Project MUSE®: Computer Music Journal - Latest Articles
https://muse.jhu.edu/journal/34
Project MUSE®: Latest articles in Computer Music Journal.daily12026-05-12T00:00:00-05:00text/htmlen-USVol. 25 (2001) through current issueLatest Articles: Computer Music JournalTWOProject MUSE®Computer Music Journal1531-51690148-9267Latest articles in Computer Music Journal. Feed provided by Project MUSE®The Kolmogorov Complexity of Traditional Irish Dance Music
https://muse.jhu.edu/article/983541
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The Kolmogorov complexity (Kolmogorov 1968; Li and Vitányi 2019) of a sequence of letters is defined to be the length of the shortest program that will output that sequence of letters. As such, it is known to be uncomputable. Nonetheless, it can be estimated in a reliable way using lossless compression techniques such as Lempel-Ziv (LZ) compression (Ziv and Lempel 1977). Over two decades ago, Cilibrasi, Vitányi, and de Wolf introduced the use of Kolmogorov complexity theory (via the normalized compression distance) in music analysis (Cilibrasi, Vitányi, and de Wolf 2004). Li and Sleep (2004a, b) showed how these ideas can be used to estimate the similarity between different melodies. Since then, many authors have
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Project MUSE®https://muse.jhu.edu/2026-05-12T00:00:00-05:00https://muse.jhu.edu/journal/34/image/coversmallThe Kolmogorov Complexity of Traditional Irish Dance Music2026-02-26text/htmlen-USThe Kolmogorov Complexity of Traditional Irish Dance Music2026-02-262026TWOProject MUSE®315432026-05-12T00:00:00-05:002026-02-26Toward Guided Musical Form: Structuring Generative Music with LLMs
https://muse.jhu.edu/article/983539
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Recent advances in generative machine learning models have reignited interest in algorithmic music generation. Earlier models such as generative adversarial networks (Goodfellow et al. 2014) and variational autoencoders (Kingma and Welling 2022) demonstrated early promise, but their reliance on convolutional layers made them computationally inefficient for modeling long sequences like music or text. The introduction of the Transformer architecture (Vaswani et al. 2017) marked a turning point: By replacing convolutional filters with self-attention mechanisms, Transformers enabled more scalable sequence modeling. Although recent work shows that convolutional networks can be competitive with Transformers (Liu et al.
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Project MUSE®https://muse.jhu.edu/2026-05-12T00:00:00-05:00https://muse.jhu.edu/journal/34/image/coversmallToward Guided Musical Form: Structuring Generative Music with LLMs2026-02-26text/htmlen-USToward Guided Musical Form: Structuring Generative Music with LLMs2026-02-262026TWOProject MUSE®860092026-05-12T00:00:00-05:002026-02-26Composing for Performers and Electronics Using a Dynamically Trained Neuron as an Effects Modulator
https://muse.jhu.edu/article/983540
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In 2006, Bown and Lexer suggested the use of continuous-time recurrent neural networks for generative and interactive music performance. They observed that musically pleasing results can be obtained by connecting neurons' oscillations to "various synthesis or filter parameters" (Bown and Lexer 2006, p. 660).Expanding this approach to mixed composition (i.e., music for live instruments and electronics), this study proposes the use of a "simple neuron" (Spencer-Harper 2015) to oscillate the parameters of a delay plug-in, driven by the neuron's dynamically changing output during training. The parameters that are oscillated at each moment are randomly selected. The neuron is implemented in a Max for Live patch that
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Project MUSE®https://muse.jhu.edu/2026-05-12T00:00:00-05:00https://muse.jhu.edu/journal/34/image/coversmallComposing for Performers and Electronics Using a Dynamically Trained Neuron as an Effects Modulator2026-02-26text/htmlen-USComposing for Performers and Electronics Using a Dynamically Trained Neuron as an Effects Modulator2026-02-262026TWOProject MUSE®1081082026-05-12T00:00:00-05:002026-02-26Audio, Vibrotactile, and Visual Augmentation of an Electric Guitar: A Pilot Study
https://muse.jhu.edu/article/983538
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When investigating how musical instruments are perceived by their players, scientists generally focus on sound and auditory perception only, leading to experimental protocols in which other sensory modalities are hindered. A typical example is the listening test, a rather unnatural situation for musicians in which they are asked to trigger sound recordings of an instrument they cannot see, touch, or smell, etc., as they usually would (Paté et al. 2015; Fritz and Dubois 2015). In real life, musicians are involved in a complex, multimodal relationship with their instruments. In order to study and understand the perception of musical instruments, this multimodal relationship must be kept when designing experimental
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Project MUSE®https://muse.jhu.edu/2026-05-12T00:00:00-05:00https://muse.jhu.edu/journal/34/image/coversmallAudio, Vibrotactile, and Visual Augmentation of an Electric Guitar: A Pilot Study2026-02-26text/htmlen-USAudio, Vibrotactile, and Visual Augmentation of an Electric Guitar: A Pilot Study2026-02-262026TWOProject MUSE®1708402026-05-12T00:00:00-05:002026-02-26Sound and Video Anthology: Program Notes
https://muse.jhu.edu/article/983542
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When Computer Music Journal's Editor, Doug Keislar, invited me to create an anthology for the Journal, I decided to focus on the intersecting lines that reflect my personal, rather wide interests in music and sound. My artistic journey was most strongly influenced by three figures: Clarence Barlow, whom I met in Cologne in the early 1980s, György Ligeti, whose composition seminars I regularly attended in Hamburg in the late 1980s, and David Wessel, who became my mentor at UC Berkeley's Center for New Music and Audio Technologies (CNMAT) in the 1990s. These three outstanding individuals sharpened my artistic sensibilities, which encompass the creation of musical works, the development of software to facilitate
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Project MUSE®https://muse.jhu.edu/2026-05-12T00:00:00-05:00https://muse.jhu.edu/journal/34/image/coversmallSound and Video Anthology: Program Notes2026-02-26text/htmlen-USSound and Video Anthology: Program Notes2026-02-262026TWOProject MUSE®447042026-05-12T00:00:00-05:002026-02-26About This Issue
https://muse.jhu.edu/article/983543
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You are reading Computer Music Journal's final "About This Issue." For decades, "About This Issue" provided the sole summary of the contents of an issue's articles. Since 2012, however, each article has included its own abstract, lessening the need for such a summary—though "About This Issue" has also offered occasional perspectives on commonalities between articles within an issue and across issues, as well as summaries of other sections of the Journal.That said, the reason for discontinuing "About This Issue" is more fundamental. The present issue concludes Volume 48 of the Journal, and starting with Volume 49 the Journal will adopt a "continuous publication" model. That approach involves publishing individual
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Project MUSE®https://muse.jhu.edu/2026-05-12T00:00:00-05:00https://muse.jhu.edu/journal/34/image/coversmallAbout This Issue2026-02-26text/htmlen-USAbout This Issue2026-02-262026TWOProject MUSE®85062026-05-12T00:00:00-05:002026-02-26News
https://muse.jhu.edu/article/983544
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The Ars Electronica Festival for Art, Technology, and Society took place 3–7 September 2025 in Linz, Austria. In the category of Digital Musics and Sound Art, the Golden Nica went to Navid Navab and Garnet Willis for Organism, a performance platform, installation, and live performance work employing a multi-jointed structure with electronics to drive and gather data from its swinging motions, mapped to a reconstructed pipe organ that has had turbulence-mitigating measures removed. Awards of distinction went to Jonathan Chaim Reus for BLA BLAVATAR vs JAAP BLONK, a performance for voice and neural network that begins with providing vocal sounds for a training dataset and evolves into an improvisational duo, and Ioana
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Project MUSE®https://muse.jhu.edu/2026-05-12T00:00:00-05:00https://muse.jhu.edu/journal/34/image/coversmallNews2026-02-26text/htmlen-USNews2026-02-262026TWOProject MUSE®56272026-05-12T00:00:00-05:002026-02-26Per Bloland: Shadows of the Electric Moon (review)
https://muse.jhu.edu/article/983545
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Per Bloland's latest album, Shadows of the Electric Moon on New Focus Recordings, contains five instrumental and electroacoustic works. Composed between 2013 and 2021, the music on this album is based upon two through lines—fictional narratives and the composer's software programming, used to process sound and integrate the electronics parts into the overall work. According to the liner notes, the interaction between these two through lines "is highly inventive in its subtle modulation of both instrumental and electronic textures, while unfolding within narrative, dramatic structures." The composer's custom-made processing software largely serves to foreground subtle or idiosyncratic instrumental timbres, which he
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Project MUSE®https://muse.jhu.edu/2026-05-12T00:00:00-05:00https://muse.jhu.edu/journal/34/image/coversmallPer Bloland: Shadows of the Electric Moon (review)2026-02-26text/htmlen-USPer Bloland: Shadows of the Electric Moon (review)2026-02-262026TWOProject MUSE®164372026-05-12T00:00:00-05:002026-02-26Products of Interest
https://muse.jhu.edu/article/983546
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Audient's iD48 is an audio interface that features 24 inputs, 32 outputs, 8 microphones preamplifiers, 32-bit conversion, and switchable analog insert technology (see Figure 1). The interface has eight combination microphone/line inputs on the rear panel. The eight ultra-low noise, low distortion, Class-A microphone preamplifiers use the same analog, discrete preamplifier circuit from the company's ASP8024-HE recording console. They provide 68 dB of gain, a dynamic range of 126.5 dB, equivalent input noise (EIN) of –127 dBu, and signal-to-noise ratio (SNR) of 99 dBA. The interface features 32-bit ESS D–A converter technology for clear, clean audio. It can support up to 96-kHz sampling at 24-bit depth. Each channel
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Project MUSE®https://muse.jhu.edu/2026-05-12T00:00:00-05:00https://muse.jhu.edu/journal/34/image/coversmallProducts of Interest2026-02-26text/htmlen-USProducts of Interest2026-02-262026TWOProject MUSE®502842026-05-12T00:00:00-05:002026-02-26