental sounds. Therefore, we employ transfer learning, which allows relevant knowledge to be transferred from one domain to another. In the pre‐training stage, the ASLC model learns to predict the accurate labels from the musical instrument audio. We then consider the vocal as a special musical instrument and fine‐tune the pretrained ASLC model using a singing annotation data set. Experimental results demonstrate that our transfer learning‐based approach outperforms the original ASLC model. By leveraging the readily available musical instrument audio, our method achieves improved performance in enhancing the labeling accuracy of singing audio. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

Iontronic Dynamic Sensor with Broad Bandwidth and Flat Frequency Response Using Controlled Preloading Strategy.

Rapid advancements in human-machine interaction and voice biometrics impose desirability on soft mechanical sensors for sensing complex dynamic signals. However, existing soft mechanical sensors mainly concern quasi-static signals such as pressure and pulsation for health monitoring, limiting their applications in emerging wearable electronics. Here, we propose a hydrogel-based soft mechanical sensor that enables recording a wide range of dynamic signals relevant to humans by combining a preloading design strategy and iontronic sensing mechanism. The proposed sensor offers a two-orders-of-magnitude larger working bandwidth (up to 1000 Hz) than most of the reported soft mechanical sensors and meanwhile provides a high sensitivity (-23 dB) that surpasses the common commercial microphone. The amplitude-frequency characteristic of the proposed sensor can be precisely tuned to meet the desired requirement by adjusting the preloads and the parameters of the microstructured hydrogel. The sensor is capable of recording instrumental sounds with high fidelity from simple pure tones to melodic songs. Demonstration of a skin-mountable sensor used for human-voice-based remote control of a toy car shows great potential for applications in the voice user interface of human-machine interactions.

Exploring the relation between fundamental frequency and spectral envelope in the perception of musical instrument sounds

Pitch and timbre perception centrally depend on fundamental frequency (F0) and spectral envelopes (SE), respectively. However, timbre perception as a function of F0 is less explored in the literature. An analysis-synthesis approach was used to compare sounds with congruent and incongruent F0 and SE parameters via sound pleasantness, brightness, and plausibility ratings. Analyzing 1900 musical instruments and voice sounds, a two-dimensional synthesis space was derived. The space was sampled in its inner and outer regions, reflecting high and low similarity to existing acoustical instrument sounds. Sounds with congruent and incongruent F0-SE relations were synthesized, defining congruency as the co-occurrence of F0 and SE in the analyzed set of sounds. In Experiment 1, inner regions of the space were rated as more pleasant compared to outer regions. Further, sounds with incongruent F0-SE relation were rated as less pleasant only for F0 discrepancies above two octaves. Finally, the first dimension of the space strongly reflected differences in brightness when F0 was controlled for. In Experiment 2, a spectrally more fine-grained resynthesis approach revealed effects of congruency on pleasantness and plausibility for smaller F0 separations. These results provide evidence for a co-dependence of F0 and SE properties in the perception of harmonic sounds.

Comparison of Heard and Imagined Blends of Instrumental Dyads

Timbral blend is fundamental in various musical activities for shaping sounds and musical intentions. Previous studies on blend perception have mostly focused on sounding blends, neglecting imagined blend made possible by inner hearing where sounds are imagined in the mind. Experiment 1 investigated how imagined blend compares to the perception of heard blend and whether musical background has an effect. Two groups of participants (musicians and nonmusicians; N = 31 per group) were presented with pairs of short instrument sounds in unison from 14 different instruments in two different experimental conditions. In the first condition, paired sounds were played sequentially, and participants were instructed to imagine them being played simultaneously and to rate their degree of blend. In the second condition, pairs of instrument sounds were played simultaneously, and participants were asked to rate the perceived degree of blend. Results showed significant interaction effects among the instrument pairs, presentation conditions, and musical backgrounds. Acoustic modeling and multidimensional scaling of blend ratings showed both varying and invariant roles of different acoustic features between the two types of blend perception. Imagining blend appears to be more sensitive to differences in brightness and richness of the high partial content between the blending sounds. Experiment 2 with 48 participants was conducted on the perception of dissimilarity between instruments, using the same stimuli as the previous experiment. Results from this experiment provided evidence that evaluating imagined blends is strongly informed by judging the dissimilarity of blending instruments. In practice, how the two types of blends differ is a result of complex interactions involving the specificity of blending instruments and listeners’ musical backgrounds.

Visually Guided Sound Source Separation With Audio-Visual Predictive Coding.

The framework of visually guided sound source separation generally consists of three parts: visual feature extraction, multimodal feature fusion, and sound signal processing. An ongoing trend in this field has been to tailor involved visual feature extractor for informative visual guidance and separately devise module for feature fusion, while utilizing U-Net by default for sound analysis. However, such a divide-and-conquer paradigm is parameter-inefficient and, meanwhile, may obtain suboptimal performance as jointly optimizing and harmonizing various model components is challengeable. By contrast, this article presents a novel approach, dubbed audio-visual predictive coding (AVPC), to tackle this task in a parameter-efficient and more effective manner. The network of AVPC features a simple ResNet-based video analysis network for deriving semantic visual features, and a predictive coding (PC)-based sound separation network that can extract audio features, fuse multimodal information, and predict sound separation masks in the same architecture. By iteratively minimizing the prediction error between features, AVPC integrates audio and visual information recursively, leading to progressively improved performance. In addition, we develop a valid self-supervised learning strategy for AVPC via copredicting two audio-visual representations of the same sound source. Extensive evaluations demonstrate that AVPC outperforms several baselines in separating musical instrument sounds, while reducing the model size significantly. Code is available at: https://github.com/zjsong/Audio-Visual-Predictive-Coding.

The Analysis of Organizational Changes using Structural Equation Modelling with Mediating Readiness to Change in Higher Education

This research study demonstrates that the readiness to change moderates the association between supervisor support and commitment to organizational change. The variable "readiness to change" fulfills this moderating function. The State University in West Sumatra, a prestigious institution of higher education in Indonesia, boasts a faculty comprising 260 committed teaching personnel. The formulation of the questionnaire was grounded upon a predetermined set of criteria. The data analysis process involved utilizing Structural Equation Modelling (SEM) and SmartPLS 3.0 software. The results obtained from using structural equation modeling (SEM) were consistent with recognized metrics such as Cronbach alpha, composite reliability, mean-variance extracted, and evaluation criteria for both measurement and structural models. Furthermore, it also showcased the soundness and reliability of the measurement instruments. The study suggests that the mediating factor of preparation for change plays a role in the association between the provision of high-quality support and the level of commitment towards organizational change. This study contributes to the field of management and organizational leadership by providing insights on how to develop robust change strategies through enhancing employees' readiness for transition. This study makes a valuable contribution to the field of change management by emphasizing the role of readiness to change as a mediating factor in the relationship between supervisor support and organizational change commitment. This research additionally aids organizations in developing grooming and training programs aimed at equipping employees with the necessary skills and knowledge to adapt to change.

The Analysis of Organizational Changes using Structural Equation Modelling with Mediating Readiness to Change in Higher Education

This research study demonstrates that the readiness to change moderates the association between supervisor support and commitment to organizational change. The variable "readiness to change" fulfills this moderating function. The State University in West Sumatra, a prestigious institution of higher education in Indonesia, boasts a faculty comprising 260 committed teaching personnel. The formulation of the questionnaire was grounded upon a predetermined set of criteria. The data analysis process involved utilizing Structural Equation Modelling (SEM) and SmartPLS 3.0 software. The results obtained from using structural equation modeling (SEM) were consistent with recognized metrics such as Cronbach alpha, composite reliability, mean-variance extracted, and evaluation criteria for both measurement and structural models. Furthermore, it also showcased the soundness and reliability of the measurement instruments. The study suggests that the mediating factor of preparation for change plays a role in the association between the provision of high-quality support and the level of commitment towards organizational change. This study contributes to the field of management and organizational leadership by providing insights on how to develop robust change strategies through enhancing employees' readiness for transition. This study makes a valuable contribution to the field of change management by emphasizing the role of readiness to change as a mediating factor in the relationship between supervisor support and organizational change commitment. This research additionally aids organizations in developing grooming and training programs aimed at equipping employees with the necessary skills and knowledge to adapt to change.

The Analysis of Organizational Changes using Structural Equation Modelling with Mediating Readiness to Change in Higher Education

This research study demonstrates that the readiness to change moderates the association between supervisor support and commitment to organizational change. The variable "readiness to change" fulfills this moderating function. The State University in West Sumatra, a prestigious institution of higher education in Indonesia, boasts a faculty comprising 260 committed teaching personnel. The formulation of the questionnaire was grounded upon a predetermined set of criteria. The data analysis process involved utilizing Structural Equation Modelling (SEM) and SmartPLS 3.0 software. The results obtained from using structural equation modeling (SEM) were consistent with recognized metrics such as Cronbach alpha, composite reliability, mean-variance extracted, and evaluation criteria for both measurement and structural models. Furthermore, it also showcased the soundness and reliability of the measurement instruments. The study suggests that the mediating factor of preparation for change plays a role in the association between the provision of high-quality support and the level of commitment towards organizational change. This study contributes to the field of management and organizational leadership by providing insights on how to develop robust change strategies through enhancing employees' readiness for transition. This study makes a valuable contribution to the field of change management by emphasizing the role of readiness to change as a mediating factor in the relationship between supervisor support and organizational change commitment. This research additionally aids organizations in developing grooming and training programs aimed at equipping employees with the necessary skills and knowledge to adapt to change.

CLASSIFICATION OF INSTRUMENT SOUNDS WITH IMAGE CLASSIFICATION ALGORITHMS

Classification of audio files using CNN (Convolutional Neural Network) algorithm is an important application in the field of audio processing and artificial intelligence. This process aims to automatically classify audio files into different classes and can be used in speech recognition, emotional analysis, voice-based control systems and many other applications. The aim of this study is to perform spectrum transformation of instrumental sounds and classify them using image classification algorithms. The dataset contains a total of 1500 data from five different instruments. Audio files were processed, and signal and spectrogram images of each audio file were obtained. DenseNet121, ResNet and CNN algorithms were tested in experimental studies. The most successful results belong to the CNN algorithm with 99.34%.

Effect of physical characteristics of strings on the instrumental sound of violin

The stringed instrument can produce different vibration characteristics depending on the materials and types of vibrating parts such as strings, bridge, and body. So, the timbre of the violin is considered to be affected by the physical characteristics of the strings and body. In recent years, experiments have been conducted to compare the timbre quality of several violins of different materials and shapes, and their evaluation has also been conducted. On the other hand, there have been few studies on strings for stringed instruments. There are many types of strings. The strings are the source of vibration, with different materials and physical properties, and these are thought to affect the timbre. In this study, violins made by different manufacturers, with strings of different materials, will be used to examine what trends exist in the frequency spectrum of instrumental sounds and how the physical characteristics influence the sound impression.