Difference between revisions of "Acoustic Analysis"
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'''Burst detection (burst.py)''' |
'''Burst detection (burst.py)''' |
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| − | This script |
+ | This script defines a function burst() |
| − | + | -- input 1) the name of a soundfile, 2) start time (in seconds), and (3) end time of an analysis window where we will look for a burst. |
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| − | + | -- output 1) a burst_time (in seconds), and 2) a burst_score which is a measure of how much like a burst the burst is. |
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| + | The script does the following things: |
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| − | In the soundfile waveform: |
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| + | '''With the Audio Waveform:''' |
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| − | 5) Gives each of these peaks a time-stamped score based on amount of change in waveform relative to neighboring samples |
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| − | In the spectragram: |
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| + | '''Compute a series of Mel Frequency Spectra:''' |
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| − | 6) Now, takes a Mel frequency spectrum with freq above 300 Hz, in 5 ms windows. |
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| + | 4) Now, take Mel frequency spectra from 300Hz to 8000Hz, in non-overlapping 5 ms windows, spanning the interval from start to end times. This is done with the ESPS routine melspec() |
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| − | 7) Compares this window to the next 5 ms spectral window, and selects top three candidates with most change in the spectrum. |
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| + | 5) Compare successive windows, and select the top three candidates with most change in the spectrum. This is done with the ESPS routine diff(). This is the ''spectral_difference''. |
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| + | '''Combine these two acoustic landmarks into a burst score''' |
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| − | 10) Selects candidate with highest burst score. |
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| + | ''b'' - the burst score is calcuated: ''b = -1.814 + 0.618*log(waveform_difference) + 0.003*spectral_difference'' |
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| + | 8) Select the candidate with highest burst score, and report the score and the time location of the burst (based on the waveform peak, which is more accurate. |
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Latest revision as of 11:26, 26 April 2018
Burst detection (burst.py)
This script defines a function burst()
-- input 1) the name of a soundfile, 2) start time (in seconds), and (3) end time of an analysis window where we will look for a burst.
-- output 1) a burst_time (in seconds), and 2) a burst_score which is a measure of how much like a burst the burst is.
The script does the following things:
1) Resample sound file to 16,000 Hz.
With the Audio Waveform:
2) Within the window specified by the start and end times, look at each sample and determines whether it is a peak or valley.
3) Find three biggest valleys in the waveform (corresponding to pressure peaks) within specified time window. The biggest valleys are those that have the largest difference at the valley relative to the adjacent samples. This is the waveform_difference.
Compute a series of Mel Frequency Spectra:
4) Now, take Mel frequency spectra from 300Hz to 8000Hz, in non-overlapping 5 ms windows, spanning the interval from start to end times. This is done with the ESPS routine melspec()
5) Compare successive windows, and select the top three candidates with most change in the spectrum. This is done with the ESPS routine diff(). This is the spectral_difference.
Combine these two acoustic landmarks into a burst score
6) Compare waveform candidates to spectrum candidates, and keep those where the times align.
7) Calculate a burst score (burst strength) for the remaining candidates. This is done using a linear discriminant function trained on bursts in TIMIT.
b - the burst score is calcuated: b = -1.814 + 0.618*log(waveform_difference) + 0.003*spectral_difference
8) Select the candidate with highest burst score, and report the score and the time location of the burst (based on the waveform peak, which is more accurate.
9) If no burst is detected, return a burst score of 0 and a burst_time of -1.
