Sliding DFT - Revision history

TF3RDL: Renamed the last link on the External links section: Sorry about that, but foo_cqt_analyzer only belongs to Fanon Wiki

2023-09-10T01:33:15Z

Renamed the last link on the External links section: Sorry about that, but foo_cqt_analyzer only belongs to Fanon Wiki

← Older revision		Revision as of 01:33, 10 September 2023
Line 13:		Line 13:
	== External links ==		== External links ==
	* {{Wikipedia\|Sliding DFT}}		* {{Wikipedia\|Sliding DFT}}
	* [https://scratch.mit.edu/projects/858061428/ ~~A video explaining~~ how sliding DFT algorithm ~~(foo_cqt_analyzer)~~ works].		* [https://scratch.mit.edu/projects/858061428/ An interactive Scratch project on how sliding DFT algorithm with noninteger k support works].
	[[Category:Technical]]		[[Category:Technical]]
	[[Category:Signal Processing]]		[[Category:Signal Processing]]

TF3RDL at 09:21, 28 June 2023

2023-06-28T09:21:39Z

← Older revision		Revision as of 09:21, 28 June 2023
Line 13:		Line 13:
	== External links ==		== External links ==
	* {{Wikipedia\|Sliding DFT}}		* {{Wikipedia\|Sliding DFT}}
	* [https://scratch.mit.edu/projects/858061428~~/editor~~/ A video explaining how sliding DFT algorithm (foo_cqt_analyzer) works].		* [https://scratch.mit.edu/projects/858061428/ A video explaining how sliding DFT algorithm (foo_cqt_analyzer) works].
	[[Category:Technical]]		[[Category:Technical]]
	[[Category:Signal Processing]]		[[Category:Signal Processing]]

TF3RDL: /* External links */ More relevant link

2023-06-28T09:21:07Z

External links: More relevant link

← Older revision		Revision as of 09:21, 28 June 2023
Line 13:		Line 13:
	== External links ==		== External links ==
	* {{Wikipedia\|Sliding DFT}}		* {{Wikipedia\|Sliding DFT}}
	* [https://scratch.mit.edu/projects/~~184263583~~/editor/ A video explaining how sliding DFT works ~~and how to properly capture audio samples for sliding DFT calculation in a foobar2000 visualization component~~]		* [https://scratch.mit.edu/projects/858061428/editor/ A video explaining how sliding DFT algorithm (foo_cqt_analyzer) works].
	[[Category:Technical]]		[[Category:Technical]]
	[[Category:Signal Processing]]		[[Category:Signal Processing]]

TF3RDL: Looking at the source code of R128 meter for foobar2000, the get_chunk_absolute() seems to have full offset (all samples ahead actual playback) and there is an alternative way to do this properly

2023-06-19T02:55:05Z

Looking at the source code of R128 meter for foobar2000, the get_chunk_absolute() seems to have full offset (all samples ahead actual playback) and there is an alternative way to do this properly

← Older revision		Revision as of 02:55, 19 June 2023
Line 1:		Line 1:
	{{Stub}}		{{Stub}}
	[[Image:sDFT network.png\|thumb\|A block diagram of guaranteed-stable sDFT network with non-integer K.]]		[[Image:sDFT network.png\|thumb\|A block diagram of guaranteed-stable sDFT network with non-integer K compatibility.]]
	A '''sliding [[DFT]]''' is a recursive complex FIR filter bank that calculates short-time Fourier transform sample-by-sample. Unlike [[FFT]], the sDFT calculation for each bin is independent of each other, making it ideal for single-bin STFT and even [[constant-Q transform]] but this technique are limited by having only integer K otherwise, the sDFT goes out of phase but this is not in case on some sDFT implementations that allows non-integer K.<ref>The section "Sliding Spectrum Analysis for a Non-integer k Analysis Frequency" at "[https://digital.library.adelaide.edu.au/dspace/bitstream/2440/133370/3/hdl_133370.pdf Improvements to the Sliding DFT Algorithm]" by Richard Lyons and Carl Howard (July 2021)</ref>		A '''sliding [[DFT]]''' is a recursive complex FIR filter bank that calculates short-time Fourier transform sample-by-sample. Unlike [[FFT]], the sDFT calculation for each bin is independent of each other, making it ideal for single-bin STFT and even [[constant-Q transform]] but this technique are limited by having only integer K otherwise, the sDFT goes out of phase but this is not in case on some sDFT implementations that allows non-integer K.<ref>The section "Sliding Spectrum Analysis for a Non-integer k Analysis Frequency" at "[https://digital.library.adelaide.edu.au/dspace/bitstream/2440/133370/3/hdl_133370.pdf Improvements to the Sliding DFT Algorithm]" by Richard Lyons and Carl Howard (July 2021)</ref>

	The sliding DFT can also have asymmetric windowing function by replacing ~~ring buffers~~ with an ~~IIR~~ exponential decay and it can be cascaded to further increase rolloff.<ref>A paper "[https://par.nsf.gov/servlets/purl/10078055 The Sliding Windowed Infinite Fourier Transform]" on "Tips & Tricks" part of ''IEEE Signal Processing Magazine''</ref><ref>[https://gist.github.com/TF3RDL/0f20d771ad92841d8a30d448f1140e6e Implementation of the sliding windowed infinite Fourier transform with functionality to cascade sDFTs serially] at GitHub Gist</ref>		The sliding DFT can also have asymmetric windowing function by replacing comb filtering stage with an exponential decay and it can be cascaded to further increase rolloff.<ref>A paper "[https://par.nsf.gov/servlets/purl/10078055 The Sliding Windowed Infinite Fourier Transform]" on "Tips & Tricks" part of ''IEEE Signal Processing Magazine''</ref><ref>[https://gist.github.com/TF3RDL/0f20d771ad92841d8a30d448f1140e6e Implementation of the sliding windowed infinite Fourier transform with functionality to cascade sDFTs serially] at GitHub Gist</ref>

	== Implementation in foobar2000 ==		== Implementation in foobar2000 ==
	The sliding DFT, unlike FFT requires contiguous stream of data, which means implementing that requires tricks involved with delta variable (time difference between current and previous calculations). However, the offset part of audio capture part of visualization (especially with <code>get_chunk_absolute(data, offset, size)</code>) is kinda complicated; the time of the resulting captured signal is <code>requested_length/2</code> samples ahead of actual playback (as long as the requested length is lower than buffer size in samples), which means the offset should be adjusted to something like <code>time - delta/2</code>.		The sliding DFT, unlike FFT requires contiguous stream of data, which means implementing that requires tricks involved with delta variable (time difference between current and previous calculations). However, the offset part of audio capture part of visualization (especially with <code>get_chunk_absolute(data, offset, size)</code>) is kinda complicated; the time of the resulting captured signal is <code>requested_length</code> samples ahead of actual playback (as long as the requested length is lower than buffer size in samples), which means the offset should be adjusted to something like <code>time - delta</code>. Alternatively, you can use previous time as a variable and do <code>get_chunk_absolute(data, previous_time, time - previous_time)</code> this instead and it will have same effect.

	== References ==		== References ==

TF3RDL: Added image(s)

2023-06-15T21:03:35Z

Added image(s)

← Older revision		Revision as of 21:03, 15 June 2023
Line 1:		Line 1:
	{{Stub}}		{{Stub}}
			[[Image:sDFT network.png\|thumb\|A block diagram of guaranteed-stable sDFT network with non-integer K.]]
	A '''sliding [[DFT]]''' is a recursive complex FIR filter bank that calculates short-time Fourier transform sample-by-sample. Unlike [[FFT]], the sDFT calculation for each bin is independent of each other, making it ideal for single-bin STFT and even [[constant-Q transform]] but this technique are limited by having only integer K otherwise, the sDFT goes out of phase but this is not in case on some sDFT implementations that allows non-integer K.<ref>The section "Sliding Spectrum Analysis for a Non-integer k Analysis Frequency" at "[https://digital.library.adelaide.edu.au/dspace/bitstream/2440/133370/3/hdl_133370.pdf Improvements to the Sliding DFT Algorithm]" by Richard Lyons and Carl Howard (July 2021)</ref>		A '''sliding [[DFT]]''' is a recursive complex FIR filter bank that calculates short-time Fourier transform sample-by-sample. Unlike [[FFT]], the sDFT calculation for each bin is independent of each other, making it ideal for single-bin STFT and even [[constant-Q transform]] but this technique are limited by having only integer K otherwise, the sDFT goes out of phase but this is not in case on some sDFT implementations that allows non-integer K.<ref>The section "Sliding Spectrum Analysis for a Non-integer k Analysis Frequency" at "[https://digital.library.adelaide.edu.au/dspace/bitstream/2440/133370/3/hdl_133370.pdf Improvements to the Sliding DFT Algorithm]" by Richard Lyons and Carl Howard (July 2021)</ref>

TF3RDL at 21:56, 9 June 2023

2023-06-09T21:56:07Z

← Older revision		Revision as of 21:56, 9 June 2023
Line 1:		Line 1:
	{{Stub}}		{{Stub}}
	A '''sliding [[DFT]]''' is a recursive complex FIR filter bank that calculates short-time Fourier transform sample-by-sample. Unlike [[FFT]], the sDFT calculation for each bin is independent of each other, making it ideal for single-bin STFT and even [[constant-Q transform]] but this technique are limited by having only integer K otherwise, the sDFT goes out of phase but this is not in case on some sDFT implementations that allows non-integer K.<ref>The section "Sliding Spectrum Analysis for a Non-integer k Analysis Frequency" at "[Improvements to the Sliding DFT Algorithm]" by Richard Lyons and Carl Howard (July 2021)</ref>		A '''sliding [[DFT]]''' is a recursive complex FIR filter bank that calculates short-time Fourier transform sample-by-sample. Unlike [[FFT]], the sDFT calculation for each bin is independent of each other, making it ideal for single-bin STFT and even [[constant-Q transform]] but this technique are limited by having only integer K otherwise, the sDFT goes out of phase but this is not in case on some sDFT implementations that allows non-integer K.<ref>The section "Sliding Spectrum Analysis for a Non-integer k Analysis Frequency" at "[https://digital.library.adelaide.edu.au/dspace/bitstream/2440/133370/3/hdl_133370.pdf Improvements to the Sliding DFT Algorithm]" by Richard Lyons and Carl Howard (July 2021)</ref>

	The sliding DFT can also have asymmetric windowing function by replacing ring buffers with an IIR exponential decay and it can be cascaded to further increase rolloff.<ref>A paper "[https://par.nsf.gov/servlets/purl/10078055 The Sliding Windowed Infinite Fourier Transform]" on "Tips & Tricks" part of ''IEEE Signal Processing Magazine''</ref><ref>[https://gist.github.com/TF3RDL/0f20d771ad92841d8a30d448f1140e6e Implementation of the sliding windowed infinite Fourier transform with functionality to cascade sDFTs serially] at GitHub Gist</ref>		The sliding DFT can also have asymmetric windowing function by replacing ring buffers with an IIR exponential decay and it can be cascaded to further increase rolloff.<ref>A paper "[https://par.nsf.gov/servlets/purl/10078055 The Sliding Windowed Infinite Fourier Transform]" on "Tips & Tricks" part of ''IEEE Signal Processing Magazine''</ref><ref>[https://gist.github.com/TF3RDL/0f20d771ad92841d8a30d448f1140e6e Implementation of the sliding windowed infinite Fourier transform with functionality to cascade sDFTs serially] at GitHub Gist</ref>

TF3RDL: Added sources

2023-06-09T21:55:10Z

Added sources

← Older revision		Revision as of 21:55, 9 June 2023
Line 1:		Line 1:
	{{Stub}}		{{Stub}}
	A '''sliding [[DFT]]''' is a recursive complex FIR filter bank that calculates short-time Fourier transform sample-by-sample. Unlike [[FFT]], the sDFT calculation for each bin is independent of each other, making it ideal for single-bin STFT and even [[constant-Q transform]] but this technique are limited by having only integer K otherwise, the sDFT goes out of phase but this is not in case on some sDFT implementations that allows non-integer K.		A '''sliding [[DFT]]''' is a recursive complex FIR filter bank that calculates short-time Fourier transform sample-by-sample. Unlike [[FFT]], the sDFT calculation for each bin is independent of each other, making it ideal for single-bin STFT and even [[constant-Q transform]] but this technique are limited by having only integer K otherwise, the sDFT goes out of phase but this is not in case on some sDFT implementations that allows non-integer K.<ref>The section "Sliding Spectrum Analysis for a Non-integer k Analysis Frequency" at "[Improvements to the Sliding DFT Algorithm]" by Richard Lyons and Carl Howard (July 2021)</ref>

	The sliding DFT can also have asymmetric windowing function by replacing ring buffers with an IIR exponential decay and it can be cascaded to further increase rolloff.		The sliding DFT can also have asymmetric windowing function by replacing ring buffers with an IIR exponential decay and it can be cascaded to further increase rolloff.<ref>A paper "[https://par.nsf.gov/servlets/purl/10078055 The Sliding Windowed Infinite Fourier Transform]" on "Tips & Tricks" part of ''IEEE Signal Processing Magazine''</ref><ref>[https://gist.github.com/TF3RDL/0f20d771ad92841d8a30d448f1140e6e Implementation of the sliding windowed infinite Fourier transform with functionality to cascade sDFTs serially] at GitHub Gist</ref>

	== Implementation in foobar2000 ==		== Implementation in foobar2000 ==
	The sliding DFT, unlike FFT requires contiguous stream of data, which means implementing that requires tricks involved with delta variable (time difference between current and previous calculations). However, the offset part of audio capture part of visualization (especially with <code>get_chunk_absolute(data, offset, size)</code>) is kinda complicated; the time of the resulting captured signal is <code>requested_length/2</code> samples ahead of actual playback (as long as the requested length is lower than buffer size in samples), which means the offset should be adjusted to something like <code>time - delta/2</code>.		The sliding DFT, unlike FFT requires contiguous stream of data, which means implementing that requires tricks involved with delta variable (time difference between current and previous calculations). However, the offset part of audio capture part of visualization (especially with <code>get_chunk_absolute(data, offset, size)</code>) is kinda complicated; the time of the resulting captured signal is <code>requested_length/2</code> samples ahead of actual playback (as long as the requested length is lower than buffer size in samples), which means the offset should be adjusted to something like <code>time - delta/2</code>.

			== References ==
			<references/>

	== External links ==		== External links ==

TF3RDL at 04:28, 18 May 2023

2023-05-18T04:28:46Z

← Older revision		Revision as of 04:28, 18 May 2023
Line 9:		Line 9:
	== External links ==		== External links ==
	* {{Wikipedia\|Sliding DFT}}		* {{Wikipedia\|Sliding DFT}}
	* [https://scratch.mit.edu/projects/184263583/editor/ A video explaining how sliding DFT works and how to properly capture audio samples for sliding DFT calculation]		* [https://scratch.mit.edu/projects/184263583/editor/ A video explaining how sliding DFT works and how to properly capture audio samples for sliding DFT calculation in a foobar2000 visualization component]
	[[Category:Technical]]		[[Category:Technical]]
	[[Category:Signal Processing]]		[[Category:Signal Processing]]

TF3RDL: Added external links section

2023-05-18T04:27:19Z

Added external links section

← Older revision		Revision as of 04:27, 18 May 2023
Line 7:		Line 7:
	The sliding DFT, unlike FFT requires contiguous stream of data, which means implementing that requires tricks involved with delta variable (time difference between current and previous calculations). However, the offset part of audio capture part of visualization (especially with <code>get_chunk_absolute(data, offset, size)</code>) is kinda complicated; the time of the resulting captured signal is <code>requested_length/2</code> samples ahead of actual playback (as long as the requested length is lower than buffer size in samples), which means the offset should be adjusted to something like <code>time - delta/2</code>.		The sliding DFT, unlike FFT requires contiguous stream of data, which means implementing that requires tricks involved with delta variable (time difference between current and previous calculations). However, the offset part of audio capture part of visualization (especially with <code>get_chunk_absolute(data, offset, size)</code>) is kinda complicated; the time of the resulting captured signal is <code>requested_length/2</code> samples ahead of actual playback (as long as the requested length is lower than buffer size in samples), which means the offset should be adjusted to something like <code>time - delta/2</code>.

			== External links ==
			* {{Wikipedia\|Sliding DFT}}
			* [https://scratch.mit.edu/projects/184263583/editor/ A video explaining how sliding DFT works and how to properly capture audio samples for sliding DFT calculation]
	[[Category:Technical]]		[[Category:Technical]]
	[[Category:Signal Processing]]		[[Category:Signal Processing]]

TF3RDL: implementing the sliding DFT is more complicated than it seems right?

2023-05-01T03:56:42Z

implementing the sliding DFT is more complicated than it seems right?

← Older revision		Revision as of 03:56, 1 May 2023
Line 3:		Line 3:

	The sliding DFT can also have asymmetric windowing function by replacing ring buffers with an IIR exponential decay and it can be cascaded to further increase rolloff.		The sliding DFT can also have asymmetric windowing function by replacing ring buffers with an IIR exponential decay and it can be cascaded to further increase rolloff.

			== Implementation in foobar2000 ==
			The sliding DFT, unlike FFT requires contiguous stream of data, which means implementing that requires tricks involved with delta variable (time difference between current and previous calculations). However, the offset part of audio capture part of visualization (especially with <code>get_chunk_absolute(data, offset, size)</code>) is kinda complicated; the time of the resulting captured signal is <code>requested_length/2</code> samples ahead of actual playback (as long as the requested length is lower than buffer size in samples), which means the offset should be adjusted to something like <code>time - delta/2</code>.

	[[Category:Technical]]		[[Category:Technical]]
	[[Category:Signal Processing]]		[[Category:Signal Processing]]