Timbre and the Perceptual Effects of Three Types of Data Reduction

Charbonneau, Georges

doi:10.2307/3679875

Cited by 21 publications

(7 citation statements)

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“…Onset asynchrony -By tracking the spectral envelope over time, with concurrent pitch estimates, it is possible to measure the onset characteristics of a musical tone's harmonics in a psychophysically appropriate manner. Since the first few harmonics are resolved by the filterbank processing stage, they may be monitored individually; upper harmonics may also be monitored, as groups within third-octave bands (this level of detail is consistent with Charbonneau's (1981) study of human sensitivity to the behavior of individual partials).…”

Section: Feature Extractionmentioning

confidence: 72%

Musical instrument identification: A pattern-recognition approach

Martin¹,

Kim²

1998

The Journal of the Acoustical Society of America

105

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A statistical pattern-recognition technique was applied to the classification of musical instrument tones within a taxonomic hierarchy. Perceptually salient acoustic featuresrelated to the physical properties of source excitation and resonance structure-were measured from the output of an auditory model (the log-lag correlogram) for 1023 isolated tones over the full pitch ranges of 15 orchestral instruments. The data set included examples from the string (bowed and plucked), woodwind (single, double, and air reed), and brass families. Using 70%/30% splits between training and test data, maximum a posteriori classifiers were constructed based on Gaussian models arrived at through Fisher multiplediscriminant analysis. The classifiers distinguished transient from continuant tones with approximately 99% correct performance. Instrument families were identified with approximately 90% performance, and individual instruments were identified with an overall success rate of approximately 70%. These preliminary analyses compare favorably with human performance on the same task and demonstrate the utility of the hierarchical approach to classification.

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Section: Feature Extractionmentioning

confidence: 72%

Musical instrument identification: A pattern-recognition approach

Martin¹,

Kim²

1998

The Journal of the Acoustical Society of America

105

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“…Although it could be argued that fundamental frequency should have been held constant while only acoustic properties that are associated with timbre varied (e.g., spectral and amplitude envelope), these properties covary naturally with frequency across an instrument's range. Indeed, holding them constant can result in a change in timbre (Charbonneau, 1981; see also Grey & Moorer, 1977). I expected the frequency difference to have a negligible effect on the outcome, given that past work (Cutting et aI., 1976) has obtained adaptation under frequency variation.…”

Section: Stimulimentioning

confidence: 99%

Evidence for a central representation of instrument timbre

Pitt

1995

Perception & Psychophysics

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An information processing account of perception seeks to delineate the stages of processing through which a stimulus passes and determine the properties of the representation at each stage. Research in phonetic perception has identified two stages, the second of which is thought to encode abstract acoustic attributes of sounds. The present study provided a further test of this proposal by assessing whether nonphonetic stimuli could yield results similar to those obtained with phonetic stimuli. Five selective adaptation experiments were carried out with a trumpet-piano timbre continuum. Twomanipulations were used to measure abstract encoding: cross-ear presentation of adaptor and test series, and the use of adaptors that were acoustically different from the continuum endpoints. The results provide evidence for an abstract representation of timbre. The similarity of the findings to those in the phonetic adaptation literature is discussed.A fundamental problem in perception is understanding how perceivers maintain object constancy across wide variation in the physical properties of a stimulus. In the auditory domain, this problem may be best known in the form of the acoustic-phonetic variability problem in speech perception, in which there is a lack of one-to-one correspondence between the acoustic properties of the speech signal and the resulting phonetic percept. For example, not only can the same acoustic cue signal different phonemes in different contexts, but different cues can signal the same phoneme in different contexts (Liberman, Cooper, Shankweiler, & Studdert-Kennedy, 1967).The question of how listeners perceive object constancy also arises when investigating the perception of nonphonetic sound objects, such as the timbre of musical instruments (Handel, 1989). Tones produced by a saxophone or clarinet are readily perceived as such over most of the instrument's range, yet the acoustic properties of the tones change markedly across this range. Pressing and releasing keys alters not only the fundamental frequency of the tone but also the resonance (filtering) characteristics of the instrument. This variation is particularly large when the register (octave) key is used. Additional variability in the waveform is introduced by changes in the source of production (e.g., vibration amplitude of the reed, tone attack rate; Backus, 1977;Benade, 1976).As with phonetic percepts, the acoustic cues that signal instrument identity vary across contexts, and no one cue has been found that is necessary and sufficient for I thank Gary Starr for testing subjects and Alice Cimino for invaluable assistance in performing the timbral interpolation. A conversation with Arthur Samuel led to the design of Experiment 5.

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“…Just as researchers have long-sought to chart the multidimensional timbre space of instruments [55][56][57][58][59][60][61][62][63][64][65][66][67][68][69][70][71], it would be fascinating to chart the space of emotional characteristics for different types of music and instruments. What are the shapes of these characteristics, how do they overlap, and what are their symmetries?…”

Section: Implications For the 4-quadrant Model And Future Workmentioning

confidence: 99%

An Analysis of Low-Arousal Piano Music Ratings to Uncover What Makes Calm and Sad Music So Difficult to Distinguish in Music Emotion Recognition

Yu¹,

Chau²,

Horner³

2017

J. Audio Eng. Soc.

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Music emotion recognition and recommendation systems often use a simplified 4-quadrant model with categories such as Happy, Sad, Angry, and Calm. Previous research has shown that both listeners and automated systems often have difficulty distinguishing low-arousal categories such as Calm and Sad. This paper seeks to explore what makes the categories Calm and Sad so difficult to distinguish. We used 300 low-arousal excerpts from the classical piano repertoire to determine the coverage of the categories Calm and Sad in the low-arousal space, their overlap, and their balance to one another. Our results show that Calm was 40% bigger in terms of coverage than Sad, but that on average Sad excerpts were significantly more negative in mood than Calm excerpts were positive. Calm and Sad overlapped in nearly 20% of the excerpts, meaning 20% of the excerpts were about equally Calm and Sad. Calm and Sad covered about 92% of the low-arousal space, where 8% of the space were holes that were not-at-all Calm or Sad. The largest holes were for excerpts considered Mysterious and Doubtful, but there were smaller holes among positive excerpts as well. Due to the holes in the coverage, the overlaps, and imbalances the Calm-Sad model adds about 6% more errors when compared to asking users directly whether the mood of the music is positive or negative. Nevertheless, the Calm-Sad model is still useful and appropriate for applications in music emotion recognition and recommendation such as when a simple and intuitive interface is preferred or when categorization is more important than precise differentiation.

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Timbre and the Perceptual Effects of Three Types of Data Reduction

Cited by 21 publications

References 0 publications

Musical instrument identification: A pattern-recognition approach

Musical instrument identification: A pattern-recognition approach

Evidence for a central representation of instrument timbre

An Analysis of Low-Arousal Piano Music Ratings to Uncover What Makes Calm and Sad Music So Difficult to Distinguish in Music Emotion Recognition

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