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The onset structure of the original rhythm (left column) was preserved. Tone duration (middle column) and onset/offset ramp duration (right column) were parametrically varied. After obtaining the amplitude envelopes (middle row) of the stimulus waveforms (top row) via a <t>Hilbert</t> transform, the envelopes were transformed to the stimulus spectra in the frequency domain using a FFT (bottom row). Arrows mark the beat-related frequencies 0.416 Hz (1:12), 1.25 Hz (1:4), 2.5 Hz (1:2), and 5 Hz (1:1).
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The onset structure of the original rhythm (left column) was preserved. Tone duration (middle column) and onset/offset ramp duration (right column) were parametrically varied. After obtaining the amplitude envelopes (middle row) of the stimulus waveforms (top row) via a <t>Hilbert</t> transform, the envelopes were transformed to the stimulus spectra in the frequency domain using a FFT (bottom row). Arrows mark the beat-related frequencies 0.416 Hz (1:12), 1.25 Hz (1:4), 2.5 Hz (1:2), and 5 Hz (1:1).
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The onset structure of the original rhythm (left column) was preserved. Tone duration (middle column) and onset/offset ramp duration (right column) were parametrically varied. After obtaining the amplitude envelopes (middle row) of the stimulus waveforms (top row) via a <t>Hilbert</t> transform, the envelopes were transformed to the stimulus spectra in the frequency domain using a FFT (bottom row). Arrows mark the beat-related frequencies 0.416 Hz (1:12), 1.25 Hz (1:4), 2.5 Hz (1:2), and 5 Hz (1:1).
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The onset structure of the original rhythm (left column) was preserved. Tone duration (middle column) and onset/offset ramp duration (right column) were parametrically varied. After obtaining the amplitude envelopes (middle row) of the stimulus waveforms (top row) via a <t>Hilbert</t> transform, the envelopes were transformed to the stimulus spectra in the frequency domain using a FFT (bottom row). Arrows mark the beat-related frequencies 0.416 Hz (1:12), 1.25 Hz (1:4), 2.5 Hz (1:2), and 5 Hz (1:1).
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The onset structure of the original rhythm (left column) was preserved. Tone duration (middle column) and onset/offset ramp duration (right column) were parametrically varied. After obtaining the amplitude envelopes (middle row) of the stimulus waveforms (top row) via a <t>Hilbert</t> transform, the envelopes were transformed to the stimulus spectra in the frequency domain using a FFT (bottom row). Arrows mark the beat-related frequencies 0.416 Hz (1:12), 1.25 Hz (1:4), 2.5 Hz (1:2), and 5 Hz (1:1).
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The onset structure of the original rhythm (left column) was preserved. Tone duration (middle column) and onset/offset ramp duration (right column) were parametrically varied. After obtaining the amplitude envelopes (middle row) of the stimulus waveforms (top row) via a <t>Hilbert</t> transform, the envelopes were transformed to the stimulus spectra in the frequency domain using a FFT (bottom row). Arrows mark the beat-related frequencies 0.416 Hz (1:12), 1.25 Hz (1:4), 2.5 Hz (1:2), and 5 Hz (1:1).
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The onset structure of the original rhythm (left column) was preserved. Tone duration (middle column) and onset/offset ramp duration (right column) were parametrically varied. After obtaining the amplitude envelopes (middle row) of the stimulus waveforms (top row) via a <t>Hilbert</t> transform, the envelopes were transformed to the stimulus spectra in the frequency domain using a FFT (bottom row). Arrows mark the beat-related frequencies 0.416 Hz (1:12), 1.25 Hz (1:4), 2.5 Hz (1:2), and 5 Hz (1:1).
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The onset structure of the original rhythm (left column) was preserved. Tone duration (middle column) and onset/offset ramp duration (right column) were parametrically varied. After obtaining the amplitude envelopes (middle row) of the stimulus waveforms (top row) via a <t>Hilbert</t> transform, the envelopes were transformed to the stimulus spectra in the frequency domain using a FFT (bottom row). Arrows mark the beat-related frequencies 0.416 Hz (1:12), 1.25 Hz (1:4), 2.5 Hz (1:2), and 5 Hz (1:1).
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Verlag GmbH convex analysis and monotone operator theory in hilbert spaces
The onset structure of the original rhythm (left column) was preserved. Tone duration (middle column) and onset/offset ramp duration (right column) were parametrically varied. After obtaining the amplitude envelopes (middle row) of the stimulus waveforms (top row) via a <t>Hilbert</t> transform, the envelopes were transformed to the stimulus spectra in the frequency domain using a FFT (bottom row). Arrows mark the beat-related frequencies 0.416 Hz (1:12), 1.25 Hz (1:4), 2.5 Hz (1:2), and 5 Hz (1:1).
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Image Search Results


The onset structure of the original rhythm (left column) was preserved. Tone duration (middle column) and onset/offset ramp duration (right column) were parametrically varied. After obtaining the amplitude envelopes (middle row) of the stimulus waveforms (top row) via a Hilbert transform, the envelopes were transformed to the stimulus spectra in the frequency domain using a FFT (bottom row). Arrows mark the beat-related frequencies 0.416 Hz (1:12), 1.25 Hz (1:4), 2.5 Hz (1:2), and 5 Hz (1:1).

Journal: PLoS ONE

Article Title: What can we learn about beat perception by comparing brain signals and stimulus envelopes?

doi: 10.1371/journal.pone.0172454

Figure Lengend Snippet: The onset structure of the original rhythm (left column) was preserved. Tone duration (middle column) and onset/offset ramp duration (right column) were parametrically varied. After obtaining the amplitude envelopes (middle row) of the stimulus waveforms (top row) via a Hilbert transform, the envelopes were transformed to the stimulus spectra in the frequency domain using a FFT (bottom row). Arrows mark the beat-related frequencies 0.416 Hz (1:12), 1.25 Hz (1:4), 2.5 Hz (1:2), and 5 Hz (1:1).

Article Snippet: For this reason, the two techniques yielded different results in the frequency domain for identical stimuli , so we evaluated the consequences of comparing neural responses to stimulus envelopes calculated by the MIR toolbox versus the Matlab Hilbert function.

Techniques: Transformation Assay

Since the MIR toolbox makes use of time-domain filtering, envelopes are smooth and frequency spectra differ from those obtained from the Matlab Hilbert transform. The most obvious discrepancy is at 2.5 Hz, where there is no energy in the spectrum obtained using the Matlab Hilbert function.

Journal: PLoS ONE

Article Title: What can we learn about beat perception by comparing brain signals and stimulus envelopes?

doi: 10.1371/journal.pone.0172454

Figure Lengend Snippet: Since the MIR toolbox makes use of time-domain filtering, envelopes are smooth and frequency spectra differ from those obtained from the Matlab Hilbert transform. The most obvious discrepancy is at 2.5 Hz, where there is no energy in the spectrum obtained using the Matlab Hilbert function.

Article Snippet: For this reason, the two techniques yielded different results in the frequency domain for identical stimuli , so we evaluated the consequences of comparing neural responses to stimulus envelopes calculated by the MIR toolbox versus the Matlab Hilbert function.

Techniques: