Time‐Segment Processing

Dutilleux, Pierre; Poli, Giovanni De; Knesebeck, Adrian von dem; Zölzer, Udo

doi:10.1002/9781119991298.ch6

Cited by 4 publications

(5 citation statements)

References 38 publications

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“…The synthesis technology that ultimately was adopted for this project resembles granular synthesis (see [5]), in that a multitude of short sound sources formed an ensemble output (likened to a 'swarm') rather than forming clearly separable events that might be heard as distinct in time and space. In all sonifications designed for the current work in this way, there was always a hypothesis to be tested regarding which parameters of the data were 'mapped' to particular synthesis parameters.…”

Section: Sound Synthesis For the Sonificationsmentioning

confidence: 99%

“…It is beyond the scope of this paper to present the details of the synthesis technology that was developed and refined through experimentation with the available multidimensional proteomic data. Suffice it to say that swarms of percussive 'grains' (again, see [5]), synthesized with 'parameter-mapped' control over multiple timbral attributes, were distributed in time and space according to the distribution of proteomic data that featured 1815 variables observed over the nine cases to be examined.…”

Section: Sound Synthesis For the Sonificationsmentioning

confidence: 99%

See 1 more Smart Citation

Hypothesis-Driven Sonification of Proteomic Data Distributions Indicating Neurodegredation in Amyotrophic Lateral Sclerosis

Martens¹,

Poronnik²,

Saunders³

2016

Proceedings of the 22nd International Conference on Auditory Display - ICAD 2016

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Three alternative sonifications of proteomic data distributions were compared as a means to indicate the neuropathology associated with Amyotrophic Lateral Sclerosis (ALS) via auditory display (through exploration of the differentiation of induced pluripotent stem cell derived neurons). Pure visual displays of proteomic data often result in ”visual overload” such that detailed or subtle data important to describe ALS neurodegradation may be glossed over, and so three competing approaches to the sonification of proteomic data were designed to capitalize upon human auditory capacities that complement the visual capacities engaged by more conventional graphic representations. The auditory displays resulting from hypothesis-driven design of three alternative sonifications were evaluated by naïve listeners, who were instructed to listen for differences between the sonifications produce from proteomic data associated with three different types of cells. One of the sonifications was based upon the hypothesis that auditory sensitivity to regularities and irregularities in spatio-temporal patterns in the data could be heard through spatial distribution of sonification components. The design of a second sonification was based upon the hypothesis that variation in timbral components might create a distinguishable sound for each of three types of cells. A third sonification was based upon the hypothesis that redundant variation in both spatial and timbral components would be even more powerful as a means for identifying spatio-temporal patterns in the dynamic, multidimensional data generated in current proteomic studies of ALS.

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Section: Sound Synthesis For the Sonificationsmentioning

confidence: 99%

Section: Sound Synthesis For the Sonificationsmentioning

confidence: 99%

Hypothesis-Driven Sonification of Proteomic Data Distributions Indicating Neurodegredation in Amyotrophic Lateral Sclerosis

Martens¹,

Poronnik²,

Saunders³

2016

Proceedings of the 22nd International Conference on Auditory Display - ICAD 2016

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“…Time expansion is a well-known and widely adopted technique, for example, in digital signal audio processing [6][7][8][9][10][11][12][13][14] where both basic and advanced algorithms are available in order to change the playback speed of a recorded signal and, if needed, preserve the pitch. At times, the available hardware does not have enough computational power to put in place sophisticated algorithms or, more simply, if the output signal has to be in analog form, strictly speaking, fully digital processing may not be required.…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic Signal Time-Expansion Using DAC Frequency Modulation

Rossi¹,

Frasca²

2023

Vibration

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Ultrasonic signals can be conveniently recorded using modern high-speed analog-to-digital converters and analyzed through digital signal processing algorithms. Sometimes, in some applications, such as in bioacoustics, it is necessary to convert digital data to analog signals with a special transformation that allows compressing and translating the spectrum toward audible frequencies. The process is called time expansion and can be conveniently achieved by slowing down the frequency clock of a digital-to-analog converter. This paper analyzes in detail the spectral characteristics of a time-expanded signal.

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“…Time-scale modification (TSM) refers to a change in the duration or the playback speed of a sound that does not affect its spectral characteristics, such as pitch, timbre, and brightness [1,2,3]. If an audio signal is simply played at a different sample rate, the frequency content is deemed to be changed as the formants of the sound are moved.…”

Section: Introductionmentioning

confidence: 99%

“…TSM has been long used in speech, e.g. in audio books and language-learning services [4,5,6], music and remixing [7], broadcasting services [2], and streaming platforms [8]. The ratio between the modified and the original time support is controlled by the TSM factor α, defining time stretching for α > 1 and time compression for α < 1.…”

Section: Introductionmentioning

confidence: 99%

Extreme Audio Time Stretching Using Neural Synthesis

Fierro

Wright

Välimäki

et al. 2023

ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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A deep neural network solution for time-scale modification (TSM) focused on large stretching factors is proposed, targeting environmental sounds. Traditional TSM artifacts such as transient smearing, loss of presence, and phasiness are heavily accentuated and cause poor audio quality when the TSM factor is four or larger. The weakness of established TSM methods, often based on a phase vocoder structure, lies in the poor description and scaling of the transient and noise components, or nuances, of a sound. Our novel solution combines a sines-transients-noise decomposition with an independent WaveNet synthesizer to provide a better description of the noise component and an improve sound quality for large stretching factors. Results of a subjective listening test against four other TSM algorithms are reported, showing the proposed method to be often superior. The proposed method is stereo compatible and has a wide range of applications related to the slow motion of media content.

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Time‐Segment Processing

Cited by 4 publications

References 38 publications

Hypothesis-Driven Sonification of Proteomic Data Distributions Indicating Neurodegredation in Amyotrophic Lateral Sclerosis

Hypothesis-Driven Sonification of Proteomic Data Distributions Indicating Neurodegredation in Amyotrophic Lateral Sclerosis

Ultrasonic Signal Time-Expansion Using DAC Frequency Modulation

Extreme Audio Time Stretching Using Neural Synthesis

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