Hornbostel-Sachs Classification of Musical Instruments

Lee, Deborah

doi:10.5771/0943-7444-2020-1-72

Cited by 12 publications

(10 citation statements)

References 14 publications

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“…Thus, recent scholarship has sought to revisit musical phenomena for classification in the Semantic Web, deliberately apart from the classification of musical documents. Lee (2011, 2015, 2017a, 2017b) and Lee and Robinson (2017) has been the most prolific analyst of musical phenomena, producing detailed analyses of concepts of medium of performance, musical reception, and musical ensembles. Lee et al (2018) describe the situation of relationships among phenomena between “scientific” and “bibliographic” classifications of musical phenomena.…”

Section: Some Applicationsmentioning

confidence: 99%

Phenomenon‐based classification: An Annual Review of Information Science and Technology (ARIST) paper

Gnoli,

Smiraglia,

Szostak

2024

Asso for Info Science & Tech

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While bibliographic classifications are traditionally based on disciplines, the logical alternative is phenomenon‐based classification. Although not prevalent, this approach has been explored in the 20th century by J.D. Brown, the Classification Research Group, and others. Its principles have been stated in the León Manifesto (2007) and are currently represented by such general schemes as the Basic Concepts Classification and the Integrative Levels Classification. A phenomenon‐based classification lists classes of phenomena, including things and processes irrespective of the discipline studying them (which can optionally be specified as an additional facet). Facets can work in a phenomenon‐based system much as in a disciplinary one. This kind of system will promote the identification of potential relationships between research in different disciplines, and will especially benefit interdisciplinary work. The paper reviews the theory, history, structure, advantages, applications, and evaluation of phenomenon‐based classification systems.

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Section: Some Applicationsmentioning

confidence: 99%

Phenomenon‐based classification: An Annual Review of Information Science and Technology (ARIST) paper

Gnoli,

Smiraglia,

Szostak

2024

Asso for Info Science & Tech

View full text Add to dashboard Cite

show abstract

“…Path (Feilden et al, 2017;Wat et al, 2019a;Wilkerson et al, 2016) Freeform extrusion-based 3D printing (Feilden et al, 2017;Wat et al, 2019a;Wilkerson et al, 2016) Freeform digital light processing 3D printing (Hofer et al, 2021) High-temperature sintering (typically 1600 °C (Feilden et al, 2017;Le Ferrand and Bouville, 2019;Hofer et al, 2021) Sound-Related Properties Categories of Instruments. Musical instruments were classified following Sachs-Hornbostel: aerophones, membranophones, chordophones, and idiophones (Lee, 2019). Aerophones, membranophones, and chordophones produce sounds by the resonance in a cavity of the vibration of the air induced by blowing, a vibrating membrane, and a vibrating string, respectively.…”

Section: Estimation Of the Weightmentioning

confidence: 99%

Could Bio-Inspired Nacre-Like Ceramics be Suitable to Fabricate Musical Instruments?

Ferrand

2022

Music & Science

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In the past, natural ceramic biomaterials like bones and seashells were used to make music. Today, ceramics are largely absent from the musical scene. Yet, recent development of bio-inspired ceramics could be used to create musical instruments that emulate sound from ancient times, that do not make use of endangered animal species or that enable exploration of new types of music. In this paper, the question of whether bio-inspired ceramics would be suitable for usage in musical instrument is posed. The study focusses on nacre-like alumina ceramics of various compositions and their suitability to be used, fabricated, and to produce sound are discussed based on materials’ properties. It is found that flat pieces could be produced with high throughput for making idiophones or parts of musical instruments to increase the sound radiance, for example, and that complex shapes could be produced by a craftsperson to reproduce other musical instruments’ designs or create new ones using 3D printing technologies. The potential application of such ceramics for music could also open ideas in architecture where tiles are used, for example. Future work to enable these applications should be on more thorough characterisation of dynamic properties according to standards, scaled-up and reliable fabrication processes, and evaluation of the sound produced.

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“…Framing & Windowing is the technique of framing means dividing the audio into small frame, the process of framing involves breaking the voice stream into brief frames, which are generally between 5 and 50 milliseconds length [3]. Windowing is the technique of adjusting each frame's window to minimize discontinuities and leakage at start and end of the each frame [4] [5]. FFT/DFT is an algorithm known as fast Fourier transform (FFT) computes a sequence's Discrete Fourier transform (DFT) or its inverse (IDFT) [6].In our research our all-music wav's sample rate is 44.1 kHz, window length is 512 which means 512 samples in each frame.…”

Section: Wav Pre-processingmentioning

confidence: 99%

Attention-based CNN-BiGRU for Bengali Music Emotion Classification

Ghosh

Riad

2022

ijcs

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For Bengali music emotion classification, deep learning models, particularly CNN and RNN are frequently used. To extract meaningful knowledge, however, past studies' shortcomings of low accuracy and overfitting have to be addressed. We have proposed a model combining Conv1D, Bi-GRU and the Bahdanau attention mechanism for music emotion classification of our Bengali music dataset. The model integrates distinct MFCCs wav preprocessing methods with deep learning methods and attention-based methods. The attention mechanism has increased the accuracy of the proposed classification model. The music is finally classified into one of the four emotion classes: Angry, Happy, Relax, Sad. The proposed Conv1D+BiGRU+Attention model is validated as more effective and efficient at classifying emotions in the Bengali music dataset than baseline methods, according to comparisons with baseline models. For our Bengali music dataset, the performance of our proposed model is 95%.

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Hornbostel-Sachs Classification of Musical Instruments

Cited by 12 publications

References 14 publications

Phenomenon‐based classification: An Annual Review of Information Science and Technology (ARIST) paper

Phenomenon‐based classification: An Annual Review of Information Science and Technology (ARIST) paper

Could Bio-Inspired Nacre-Like Ceramics be Suitable to Fabricate Musical Instruments?

Attention-based CNN-BiGRU for Bengali Music Emotion Classification

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