Traktomizer

Batch audio analyses for private and large scale digital audio libraries became available in August 2018.

First released in mid 2015 after some 18 months of modelling and coding, Traktomizer has become feature rich, highly stable and is now a mature product.

I learned the way recommendation music platform algorithms listen (classify music) can be expressed as a function such that comparative audio analysis of a song with the audio properties of a pool of top recommended songs in the same and or neighbouring genres tallies into predictable standard ranges of deviations from the calibrations of those algorithms.

I proved that benchmark-analysis applied during the premastering stage can yield a viable, ranked list of weighted suggestions, which when properly implemented predisposes the work for maximum automatic recommendations and automatic plays. Conversely, not implementing song optimisations may have the affect of rendering it invisible to recommendation-bots for life.

Traktomizer’s significance as a reliable pre-release audio litmus test comes from its ability to precisely report every important deviation and individually rank and suggest the crucial audio tweaks requiring the least amount of adjustment for the greatest overall gain.

Once elective optimisation actions have been taken, increased playlist inclusion, increased recommendations, increased plays, wider audience reach and maximised song longevity, are logical and reasonable expectations.