• views: 114
• date: 28 March 2026
• posted by: voska89

During the research phase of Addictive Trigger, users around the world reported that accurate hit detection is absolutely critical when using drum replacement plugins. Unfortunately, existing triggering technologies tend to have shortcomings that require lots of manual tweaking and correction.
We endeavor to make products that are better than what is currently available, and we ended up creating an entirely new method for drum hit detection called Audio Fingerprint. Our new system uses FFT analysis to accurately identify drum sounds even if there is a lot of mic bleed or background noise in the source material. It's fast, it's accurate, and we think it'll change the way you work with live-recorded drums.
What is Audio Fingerprint?
Typical drum replacement tools rely on rudimentary filtering and transient detection, resulting in many false triggers and missed hits. Audio Fingerprint solves this problem by using a novel FFT-based matching system. It is so accurate, it usually has no trouble detecting lightly played ghost notes from background noise while simultaneously rejecting mic bleed and other unwanted sounds.
In addition to detecting low level ghost notes with great accuracy, our Audio Fingerprint system can also differentiate between different stroke types that are played at similar velocities (for example, sidestick and open snare hits). This lets Addictive Trigger play back the correct articulations of samples, even if the incoming source material has different kinds of drum hits with similar peak levels.
Home Page-

https://www.xlnaudio.com/products/addictive_trigger

   
   
   

Forex Trading

Crypto Trading. 20 USD Bonus after KYC

We need your support!
Make a donation to help us stay online
        
Bitcoin (BTC)
bc1q08g9d22cxkawsjlf8etuek2pc9n2a3hs4cdrld
	
Bitcoin Cash (BCH)
qqvwexzhvgauxq2apgc4j0ewvcak6hh6lsnzmvtkem

Ethereum (ETH)
0xb55513D2c91A6e3c497621644ec99e206CDaf239

Litecoin (LTC)
ltc1qt6g2trfv9tjs4qj68sqc4uf0ukvc9jpnsyt59u

USDT (ERC20)
0xb55513D2c91A6e3c497621644ec99e206CDaf239

USDT (TRC20)
TYdPNrz7v1P9riWBWZ317oBgJueheGjATm

0 1 2 3 4 5

| Print version |

	Information
		Users of GUESTS are not allowed to comment this publication.