Choosic, which front-paged Product Hunt this morning, is a music-discovery iPhone app with a tinder-style interface. People are quickly able to decide whether or not they will like a song just as easily as they make sweeping judgements about others on Tinder. The speed with which this can happen could be harnessed by DJs, who constantly need to search for new music to inspire fresh new mixes. I’ve got some thoughts on how Choosic might accelerate that process even further than they already have.

1. Provide Better Track Navigation

Programs like the DJ performance tool, Traktor, give you a visual representation of the song as a waveform. When preparing their sets, DJs will listen to each track, skipping through and setting cue points for important moments in the song: intro, verse, chorus, drop, outro. This is useful in a performance context, where they might want to quickly jump to the right point to mix in.

One of Traktor’s Song Decks (note the cue points on the zoomed-out bottom waveform).

Navigating the song is also important for deciding whether or not you like it. Due to the repetitive nature of most music, people can usually determine if they like a song fairly quickly - let’s say in about 30 seconds. Those are not necessarily the first 30 seconds, however. The verse likely sounds pretty different from the chorus. Maybe the drop is amazing and totally makes up for a lacklustre intro. Perhaps the DJ is looking for songs with long mix-out periods to build off of. These characteristics are easily determined by nagivating to relevant moments in the track.

Choosic’s track navigation mechanism is the same as Hype Machine’s iOS offering — a simple circle normalized to the track’s length. It doesn’t provide any information other than how far along playback has gotten. Soundcloud’s iOS interface provides a waveform view, which is more helpful, and something Choosic could implement. It’s a bit difficult to use because of its size — in SoundCloud’s case, which is portrait-only, the screen cuts the waveform off pretty quickly, and you have to scroll the screen to see more. An optional landscape view could help with this. A second, zoomed-out view à la Traktor could also help give a sense of the track (although that might be a bit much on a mobile screen). Another interesting possibility would be for them to algorithmically determine those points of interest in the song, and suggest them as pre-built cue points to the user.

2. Export Playlist to a More Actionable Format

At the end of the day, a DJ needs the song on his/her hard drive, so that they can load it in and play it. To really push the speed boundary on how quickly a performance-ready library could be built, automatic downloading is key. In a chiller world, a desktop companion app to Choosic would be able to simply queue up the downloads. That’s clearly only legally possible for the minority of songs, but a lesser form of this concept could still be of use. An option to email yourself a list of relevant links (SoundCloud, BeatPort, etc.) for your songs, which could be easily accessed from your performance machine, would go a long way.

3. Move Past Genres: Provide Deeper Search Options

I don’t personally like searching for music on the internet using genres. The limitation to genres is making Choosic feel a little bit like a glorified SoundCloud Explore, which I rarely use because it doesn’t tend to yield the music I want. I’d like to see Choosic take a page out of the follow-bot book, and offer content filtering based on more modern criteria: users, hashtags, number of plays/likes, has it front-paged hype machine?, etc. Take Instagress’ plethora of options, for example:

Instagress.com’s content filtering settings.

Genre is but one piece of the puzzle. Content is flooding the internet at an unprecedented rate — even very specific searches will yield countless results. The machine-learning algorithm behind Choosic must help to some extent, but it would be rad if you could aim your search with greater precision from the start.