Consider also "lower performing students," "low scoring students," and "low graded. It's incredibly impressive.Alternate terms for "weak student" are "underachiever" (consider this Ngram), and "underachieving/low achieving student."For your example, consider "lower achieving students." The heuristic model gives higher achieving students an opportunity to find possible solutions, whereas lower achieving ones are. Looking for a particular melody or rhythm? Just hum, sing, or beatbox it in, and Sononym will find similar sounds in a matter of moments. Perhaps most impressively, you can record any audio in real time via Sononym’s simple Record button in the transport – including your own voice, for example – as a source to drive your search. Care more about Timbre than Pitch? Adjust the attribute sliders accordingly to skew search results in that direction.
Using the Similarity sliders, you can then calibrate your search results by weighting to any one of those five traits and see at a quick glance which qualities any similar sample bears most in common via their own preview wheels at left. Making the process even more intuitive, Sononym’s Similarity search maps the audible traits of all analyzed files across five primary aspects: Spectrum, Timbre, Pitch, Amplitude and Overall, resulting in a similarity ranking that corresponds to your source. The engine quickly presents sounds with similar characteristics, and you can further hone results by selecting instrument Category and other filtration attributes including Loudness, Frequency, Tempo and more in the lower section of the screen. While you can of course conduct a search by typing vague terms into a familiar search bar if you insist, Sononym’s power lies in seeking out sounds similar to any audio file on your computer, even in an open DAW project, by simply dragging and dropping it directly into Sononym’s GUI. Sononym’s custom Collections mode at left. Files remain in their original directories. Self-organizing category mapping impressively separates snares from kicks, leads from pads, and vocals from guitars, for example, grouping them accordingly and intelligibly within a comprehensive metadata matrix. Once analysis is complete, Sononym automatically identifies whether sounds are Loops or One Shots, while also extracting Pitch, Loudness, Tempo, Brightness, Harmonicity and Noisiness ratings.
#SONONYM SAMPLES PRO#
It took about 25 minutes to analyze 84 GB of samples on a 2.8 GHz Intel Core i7 Macbook Pro – pretty fast, though you might want to pop out for lunch while it works if you’re on an older machine or have a massive library.
#SONONYM SAMPLES HOW TO#
So, how to reinvent this particularly clunky yet undeniably crucial wheel? Berlin’s Sononym may have finally cracked the code by cleverly deploying offline machine learning algorithms to analyze sample libraries and search them across myriad qualitative dimensions. As the old saying goes, “Talking about music is like dancing about architecture.” Lost In Libraries
The words we use to describe the sounds we seek (and create) are hardly standardized, in most cases woefully inadequate, and often just plain inaccurate. Live 10’s recent Browser improvements still rely on users doing their homework to organize Collections, and it still doesn’t implement any kind of meaningful metadata: the search tool only finds relevant files if the search term was written directly into the file name haphazard criteria at best.Įven metadata itself is only sometimes helpful: one sound designer’s “Dreamy, Analog, Pad” might seem to a sound-seeking producer more like “Euphoric, Vintage, Strings”.
While synthesis, sampling and editing techniques have steadily advanced in recent years, file organization has remained a comparatively neglected aspect of most DAWs. The lowly yet essential file browser: you know you can’t live without it, but living with it isn’t exactly an exciting proposition either.
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