Audio Content Descriptors of Timbre

Caetano, Marcelo; Saitis, Charalampos; Siedenburg, Kai

doi:10.1007/978-3-030-14832-4_11

Cited by 16 publications

(18 citation statements)

References 68 publications

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“…The timbre of the clarinet sound, which is sometimes described as “hollow,” has been linked to a concentration of spectral energy near odd harmonics, or the odd-to-even harmonic energy ratio (Caetano, Saitis, & Siedenburg, 2019). Although the B-flat clarinet was not rated in the current study, the bass clarinet was ranked fourth on hollow with a rating of 3.71.…”

Section: Discussionmentioning

confidence: 99%

Using auditory imagery tasks to map the cognitive linguistic dimensions of musical instrument timbre qualia.

Reymore¹,

Huron²

2020

Psychomusicology: Music, Mind, and Brain

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Two studies are reported related to musical instrument timbre qualia. In the first study, open-ended interviews were conducted with 23 musicians who were asked to describe their phenomenal experiences of imagined sounds for 20 Western instruments. A content analysis of the transcribed interviews suggests 77 qualitative categories underlying the musicians’ descriptions. In a second study, 460 musician participants rated subsets of the same 20 imagined instrument sounds according to the 77 categories derived from Study 1. Principal component analyses were applied to the results of Study 2, yielding several models. Researcher interpretations of the components in these models were combined with the results of supplementary polls where musicians rated the descriptive utility of each candidate component, producing a final 20-dimensional timbre qualia model. The model dimensions include rumbling/low, soft/singing, watery/fluid, direct/loud, nasal/reedy, shrill/noisy, percussive, pure/clear, brassy/metallic, raspy/grainy, ringing/long decay, sparkling/brilliant, airy/breathy, resonant/vibrant, hollow, woody, muted/veiled, sustained/even, open, and focused/compact.

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Section: Discussionmentioning

confidence: 99%

Using auditory imagery tasks to map the cognitive linguistic dimensions of musical instrument timbre qualia.

Reymore¹,

Huron²

2020

Psychomusicology: Music, Mind, and Brain

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“…Due to the noisy and inharmonic spectra of distorted guitar sounds, additional sound descriptors were considered for a more comprehensive characterisation of stimuli (for more details on the audio descriptors, see Caetano et al, 2019) : 1. Spectral Entropy.…”

Section: Materials and Stimulimentioning

confidence: 99%

Confusingly Similar: Discerning between Hardware Guitar Amplifier Sounds and Simulations with the Kemper Profiling Amp

et al. 2020

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Over the last decades, the simulation of musical instruments by digital means has become an important part of modern music production and live performance. Since the first release of the Kemper Profiling Amplifier (KPA) in 2011, guitarists have been able to create and store a nearly unlimited number of “digital fingerprints” of amplifier and cabinet setups for live performances and studio productions. However, whether listeners can discriminate between the sounds of the KPA and the original amplifier remains unclear. Thus, we constructed a listening test based on musical examples from both sound sources. In a first approach, the psychoacoustic analysis using mel-frequency cepstrum coefficients (MFCCs) revealed a high degree of timbre similarity between the two sound sources. In a second step, a listening test with N = 177 showed that the overall discrimination performance was d’ = .34, which was a rather small difference (0.0 ≤ d’ ≤ 0.74). A weak relationship between the degree of general musical sophistication and discrimination performance was found. Overall, we suggest that listeners are rarely able to assign audio examples to the correct condition. We conclude that, at least on a perceptual level, our results give no support for a commonly accepted pessimistic attitude toward digital simulations of hardware sounds.

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“…In addition, previous publications on joint time–frequency scattering lack a human-centric evaluation, independently from any classification task. Beyond the case of STRF, we refer to [ 58 ] for a detailed review of the state of the art on audio descriptors of timbre.…”

Section: Related Workmentioning

confidence: 99%

Time–frequency scattering accurately models auditory similarities between instrumental playing techniques

Lostanlen

El-Hajj

Rossignol³

et al. 2021

J AUDIO SPEECH MUSIC PROC.

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Instrumentalplaying techniques such as vibratos, glissandos, and trills often denote musical expressivity, both in classical and folk contexts. However, most existing approaches to music similarity retrieval fail to describe timbre beyond the so-called “ordinary” technique, use instrument identity as a proxy for timbre quality, and do not allow for customization to the perceptual idiosyncrasies of a new subject. In this article, we ask 31 human participants to organize 78 isolated notes into a set of timbre clusters. Analyzing their responses suggests that timbre perception operates within a more flexible taxonomy than those provided by instruments or playing techniques alone. In addition, we propose a machine listening model to recover the cluster graph of auditory similarities across instruments, mutes, and techniques. Our model relies on joint time–frequency scattering features to extract spectrotemporal modulations as acoustic features. Furthermore, it minimizes triplet loss in the cluster graph by means of the large-margin nearest neighbor (LMNN) metric learning algorithm. Over a dataset of 9346 isolated notes, we report a state-of-the-art average precision at rank five (AP@5) of .%. An ablation study demonstrates that removing either the joint time–frequency scattering transform or the metric learning algorithm noticeably degrades performance.

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Audio Content Descriptors of Timbre

Cited by 16 publications

References 68 publications

Using auditory imagery tasks to map the cognitive linguistic dimensions of musical instrument timbre qualia.

Using auditory imagery tasks to map the cognitive linguistic dimensions of musical instrument timbre qualia.

Confusingly Similar: Discerning between Hardware Guitar Amplifier Sounds and Simulations with the Kemper Profiling Amp

Time–frequency scattering accurately models auditory similarities between instrumental playing techniques

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