I can believe adding overtones like this works, in the sense that listeners perceive more bass; music producers will use "saturation" to add overtones to (especially) bassy drum sounds and specifically mention that it helps on e.g. laptop speakers that lack bass response. (Search for [decap drums knock ableton] for a video suggesting that and other loudness-boosting things.)
The history of this effect in music production *also* sort of pours water on the idea that this was invented from whole cloth by this company. Maybe their special sauce is the real-time implementation of it on mobile tech, and other companies handy with a DSP could compete if it takes off?
Re: "I would love to have actually seen the FR difference in a measurement setup" I think objective measurements wouldn't have had anything good to say. Even if it also boosts the actual bass, there's still going to be only so much coming out of the speaker due to physical limitations; most of the claimed effect here is your ear (or really, brain) filling in the bass when it hears the overtones, which would be outside what traditional FR measurements pick up.
I wonder if this is done similar to how beam forming works by delaying certain frequencies by a small amount to give the effect that it from a different direction.
I've tinkered with this a bit in a programmable DSP and it was consuming the a ton of processing power. Granted, I strongly suspect that this due to how my implementation worked with multiple delays, but it was still shocking. A 16 frequency band, 5 direction steering solution was consuming ~70% of the DSP for a single channel and I probably could get this below 20% with some optimizations but that is a lot.
For a mobile software solution, I'd be curious to see what Dirac's impact that this would have on CPU overhead and battery life. This may not be as dire as I fear as Apple and LG have been using SoC's with a decent DSP block separate. If Dirac can leverage those, it'd be interesting to hear where this technology goes.
Frequency response analysis supposes that the system is linear, that is, it supposes that looking at the output of individual continuous tones of precise frequencies gives information about how a mix of frequencies of varying amplitudes sounds. As stated in the article, the processing here is nonlinear. So the effect of much of this processing will most likely not be captured by an FR analysis.
FR just gives you a measure of the response. Whether you model the transfer function as nonlinear or a linear approximation is down to your model, not the measurements.
Right, you can apply curves, but to what end? I think the best we can hope for is an A-B analysis that shows the difference in waveform outputs from the audio stack, and then compare the real output from a mobile device speaker. It won't capture the subjective quality, but it would be interesting to see exactly what the software does and how pronounced the effect is on real-world performance.
@edzieba You did note that I was talking about FR analysis, not measurement, right? Unless you want to measure data just to throw it away, you need to ask yourself what information you are looking for and if you can extract it from that data. Supposing in this case the goal is to characterize unknown non-linear behavior, my point was that knowledge of only the measured FR will not gain you much insight.
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edzieba - Wednesday, January 23, 2019 - link
I can't be the only one who skimmed that headline and misread dirac bass "donkey.bas"twotwotwo - Wednesday, January 23, 2019 - link
I can believe adding overtones like this works, in the sense that listeners perceive more bass; music producers will use "saturation" to add overtones to (especially) bassy drum sounds and specifically mention that it helps on e.g. laptop speakers that lack bass response. (Search for [decap drums knock ableton] for a video suggesting that and other loudness-boosting things.)The history of this effect in music production *also* sort of pours water on the idea that this was invented from whole cloth by this company. Maybe their special sauce is the real-time implementation of it on mobile tech, and other companies handy with a DSP could compete if it takes off?
Re: "I would love to have actually seen the FR difference in a measurement setup" I think objective measurements wouldn't have had anything good to say. Even if it also boosts the actual bass, there's still going to be only so much coming out of the speaker due to physical limitations; most of the claimed effect here is your ear (or really, brain) filling in the bass when it hears the overtones, which would be outside what traditional FR measurements pick up.
Kevin G - Wednesday, January 23, 2019 - link
I wonder if this is done similar to how beam forming works by delaying certain frequencies by a small amount to give the effect that it from a different direction.http://people.ece.cornell.edu/land/courses/ece4760...
I've tinkered with this a bit in a programmable DSP and it was consuming the a ton of processing power. Granted, I strongly suspect that this due to how my implementation worked with multiple delays, but it was still shocking. A 16 frequency band, 5 direction steering solution was consuming ~70% of the DSP for a single channel and I probably could get this below 20% with some optimizations but that is a lot.
For a mobile software solution, I'd be curious to see what Dirac's impact that this would have on CPU overhead and battery life. This may not be as dire as I fear as Apple and LG have been using SoC's with a decent DSP block separate. If Dirac can leverage those, it'd be interesting to hear where this technology goes.
JanW1 - Thursday, January 24, 2019 - link
Frequency response analysis supposes that the system is linear, that is, it supposes that looking at the output of individual continuous tones of precise frequencies gives information about how a mix of frequencies of varying amplitudes sounds. As stated in the article, the processing here is nonlinear. So the effect of much of this processing will most likely not be captured by an FR analysis.edzieba - Thursday, January 24, 2019 - link
FR just gives you a measure of the response. Whether you model the transfer function as nonlinear or a linear approximation is down to your model, not the measurements.BiggerInside - Thursday, January 24, 2019 - link
Right, you can apply curves, but to what end? I think the best we can hope for is an A-B analysis that shows the difference in waveform outputs from the audio stack, and then compare the real output from a mobile device speaker. It won't capture the subjective quality, but it would be interesting to see exactly what the software does and how pronounced the effect is on real-world performance.JanW1 - Friday, January 25, 2019 - link
@edzieba You did note that I was talking about FR analysis, not measurement, right? Unless you want to measure data just to throw it away, you need to ask yourself what information you are looking for and if you can extract it from that data. Supposing in this case the goal is to characterize unknown non-linear behavior, my point was that knowledge of only the measured FR will not gain you much insight.